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Data Analytics Student Capstone Projects, 2020-2021
Bitcoin sentiment analysis, addison mcfeely, jordan fujioka, myles lewis, and ning li.
In this paper, we try to analyze how the sentiments of the public, remarks of some celebrities, and news reports influence the bitcoin price. We build models based on the dataset of bitcoin price and text dataset from the source of Kaggle, Twitter, and OpenBlender. At last, we conclude that perdition models with sentiment analysis perform better than onefold price prediction. The bitcoin price is very hard to predict, and the predictive models are not so reliable to do the forecast for bitcoin price considering the accuracy rate of the models.
Does Happiness Matter?
The aim of this project was to build a deep learning model, in order to predict employee turnover based on employee’s self-reported happiness and satisfaction. The data comes from 34 companies in Barcelona that utilize HappyForce, an informative system that aims to collect data on employee satisfaction and daily happiness.
Energy in California: The Path to Renewable Energy
Jenna vogelsang, christa copenhaver, peichen geng and nabil syed.
California is currently taking an initiative to adopt clean renewable energy resources by 2035. Our project aims to examine the future of energy usage in the state of California and how different methods of electricity generation can impact the state in upcoming years. Our data was extracted from the U.S. Energy Information Administration and cleaned using Python. The researchers chose to do a time series analysis in hopes of predicting how energy usage will change as more renewable methods are adopted by 2035. The model chosen was the prophet model created by Facebook in 2017. This model was chosen because it accounts for seasonality and holiday effects which both have an impact on energy usage. The findings of our study indicate a steady upward trend of both electricity consumption and generation with the largest sources being natural gas and solar energy.
FOMO: Can Twitter Sentiment Predict Crypto Prices?
Rj copeland and robert spring.
The intent of our Capstone project is to observe the effect of Twitter sentiment on Crypto prices. The coins of interest are Bitcoin, Ethereum, and Litecoin. The data for sentiment analysis is pulled from Twitter. The Crypto data is comprised of static files showing minute based Crypto price changes relative to the coins listed above. The model used is a Long Short-Term Memory model (LSTM). Both the model and exploratory analysis are implemented in Python.
Forecasting Web Traffic
This project looks at the problem of forecasting future values of time-series data. Wikipedia has over 145,000 articles and their view counts available to analyze. Following the Exploratory Data Analysis (EDA) process I was able to develop an Auto-Regressive Integrated Moving Average (ARIMA) model that showed a prediction of view counts for the following 45 – 60 days. Web forecasting is gaining popularity and has many applications including load balancing for cloud services, and understanding user behavior.
Major League Soccer Analysis
With highly influential salary restrictions in Major League Soccer, organizations must navigate limited high-dollar signings and maximize effectiveness of lower cost players while translating their signings to positive team performance. My goal was to use historical player performances, salary data, and team results to find trends and understand the key metrics that result in successful team performances. This analysis can be leveraged by MLS organizations and scouts to sign the most effective players and increase chances of team success.
MLB Optimizing Outfield Alignments
This project serves to provide a program that uses variable Euclidean distance means model to create optimized outfield shifts for MLB teams. Utilizing variables such as hang time of the ball, outfielder’s reaction, jump, and speed. The result is a GUI developed in R that could be used both in a gameday setting and used by front offices to determine which players fit in to their defensive schemes better.
NFL Combine Research
Taking results from combine participants from several years and testing the data in a few different methods to find what kind of scores are needed to perform successfully in the NFL.
Last Updated March 24, 2022
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9 Project Ideas for Your Data Analytics Portfolio
Finding projects for your data analytics portfolio can be tricky, especially when you’re new to the field. You might also think that your data projects need to be especially complex or showy, but that’s not the case. The most important thing is to demonstrate your skills, ideally using a dataset that interests you. And the good news? Data is everywhere—you just need to know where to find it and what to do with it.
In this post, we’ll highlight the key elements that your data analytics portfolio should demonstrate. We’ll then share nine project ideas that will help you build your portfolio from scratch, focusing on three key areas: Data scraping , exploratory analysis , and data visualization .
- What should you include in your data analytics portfolio?
Data scraping project ideas
Exploratory data analysis project ideas, data visualization project ideas.
- What’s next?
Ready to get inspired? Let’s go!
1. What should you include in your data analytics portfolio?
Data analytics is all about finding insights that inform decision-making. But that’s just the end goal. As any experienced data analyst will tell you, the insights we see as consumers are the result of a great deal of work. In fact, about 80% of all data analytics tasks involve preparing data for analysis. This makes sense when you think about it—after all, our insights are only as good as the quality of our data.
Yes, your portfolio needs to show that you can carry out different types of data analysis . But it also needs to show that you can collect data, clean it, and report your findings in a clear, visual manner. As your skills improve, your portfolio will grow in complexity. As a beginner though, you’ll need to show that you can:
- Scrape the web for data
- Carry out exploratory analyses
- Clean untidy datasets
- Communicate your results using visualizations
If you’re inexperienced, it can help to present each item as a mini-project of its own. This makes life easier since you can learn the individual skills in a controlled way. With that in mind, we’ll keep it nice and simple with some basic ideas, and a few tools you might want to explore to help you along the way.
2. Data scraping project ideas for your portfolio
What is data scraping.
Data scraping is the first step in any data analytics project. It involves pulling data (usually from the web) and compiling it into a usable format. While there’s no shortage of great data repositories available online, scraping and cleaning data yourself is a great way to show off your skills.
The process of web scraping can be automated using tools like Parsehub , ScraperAPI , or Octoparse (for non-coders) or by using libraries like Beautiful Soup or Scrapy (for developers). Whichever tool you use, the important thing is to show that you understand how it works and can apply it effectively.
Before scraping a website, be sure that you have permission to do so. If you’re not certain, you can always search for a dataset on a repository site like Kaggle . If it exists there, it’s a good bet you can go straight to the source and scrape it yourself. Bear in mind though—data scraping can be challenging if you’re mining complex, dynamic websites. We recommend starting with something easy—a mostly-static site. Here are some ideas to get you started.
The Internet Movie Database
A good beginner’s project is to extract data from IMDb. You can collect details about popular TV shows, movie reviews and trivia, the heights and weights of various actors, and so on. Data on IMDb is stored in a consistent format across all its pages, making the task a lot easier. There’s also a lot of potential here for further analysis.
Many beginners like scraping data from job portals since they often contain standard data types. You can also find lots of online tutorials explaining how to proceed. To keep it interesting, why not focus on your local area? Collect job titles, companies, salaries, locations, required skills, and so on. This offers great potential for later visualization, such as graphing skillsets against salaries.
Another popular one is to scrape product and pricing data from e-commerce sites. For instance, extract product information about Bluetooth speakers on Amazon, or collect reviews and prices on various tablets and laptops. Once again, this is relatively straightforward to do, and it is scalable. This means you can start with a product that has a small number of reviews, and then upscale once you’re comfortable using the algorithms.
For something a bit less conventional, another option is to scrape a site like Reddit. You could search for particular keywords, upvotes, user data, and more. Reddit is a very static website, making the task nice and straightforward. Later, you can carry out interesting exploratory analyses, for instance, to see if there are any correlations between popular posts and particular keywords. Which brings us to our next section.
3. Exploratory data analysis project ideas
What is exploratory data analysis.
The next step in any data analyst’s skillset is the ability to carry out an exploratory data analysis (EDA). An EDA looks at the structure of data, allowing you to determine their patterns and characteristics. They also help you to clean your data. You can extract important variables, detect outliers and anomalies, and generally test your underlying assumptions.
While this process is one of the most time-consuming tasks for a data analyst, it can also be one of the most rewarding. Later modeling focuses on generating answers to specific questions. An EDA, meanwhile, helps you do one of the most exciting bits—generating those questions in the first place.
Languages like R and Python are often used to carry out these tasks. They have many pre-existing algorithms that you can use to carry out the work for you . The real skill lies in presenting your project and its results. How you decide to do this is up to you, but one popular method is to use an interactive documentation tool like Jupyter Notebook . This lets you capture elements of code, along with explanatory text and visualizations, all in one place. Here are some ideas for your portfolio.
Global suicide rates
This global suicide rates dataset covers suicide rates in various countries, with additional data including year, gender, age, population, GDP, and more. When carrying out your EDA, ask yourself: What patterns can you see? Are suicides rates climbing or falling in various countries? What variables (such as gender or age) can you find that might correlate to suicide rates?
World Happiness Report
On the other end of the scale, the World Happiness Report tracks six factors to measure happiness across the world’s citizens: life expectancy, economics, social support, absence of corruption, freedom, and generosity. So, which country is the happiest? Which continent? Which factor appears to have the greatest (or smallest) impact on a nation’s happiness? Overall, is happiness increasing or decreasing?
Aside from the two ideas above, you could also use your own datasets . After all, if you’ve already scraped your own data, why not use them? For instance, if you scraped a job portal, which locations or regions offer the best-paid jobs? Which offer the least well-paid ones? Why might that be? Equally, with e-commerce data, you could look at which prices and products offer the best value for money.
Ultimately, whichever dataset you’re using, it should grab your attention. If the data are too complex or don’t interest you, you’re likely to run out of steam before you get very far. Keep in mind what further probing you can do to spot interesting trends or patterns, and to extract the insights you need.
We’ve compiled a list of ten great places to find free datasets for your next project here .
4. Data visualization project ideas
What is data visualization.
Scraping, tidying, and analyzing data is one thing. Communicating your findings is another. Our brains don’t like looking at numbers and figures, but they love visuals. This is where the ability to create effective data visualizations comes in. Good visualizations—whether static or interactive—make a great addition to any data analytics portfolio. Showing that you can create visualizations that are both effective and visually appealing will go a long way towards impressing a potential employer.
Check out this video with Dr. Humera, where she explains how visualization helps tell a story with data.
Some free visualization tools include Google Charts , Canva Graph Maker (free), and Tableau Public . Meanwhile, if you want to show off your coding abilities, use a Python library such as Seaborn , or flex your R skills with Shiny . Needless to say, there are many tools available to help you. The one you choose depends on what you’re looking to achieve. Here’s a bit of inspiration…
Topical subject matter looks great on any portfolio, and the pandemic is nothing if not topical! What’s more, sites like Kaggle already have thousands of Covid-19 data sets available . How can you represent the data? Could you use a global heatmap to show where cases have spiked, versus where there are very few? Perhaps you could create two overlapping bar charts to show known infections versus predicted infections. Here’s a handy tutorial to help you visualize Covid-19 data using R, Shiny, and Plotly .
Most followed on Instagram
Whether you’re interested in social media, or celebrity and brand culture, this dataset of the most-followed people on Instagram has great potential for visualization. You could create an interactive bar chart that tracks changes in the most followed accounts over time. Or you could explore whether brand or celebrity accounts are more effective at influencer marketing. Otherwise, why not find another social media dataset to create a visualization? For instance, this map of the USA by data scientist Greg Rafferty nicely highlights the geographical source of trending topics on Instagram.
Another topic that lends itself well to visualization is transport data. Here’s a great project by Chen Chen on github , using Python to visualize the top tourist destinations worldwide, and the correlation between inbound/outbound tourists with gross domestic product (GDP).
5. What’s next?
In this post, we’ve explored which skills every beginner needs to demonstrate in their data analytics portfolio. Regardless of the dataset you’re using, you should be able to demonstrate the following abilities:
- Web scraping —using tools like Parsehub, Beautiful Soup, or Scrapy to extract data from websites (remember: static ones are easier!)
- Exploratory data analysis and data cleaning —manipulating data with tools like R and Python, before drawing some initial insights.
- Data visualization —utilizing tools like Tableau, Shiny, or Plotly to create crisp, compelling dashboards, and visualizations.
Once you’ve mastered the basics, you can start getting more ambitious with your data analytics projects. For example, why not introduce some machine learning projects, like sentiment analysis or predictive analysis? The key thing is to start simple and to remember that a good data analytics portfolio needn’t be flashy, just competent.
To further develop your skills, there are loads of online courses designed to set you on the right track. To start with, why not try our free, five-day data analytics short course ?
And, if you’d like to learn more about becoming a data analyst and building your portfolio, check out the following:
- How to build a data analytics portfolio
- The best data analytics certification programs on the market right now
- These are the most common data analytics interview questions
Oct 5, 2018
Data science capstone ideas (and how to get started)
Capstones are standalone projects meant to integrate, synthesize, and demonstrate all your data science knowledge in a multi-faceted way. Capstone projects show your readiness for using data science in real life, and are ideally something you can add to your resume, show to employers, or even use to start a career.
I find data science capstone ideas are like puppies: you want all of them, but can only keep one. Below is a list of some of my ideas and starting points.
Idea #1: Nutritional analysis from Instacart orders
In 2017 Instacart released a dataset of over 3 million grocery orders from over 200,000 users as a Kaggle competition . With a dataset this juicy, immediately a few ideas come time to mind:
- Predict what products users will order again (this was the goal of the Kaggle challenge).
- Build a model to stock the store so there are never any product shortages, but no wasted space or money in ordering.
- Predict a user’s healthiness from order content.
- Make a recommender system for healthier order alternatives.
The first and second are doable with the data you already have, which is nice.
The third was my personal choice, using the USDA food composition database to look up products and create a nutritional breakdown (by the way, they have an API ). But it also introduced a lot of hurdles:
- Users don’t eat everything they order (e.g. cat food, soap, toilet paper). This would require a lot of cleaning and munging.
- Users don’t order just for themselves (e.g. companies, birthday parties, families).
- Users order on different timelines (e.g. once per week, once every two weeks, once a month).
- Items such as deli food may not have entries in the USDA database.
The fourth would also utilize the USDA database, but would not require any user-specific information or messing about with time-series.
I dea #2: Predicting solar output from satellite imaging/historical weather
One of the big issues with mainstream adoption of solar power is unlike other energy sources (hydroelectric, oil, nuclear), you can’t control how long the sun shines for. Overestimating this amount means losses for producers and investors, and downtime for users. Underestimating means a lower chance of adoption in upfront decision-making. Sounds like a job for… machine learning!
Many datasets can be found at NREL , however they are in different years and different locations with limits on how much you can download at once. They have an API , which is useful.
SolarAnywhere has an academic license, allowing you to look up any location (but only for the year 2013). They too have an API .
Also, the NREL NSRDB data viewer .
There are three immediate approaches I can think of:
- Using previous solar output to predict current solar output (time-series or RNN).
- Using weather datasets
- Using satellite imaging datasets
There are a lot of academic papers on this last subject ( a quick Google Scholar search returns about 30,000 results ), but not a lot of publicly available satellite time-series datasets.
Idea #3: Fake news detection
This is a hot one. Without going into full rant-mode, fake news is obviously deleterious for democracy and individual mental stability.
So how to accurately identify what’s fake and what’s true? Here are a few leads on this as a data science problem:
1. Fake News Challenge
This is the best-formatted challenge around this topic, with organizers, advisors, and volunteers from the academic, ML, and fact-checking communities. Includes GitHub repos of winning submissions. Check out the competition page on Codalab.
2. Snopes Junk News
A starting point for well-verified fake news stories vs. actual events.
3. Getting Real About Fake News — Kaggle Dataset
A collection of nearly 13,000 items from 244 websites tagged “BS” from the BS Detector chrome extension. The BS Detector is powered by Open Sources , a project that classifies biased and fake websites.
Where To Get More Ideas
Never stop searching! Here are some ways to get more leads, either in the form of project ideas or datasets to use.
1. Academic papers
2. Kaggle Competitions
3. Kaggle Datasets
5. Awesome Public Datasets GitHub Repo
6. Google Datasets
Anything I can write about to help you find success in data science or trading? Tell me about it here: https://bit.ly/3mStNJG
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Python, trading, data viz. Get access to samchaaa++ for ready-to-implement algorithms and quantitative studies: https://samchaaa.substack.com/
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- Capstone Project Ideas That Will Get You That Sweet "A" Grade
Capstone Project Ideas That Will Get You That Sweet "A" Grade
Table of contents
What is a capstone project ? We have the whole blog dedicated to this question. Now, let's speak about worthy capstone project ideas as it is quite difficult to find them. You should use your research advisor’s help. Expert advice will help on the topic that will demonstrate what subject can be great for students’ proper training. You can overcome challenges and achieve required results by studying the topic thoroughly and understanding its essence. Use special academic articles, if you need help drawing a final line under the learning process. By choosing the main topic, you can reveal your skills and talents to the academic community, so no way should you neglect the preparation. You can get a good grade and demonstrate your best qualities by writing a single paper.
How to Come Up With Capstone Project Ideas
It takes time to look for the right capstone research project ideas. More so, than preparing the final paper. The fact is that it will be impossible to create an interesting project without having certain creative skills. Lacking ideas or insufficient work on covering your point will result in failure. Preparation for writing your capstone project includes this stages:
- Study the existing topics that have been covered in other papers.
- Contact your research advisor for help.
- Search for interesting topics on the Internet.
You can study only if you focus on the relevant topic. The lacking interest is quite noticeable in the paper, which is unacceptable. By brainstorming before writing the project, you should discover your advantages and demonstrate them properly. If you want instant results, then check out our capstone project writing service .
Medical Capstone Project Ideas
The best capstone project medical ideas are developed following a sample, with a suitable direction being easy to find. Delivering high-quality performance of the paper is important. After all, your work result depends on it. Every interested graduate can find a worthy topic on the Internet. Search for topics will be effective if you focus on some limited options.
Capstone Project Ideas for Nursing
The activity field plays a decisive role, so nursing capstone project ideas are worth paying attention to. Use different nursing essay examples for your writing. Keep in mind that you can count on your advisor’s help when preparing the paper. You should make a difficult choice from the following suggested options:
- Emergency care: existing practices.
- Reasons for nurse shortage.
- Peculiarities of working in medical institutions.
- Focusing on the patients’ needs.
- Pain management: effective practice.
- The best instructions for nurses.
- Value of working in medical institutions.
- Personal approach to visitors.
- Required knowledge before you start the job.
- Professional burnout: The reasons.
Don’t be afraid to look for a variety of topics! Restrictions will prevent you from making the right choice. Make sure that the chosen topic corresponds to your worldview. You should do everything possible so you can express your ideas in a comprehensible way.
Capstone Project Ideas for Healthcare Administration
Personal interest helps in covering the discussed healthcare administration capstone project ideas. You rarely get an opportunity to highlight an acute and exciting issue. You should take advantage! Take a look at these topics:
- Staff shortage: Solving the situation.
- Preparing staff for work: Where you should start.
- Stress resistance and its importance for work.
- Communication with patients as an integral responsibility.
- Professional skills’ application.
- Process management's challenges.
- Professional deformation: The signs.
- Mandatory management skills.
- Mind flexibility for finding compromises.
- Insufficient preparation for a surgery and its consequences.
Solving diverse tasks accounts for most of the administrators’ work. Can you get on well with patients and employees? Great! It will make it possible to achieve the set goals on the cheap. Professionalism without proper experience is not that important, after all.
Pharmacy Capstone Project Ideas
The search for capstone project ideas on pharmacy implies developing common issues from the field. Pharmacists are medical employees who communicate with patients more often than others. The friendly attitude and opportunity to help a visitor make them work tirelessly. The choice of the article direction can influence your working attitude in the future. Interesting ideas are as follows:
- Antibiotics efficiency.
- Traditional medicine: the importance.
- Taking medications: consequences.
- Immunosuppressants and how they affect health.
- Probiotics to combat the antibiotics effects.
- Taking medications: side effects.
- Resources conservation for producing safe medications.
- Search for alternative solutions: Pros and cons.
- Traditional treatment: consequences.
Popular non-drug treatments of certain diseases cause complications. Project on a topic that concerns it will help in attracting attention to it.
Psychology Capstone Project Ideas
It is tricky enough to choose capstone project ideas for Psychology. After all, the topic is based on a certain interest. You should forget titles you are not interested in. You will get a good grade if you describe a few opinions on the covered topic. You can finish the course in Psychology by working on one of the following topics:
- Incurable diseases' impact on psychological health.
- Are gender and a propensity for depression linked?
- Algorithm for forming habits.
- Influence of upbringing in Rainbow Family on the reality assessment.
- Television's impact on the patients' well-being.
- Obesity and what psychological problems it hides.
- Stress and its impact on different groups of people.
- Psychological development: key stages.
- Psychological improvement under external factors.
- Hyperactivity: Who is to “blame”.
Diverse topics can show you from a new side. It will allow you to put forward your own theory.
Education Capstone Project Ideas
You can show skills and capabilities for critical thinking upon deciding your capstone project ideas on Education. Those students who have chosen the right direction can get topics and continue with their professional growth. The search for worthy topics about education in a school or college will become a starting point for future achievements.
Mathematics Capstone Project Ideas
The right Math capstone project ideas will allow you to take a new look at application of calculations in everyday life. The following list will help you with finding a suitable idea:
- Hungarian approach in developing educational programs.
- Ranking methods.
- Partial and complex process interventions: comparison.
- The process of crystal structure formation.
- Process optimization using a mathematical approach.
- The number pi discovery history and development in modern science.
- Unproven theories: Why failure.
- Application of constants in calculations and their impact on the result.
- Mathematical description of the evacuation.
- Development of mathematics in the past decade.
Explaining the interest in mathematics is difficult. This doesn’t mean that there are no interesting ideas for the graduation paper. Theoretical studies ensure validity of results and allow you to control your research.
Capstone Project Ideas: High School
What are some quality high school capstone project ideas, you may ask. The education system is undergoing major changes. It is worth paying attention to the consequences of such innovations. Transition to remote learning allows to detect shortcomings in a new teaching method and develop problem-solving strategies. Choosing topics will be easier if you look through the list of options:
- Why should one attend extracurricular activities?
- Why is discrimination at school constantly increasing?
- What are the reasons for school children being more violent?
- Discipline at school: effective ways to combat disorder.
- Does respect for a teacher mean having no personal opinion?
- Conducting academic lessons while studying at school.
- Results of remote learning.
- Classroom modernization and its consequences.
- Advantages of using modern learning technologies.
- How do social networks affect the learning process at senior schools?
By studying relevant topics, you will be able to prepare for the beginning of professional activity in educational institutions.
Science Capstone Project Ideas
Science capstone project ideas depend on your direction, but this doesn’t mean that there are any restrictions. During the preparation of your final project, after completing your studies at Department of Technology, you should find new perspectives and consider those topics that potentially can make some contribution. Student must research their field of interest and focus on suitable options. Searching for information takes time, but the result is worth your effort. A small review will help you find a relevant topic.
Biology Capstone Project Ideas
By studying capstone project ideas for Biology, you can get answers for common questions. You can also find a simple solution for some issues. Thus, students can influence processes and prevent false information from spreading. Following these ideas will help get a dose of inspiration for you project:
- Reasons for different gestation periods among animals.
- Justification of gender identity by genetic specifics.
- Impact of alcohol abuse on health.
- Time required for habits to cause damage.
- Donating a human body for research.
- Which role does a human play in natural world?
- Biology of mental disorders (schizophrenia, depression).
- Viruses spread by rodents.
- Creating human body atlases: pros and cons.
- Three-dimensional modeling of life processes.
An attempt in creating a fascinating written piece will be a successful subject for studying reliable information from a few sources.
Can't find a fitting capston project topic idea? Give StudyCrumb's topic generator a try.
Physics Capstone Project Ideas
People’s interest in Physics is easy to explain. Simple and logically explained processes can help you get rid of vague questions easily. Right choice of ap physics capstone project ideas from the following list will provide you with necessary inspiration when preparing your paper:
- Real size of quantum world: how big is it?
- Interesting phenomena that are difficult to explain.
- Simple physical experiments at home.
- Interesting physics theory that can surprise.
- How does one demonstrate induction law?
- Lagrange multipliers: what is it?
- Gaps in black holes studies.
- Can everything be explained by physics laws?
- Crystal structure and how one can change it.
- What is gravitational attraction of planets like?
Any physical phenomenon that you are interested in can become the main subject of your study.
Data Science Capstone Project Ideas
Application of advanced technological methods for studying research results makes it possible to simplify project preparation, so you shouldn’t refuse such support. There is a variety of interesting capstone project ideas data science available:
- Forecast of health condition based on shopping list.
- How does information storage influence a person’s everyday life?
- Weather forecast based on historical data.
- Amount of solar energy, taking satellite images into account.
- How does one simplify false news detection?
- Determining a dog breed using a neural network.
- Analysis of users’ mood based on certain data.
- Convenient ways of storing information.
- Options for finding delicious food using data.
Keep in mind that your resources are not limited, so decide on a topic you are interested in. The more data you collect, the more field work you should go through.
Business Capstone Project Ideas
Conducting business activities enables you to cover various capstone project ideas Business. Final results of work will show how well resources have been allocated. This will also teach you to reach a new level using limited opportunities. Choice of a management tool affects research results as well. It will be much easier to cover your ideas if you shift attention to aspects of your interest. There are no other ways in which you can make your paper effective.
Management Capstone Project Ideas
Paper preparation will begin immediately after choosing project management capstone ideas and obtaining required information. Any organization that offers its services for visitors can become a research object. Modern trends show that following topics will find readers’ response:
- Corporate strategy and how one can conduct development.
- Profitability and capital investments: business value.
- Ways of solving conflict situations.
- Business activities and wrong decisions.
- How one can solve a professional conflict.
- Ways of developing a corporate mind.
- Assessment of key processes in a clinic.
- Managing different levels of administrator.
- Advertising campaign: right way of project management.
- Ethical thinking and management.
Creating a kind of application will enable you to find a way out of any difficult situations.
Topics for Capstone Project in Finance
Since students gain knowledge about commercial organizations’ financial activities, it will be impossible to avoid Finance capstone project. Those business areas that actively use financial resources are of particular interest. To choose worthy ideas, you can have look at suggested options:
- Common financial problems.
- Influence of financial resources on global economy.
- How one can manage risks at minimum losses.
- Corporate finance: how one can allocate resources.
- Internal financial markets and their interrelation with global resources.
- Investment analysis.
- Budget funds management systems.
- How one can decide on the advisability of investments?
- Electronic payment: how one can use it to business advantage.
- Developing a marketing plan to attract investors.
You can collect information you will need for your paper online.
IT Capstone Project Ideas
Using a global system so you can get results is no longer a new method. That's why choice of capstone project ideas for information Technology should be taken seriously. After all, modern computers are used more and more often in everyday life. It can provide access to a variety of publications. Use resources so you can cover a topic and be prepared to search far and wide for needed information. An advantage of choosing this field will be an opportunity to influence the future of an industry.
Computer Science Capstone Project Ideas
When it comes to capstone project ideas, Computer Science just begs to use opportunities offered by the Internet. An attempt of finding a suitable topic will be successful if you start with studying list of options for writing a paper about software:
- Automatic reporting.
- Developing computer software for registration.
- Studying data of a clinic’s clients.
- Using programs for enhancing work efficiency.
- Payment verification and ways of protecting accounts.
- Online customer survey system.
- How one can restore lost data?
- How one can prevent loss of important information?
- Personal data protection by using software.
- Main reasons for the importance of using programs.
Application of technology has reached a new practical level. You shouldn’t just get stuck with your regular printed books and papers. Searching for exciting topics and conducting studies won’t take long.
Cybersecurity Capstone Project Ideas
So, cyber security capstone project ideas are a thing nowadays. Cybersecurity plays an important role in the modern world, so, should you choose this field of study, don't ignore any piece information that you can find. Developing an exciting project will enable you to improve your skills and put them into practice. You should pay special attention to the following topics:
- Keylogger development and use of software in work.
- Data traffic analysis for process optimization.
- Network traffic and how to prevent hackers from accessing it.
- Personal data protection options.
- Decoding popular ciphers.
- Development of a program for data encryption.
- Creating antivirus security programs.
- Search for mistakes made on Internet websites.
- Practical skills to find system shortcomings.
- Software preparation for higher security.
Progressive developers should enhance modern skills and their practical application. To write a paper, you may need to get permission from an administrator, so you should keep that in mind.
Graphic Design Capstone Project Ideas
A graphic designer is a sought-after expert in the modern world and capstone design project ideas should be as good as they can. List of specialist’s main tasks includes developing logos and booklets, writing a video series for advertising products, and much more. An attempt to create a new graphical solution is the first level of skills improvement. Search for topics is the second important step, with the following to help you:
- Ways to unify characters.
- Animation and cartoon creation.
- Unique business project design.
- Improving a developed object.
- Conditions that affect design efficiency.
- Three-dimensional character modeling.
- Modern programs for creating graphics.
- Logo development depending on field.
- Individual graphic projects.
- Graphic design as a way of communicating with clients.
By developing unique design, you will attract large companies’ attention and become a confident competitor in this field.
Engineering Capstone Project Ideas
Among capstone project ideas, Engineering is one of the most interesting topics. It’s also widely promoted around the world. Available resources are used in full force, which enhances technical progress. It is still too early to stop at what has been achieved, so one should keep working and demonstrate great results. Search for topics takes quite long since this field is rapidly developing. Transition to alternative solutions to everyday tasks forces us to look for safe and working ways to achieve your set goal.
Mechanical Engineering Capstone Project Ideas
To develop capstone project ideas for Mechanical Engineering, you need to be interested in finding a solution. It’s impossible to do this without a proper interest in a breakthrough. Use knowledge you got to your advantage and take a closer look at suggested list of exciting topics:
- Remote-controlled vehicles.
- Automatic limbs: application.
- Using a mobile device to control movements.
- Development of mechanical skills to improve performance.
- Drinking water purification using a drive.
- Chain mechanisms: how to use engines for work.
- Working with devices: peculiarities.
- Performing mechanical actions instead of a human.
- Ways to improve a mechanical engine performance.
- Why does field development play an important role?
An unbiased look at existing problems will enable you to show your creative potential and prove that your are suited to be a mechanical engineer.
Electrical Engineering Capstone Project Ideas
What can you say about capstone project ideas for Electrical Engineering? Electrical engineering plays a special role in everyday life. It also significantly improves quality of life. Technology studies will not only emphasize its importance but will have you understand a thing or two about its efficiency as well. You can choose topics from the following list:
- Solar panel management.
- Creating a hybrid vehicle.
- Advantages and disadvantages of using electricity.
- Ways to extend solar panels' life.
- Systems for detecting red traffic light signals.
- Importance of electricity in everyday life.
- How to use solar panels at home?
- Solar energy and ways to use it.
- Devices for energy accumulation.
- Electric power: disadvantages of power source.
Choosing a specific direction will help you demonstrate your potential and focus on solving everyday problems.
Computer Engineering Capstone Project Ideas
With how important technology is nowadays, it's no surprise that capstone project ideas for Computer Engineering are quite popular. Students are engaged in developing new software for solving a variety of tasks. Your capstone project should be aimed at introducing computer systems-based technologies. Popular topics consist of a few relevant topics:
- Machine learning or how a computer can recognize a cat.
- Security system with lock function.
- Using modern registration methods.
- Automatic schedule creation: results of development.
- Selection of music considering emotional state.
- Facial emotion recognition system.
- A knowledge assessment system considering students' level of knowledge.
- Remote monitoring of vital activity systems.
- Ways to create an image with encrypted text.
- How to detect a hacker attack?
Introduction of engineering solutions in everyday life can improve quality of services. It can provide necessary support to people with health problems.
Civil Engineering Capstone Project Ideas
Civil Engineering capstone project ideas are important if you are interested in seeing physical evidence of your work in real life. To develop the selected area, you will need to make efforts to improve conditions for people to live in. If you want to answer some concerns of accomplished professionals in this field, you need to prepare a project on one of the following topics:
- Foundation analysis.
- Stages of designing high-rise buildings.
- Carrying out seismic activity calculations.
- Project management in construction field.
- Building design.
- Performing training of resistance to loads.
- Quantitative shooting to detect deficiencies.
- Advantages and disadvantages of seismic research.
- Open soil and design peculiarities.
- Types of soils and their influence on buildings’ duration.
A study of natural resources influence on service life and peculiarities of building construction and a careful study of underlying factors will result in an improvement in current results.
Final Thoughts on Ideas for Capstone Project
Choice of work field is based on conducting research on capstone project topics. Lack of interest has a negative effect on quality. It will be much easier to test your achievements and skills in the course using the latest topics. Or buy capstone project online for a shortcut.
Our paper writing service can help to write a capstone project for you. We guarantee meeting the deadlines and deliver a project og of high quality.
FAQ about Capstone Project Ideas
1. are capstone projects hard.
Completing the course in chosen specialty implies mandatory preparation of capstone projects. The main challenge is to choose a topic and conduct research. As a student, you should demonstrate your skills in a chosen field. It’s enough to take the first step in right direction, though. The main problem is to find a really interesting topic.
2. What is the point of capstone?
Purpose of preparing a capstone project is to demonstrate your professional attitude to raised problems. Using acquired knowledge and an opportunity to make the world a better place are the main reasons to start preparing final project.
3. What is the difference between a thesis and a capstone project?
It is worth paying attention to differences between capstone project and a thesis. Basically, thesis is written when you're aiming for bachelor's and master's degrees. Meanwhile, capstone project is a piece of writing that you are expected to finish (typically) at the end of high school. Considering this, the length and scope can be different. For instance, capstone focuses on a narrower and more specific area. At the same time, thesis is written on much broader topic.
4. Is capstone required?
No, a capstone is usually not required. Some schools may make it mandatory for certain degrees, though. Choosing a specific topic means that student is willing to take risks. It shows that you try to achieve their goal. There is no other way to draw a final line in the chosen education program.
Daniel Howard is an Essay Writing guru. He helps students create essays that will strike a chord with the readers.
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All Capstone Projects (2017-2021)
A Data-Driven Approach to Forecasting the U.S. Beer Industry
Suggested Order Quantities
Natural Language Processing for Customer Experience Evaluation
Business Churn Projection and Prediction
Revenue Integrity: Fraudulent Booking Identification
Portfolio Recommendation System for A Leading Coca-Cola Bottler
Prioritizing Customers Visits
True Sales Potential: Unleashing the untapped opportunity
Predicting Approval and Denial Rates for Insurance Shoppers
Fostering Innovative Outreach Methods to Engage with New and Existing Customers
Demand Forecasting for a Luxury Fashion Retailer
Trend Forecasting to Quantify Consumer Sentiment
Customer Retention & Targeted Recommendations
Option Take-Rate Forecasting for the BMW Group
Automotive Noise Mining and Classification
Car Recommender for U.S. Dealerships
Connecting the Dots: Matching Existing Solutions to New Defects
Automating the quality control in car manufacturing using computer vision
Reprice with Confidence: Dynamic Pricing with Robust Time-series Forecasting
Cloud Cost Prediction
COLUMBIA THREADNEEDLE INVESTMENTS/AMERIPRISE FINANCIAL
Quantifying Advisors Marketing Engagement and Predicting Quality Leads for Sales
Optimizing Content Likely Personalization
Chatbot or Call? Optimal Contact Channel Selection for Customer Issue Resolution
Automated Dataset Creation using PDF Text Extraction
Improving SMS Customer Experience through a Transformer-based Chatbot
Transport Acquisition Recommendation
Identifying Customer Sentiment’s Business Impact
Predicting Appliance Failures
Zero Crashes Initiative
Tackling Congestion Using Connected Car Data
Crowd Sourcing Fuel Data for Sustainable Routing Algorithms
Understanding US Dealership Visitation through Automated Geofence Creation
Electric Vehicle as an Energy Reservoir: Vehicle-to-Grid
[m]clusters: Audiences First
Project Peggy Olson: Data Driven Creativity
Peggy Olson 2.0: Creative AI
Advertisement Attribution for Smarter Channel Investment Strategy
Dynamic Promotion Optimization over Sparse Demand Regression
The Hidden Cost of Healthcare: Transforming medical equipment management
A Data-Driven Approach to Healthcare
Intent Classification from Unlabelled Dataset
Explainability and Bias Removal in Natural Language
Prediction and Optimization of Medical Billing Operations
USTRANSCOM Flight Data Analysis
Optimizing Lab Procurement with Sparse Vendor Selection
Predictive Aircraft Maintenance: Detecting Imminent Part Failure with Cox Regression and Advanced Ensemble Learning Methods
Avert Disaster: Safety Modeling for the Military Sealift Command (MSC) Ships
Automating UAV Classification and Detection Through Signal and Image Recognition
Budget Allocation Through Marketing AttributioN, a.k.a. BATMAN
Generating Product Recommendations for Small Businesses at Scale
Email Performance and Personalized Recommendations
MASS GENERAL HOSPITAL (MGH)
Interpretable Machine Learning to Alleviate Bias In Trauma Patient Disposition
Routing Vehicles for MBTA’s The Ride
Reducing Costs at The Ride
Paratransit Operations: Impact of Driver Behavior and Demand Forecast
Ridership forecasting and automated geocoding for paratransit ride services
MCKINSEY & CO
What are Large Organizations Hungry For?
Introducing Ratatouille: a Generalizable, Goal-Oriented Dialog Bot
Machine Learning Methods in Credit Risk
Industrial Agglomeration for Single-Industry Spatial Pattern Recognition and Predictive Growth Modeling
Algorithm for Vector-Based Topic Extraction with NLP
Knowledge video summarization through AI
Segmenting Retail Advisors and Optimizing Coverage Model
From Unstructured Text Data to Interpretable Financial Prescriptions: An Optimization Approach
Optimal Client Interaction
To meet, or not to meet, that is the question: Optimizing Interaction Strategies
NEON PAGAMENTOS SA
Customer Relationship Network for Credit Default Prediction
Local Inventory Deployment Optimization
Forecasting Demand for E-Commerce
Prevenar Factory Schedule Optimization: A Mixed Integer Programming Approach
Sharing is Caring: Investigation Load Balancing
Predicting Disease from Longitudinal Laboratory Data
Disease Risk Evaluations in Life Insurance Underwriting via Laboratory and Prescription-Driven Diagnosis Models
Finding the Needle in the Haystack: Anomaly Detection in the Cybersecurity Industry
Lateral Movement Detection: Leveraging Data in the Cybersecurity Industry
RUE GILT GROUPE
Navigation-Based Personalized Recommender System
Deep Reinforcement Learning to Automate Acoustic Data Processing
Reliable Machine Learning in a World of Uncertainty
Price Prediction for the Dubai Residential Real-Estate Market
Brewing a Better Shot: IoT Predictive Maintenance for Mastrena II Espresso Machines
Automated Ticket Trading
Events and Tickets Representation Learning and Personalized Recommendation
Dynamic Pricing Models
Home Page Event Recommendation Optimization
Project Phoenix: Wildfire Prediction in Canada
Protection Gap Explorer: A Data-Driven Exploration of US Life Underinsurance
Life and Health in a Changing Climate
Understanding what causes suboptimal operational performance in clinical trials
THERMO FISHER SCIENTIFIC
Empowering Sales Management with Potential Detection and Conversion Analysis
Optimizing User Experience in Hotels Searches by Accurate Price Forecasts
Demand Forecasting with a Segmented Approach
Digital Marketing Attribution Model
Personalized Marketing: Who, How, and When to Market Any Product at Target
Opioid Detection in US Mail Stream
Creating a Tool to Diagnose Out Of Stock Causes
Improving Inventory Placement for Walmart E-Commerce
Planogram Optimization: Finding Optimal Product Placement on the Shelf
Transportation and Shipping Efficiency
The Value of a Day: Optimizing Delivery Time
Optimizing Targeting Strategy for Services
Characterizing Intent Using Customer Journey: a Sequential and Graphical Model Approach
What Products Should be Displayed? Double Assortment Optimization
- Oct 18, 2021
- 10 min read
Google Data Analytics Capstone Project
Updated: Feb 22
I worked on the Google Data Analytics Capstone Project, Track 1, Case Study 1. I will be diving into the background, my full process of cleaning, analyzing and visualizing the data, along with my final suggestions and summary of the data.
Quick Links :
Tableau Dashboard | Github R Code for Analysis | Github R Code for Tableau Visualization | LinkedIn Post
Below is a table of contents in case you want to go to a specific section.
Table of Contents:
Summary of Data
What I Learned
Cyclistic is a bike sharing program which features more than 5,800 bikes and 600 docking stations. It offers reclining bikes, hand tricycles, and cargo bikes, making it more inclusive to people with disabilities and riders who can't use a standard two-wheeled bike. It was founded in 2016 and has grown tremendously into a fleet of bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.
Previously, Cyclistic's marketing strategy tried to build the general awareness and appeal to broad consumers. It has flexible pricing plans: single-ride passes, full-day passes, and annual memberships. Those who purchase single-ride or full-day passes are referred to as casual riders while those who purchase annual memberships are Cyclistic members .
My Role : In this scenario I am a junior data analyst at Cyclistic and my team has been tasked with the overall goal (see below) of designing marketing strategies
Overall Goal : Design marketing strategies aimed at converting casual riders into annual members.
Business Question : "How do annual members and casual riders use Cyclistic bikes differently?"
Below I will describe step-by-step the process I used to for this project. If you want to skip ahead to the business suggestions move onto the section "Insights".
Overview : I first analyzed the data separately (each month) in Excel, then used R to analyze the data as a whole (one year). Finally I created a dashboard in Tableau and used Figma to support the design elements.
I initially wanted to gather and analyze my data in Excel because it was the tool I was most familiar with and I could get a general understanding of the data quicker. I did not combine all of the spreadsheets into one because that would've taken more processing power than my computer had.
I began downloading the data from divvy-tripdata , and turning the .csv files into excel spreadsheets. I downloaded the most recent year of data which was at the time of starting my project:
Added two columns to all of the months:
ride_length calculated the total ride length for each trip using the start_at column which was: ending time minus starting time.
day_of_week calculated the day of the week for each trip using the start_at column date.
Went over the business task and the information I had at hand and how that could be used to figure out how members and casual riders use the bike service differently
Came up with metrics to look at such as :
total number of rides per hour, per day of the month, per season, per day of the week, and for different bike types
Average ride length between members and casual
For every month in Excel created pivot tables and charts to go with the analysis on (this took the longest):
Total Rides per Weekday - calculated the total rides for members and casual and separated it by day of the week; used a cluster column chart
Average Ride Length - calculated the average ride length for members and casual and separated it by day of the week; used a cluster column chart
Total Rides per Hour - calculated the total rides for members and casual separated by the time of the day (24hr); used a line comparison chart
Total Rides per Day - calculated the total rides for members and casual separated by the day of the month; used a line comparison chart
Total Rides per Bike Type - calculated the total rides for members and casual separated by Bike type; used stacked column chart
I also created a Google docs Notes list where I wrote down the exact steps for each month (had a checklist) and included my insights for each month
535 minutes or just under 9 hours to complete.
I originally wanted to use SQL but the files were too big to upload and I couldn't figure out how to utilize Google Cloud Platform. Instead I used R to analyze the data because it could handle all of the information quicker than Excel, and I wanted to work on my R skills. Below is my general process in R, I didn't include my mistakes/missteps or errors for the sake of brevity. If you are interested in my full process including my mistakes, you can email me at: [email protected] and I would be happy to discuss it.
View my full code on my Github for this capstone project here .
Load all of the libraries I used: tidyverse, lubridate, hms, data.table
Uploaded all of the original data from the data source divytrip into R using read_csv function to upload all individual csv files and save them in separate data frames. For august 2020 data I saved it into aug08_df, september 2020 to sep09_df and so on.
Merged the 12 months of data together using rbind to create a one year view
Created a new data frame called cyclistic_date that would contain all of my new columns
Created new columns for:
Ride Length - did this by subtracting end_at time from start_at time
Day of the Week
Time - convert the time to HH:MM:SS format
Season - Spring, Summer, Winter or Fall
Time of Day - Night, Morning, Afternoon or Evening
Cleaned the data by:
Removing duplicate rows
Remove rows with NA values (blank rows)
Remove where ride_length is 0 or negative (ride_length should be a positive number)
Remove unnecessary columns: ride_id, start_station_id, end_station_id, start_lat, start_long, end_lat, end_lng
Calculated Total Rides for:
Total number of rides which was just the row count = 4,152,139
Member type - casual riders vs. annual members
Type of Bike - classic vs docked vs electric; separated by member type and total rides for each bike type
Hour - separated by member type and total rides for each hour in a day
Time of Day - separated by member type and total rides for each time of day (morning, afternoon, evening, night)
Day of the Week - separated by member type and total rides for each day of the week
Day of the Month - separated by member type and total rides for each day of the month
Month - separated by member type and total rides for each month
Season - separated by member type and total rides for each season (spring, summer, fall, winter)
Calculated Average Ride Length for:
Total average ride length
Type of Bike - separated by member type and average ride length for each bike type
Hour - separated by member type and average ride length for each hour in a day
Time of Day - separated by member type and average ride length for each time of day (morning, afternoon, evening, night)
Day of the Week - separated by member type and average ride length for each day of the week
Day of the Month - separated by member type and average ride length for each day of the month
Month - separated by member type and average ride length for each month
Season - separated by member type and average ride lengths for each season (spring, summer, fall, winter)
Then using all of this data I created my own summary in my case notes and took note of the: total rides for each variable, average ride lengths for each variable, and the difference between members versus casual riders. I originally wanted to create a report using R Markdown as well but for the sake of time (I had already spent over 20 hours on the project so far), I decided to skip this step, and write this article instead.
1045 minutes or about 17 and a half hours to complete.
While I learned the basics of Tableau in the Google Course I wanted more practice with visualizing data and creating dashboards.
To view my completed dashboard click here .
I created a separate R code (you can view it here on Github) that made some changes for specifically the Tableau portion.
For ride length I rounded the digits by 1, meaning my numbers were 29.8 or 12.5.
Revised how I created my "month" column. I used mutate() to create a column that had the month in ___ format and not number format. So instead of 01 it would say "January"
Cleaned the data: removed rows with NA values, removed duplicate rows, removed where ride_length was 0 or negative and removed unnecessary columns like: ride_id, start_station_id, end_station_id, start_lat, start_long, end_lat, end_lng
Created a new dataframe with this information so I could test the difference between the original data frame (cyclistic_date) that I used for my analysis and the data frame I would use for Tableau (cyclistic_tableau).
In this new data frame I removed more columns to make calculations quicker in Tableau. I removed: start_station_name, end_station_name, time, started_at, ended_at
Downloaded this data frame into a .csv file which I uploaded to Tableau
Created graphs similar to those I created in Excel but added a few:
Total Rides by Bike Type
Ride Length by Weekday
Total Rides by Weekday
Total rides by hour, total rides by month.
Then I created a basic dashboard with all of that information, a prototype for me to view while I was creating the final dashboard ( Figure 1 below).
Created a prototype mockup in Figma, added in values like average ride length, busiest month, season, hour, time of day, and most used bike type
Created a final version of the mockup in Figma
Edited Dashboard in Tableau to reflect design in Figma
Edited graphs in Tableau
Made bar graphs round
Highlights to specific important notes
Got rid of labels for visual purposes
Combined Figma and Tableau (used dashboard created in Figma as the background for my Tableau Dashboard) to create a final prototype ( Figure 2 below)
Made minor edits to design elements and created final dashboard (see Finished Project below)
765 minutes or almost 13 hours to complete.
I am including the other tools I used.
Figma to create my background and help develop the dashboard aesthetics.
Google Docs helped me keep track of all of my documents for this project like:
Date Log - I wrote down what I did that day related to my project
Resources - A list of resources I frequently used
Case Notes - Notes for the case study including the final insights, what I was looking for, and anything else having to do with the case
Evernote to draft this article before I uploaded it here.
Here is my finished project: Google Capstone Project | Cyclistic . You can view the links to my R code on Github used for analysis here and the code for Tableau here .
SUMMARY OF DATA
Those who purchase single-ride or full-day passes are referred to as casual riders while those who purchase annual memberships are Cyclistic members .
Total Rides by User Type
Average Ride Length per User Type
Average Ride per Weekday
Members had more rides with 2,328,763 total rides or 56% and casual riders had 1,823,376 total rides or 43%.
Total Rides per Bike Type
Both casual riders and members used the classic bike the most with 1,777,593 rides or 43% of total rides, followed by docked bikes with 1,545,936 rides or 37% of total rides, and lastly with electric bikes at 828,610 rides or 20% of total rides.
Average Ride Length by User Type
The total average ride length was 24 minutes. For casual riders it was longer at 27 minutes while members was 14 minutes.
Average Ride Length per Weekday
For the average ride length per weekday both casual riders and members had an increase in the average ride length on the weekends. For both Sunday was the longest at 31 minutes.
Saturday was the most popular weekday combining casual riders and member rides with 784,239 rides or 19% of total rides. But for member rides only Wednesday was the most popular day with 356,060 rides, 5,407 rides more than Saturday.
5PM or 17:00 was the busiest hour for both members and casual riders with 426,685 rides or 10% of the total rides. Typically rides began increasing in the morning at 6AM and rose until 5PM then dropped afterwards. The afternoon was the busiest for both rider types with 1,905,797 rides or 45% of total rides. 4AM was the least popular hour.
July was the busiest month combining casual riders and member rides at 691,476 rides or 16% of total rides. While summer was the most popular season for both at 1,903,446 rides or 46% of total rides. Looking at just members August is actually the busiest month with 323,140 rides, 816 rides more than July. Winter is the least popular season and February is the least popular month.
The most popular bike among with riders was the classic.
Busiest time was afternoon and the peak time was at 5PM for both casual riders and members.
Busiest weekday was Saturday, casual riders used the service the most on the weekends.
Busiest season was Summer for both types of riders.
Most rides by User Type was members but casual riders weren't far behind.
The average ride length was 24 minutes but casual riders on average rode 23 minutes longer than members.
This was the hardest part for me for the whole project. I have never provided suggestions for a business nor worked in marketing. Any feedback here would be appreciated.
These are my suggestions for the marketing team to convert casual riders to annual members:
Personalize discounts and show perks in the membership program based on their preferences and riding habits.
Emphasize the benefits of memberships, including discounts during busy times of the year like during Summer, or on the weekends.
Have existing members to share their stories about how using Cyclistic's system has changed their life, to create a sense of community, offer a discount if they do so this will help encourage new riders to join the program.
WHAT I LEARNED
Below is what I learned/practiced from over 40 hours spent on this project:
Pivot Tables in Microsoft Excel
Practice using R for data analysis and cleaning specifically using the tidyverse package for data analysis
Graphs in Tableau, edited visual elements along with creating different charts and filters.
Design elements of an effective dashboard
Combining the design feature of Figma with the functionality of Tableau
R portion of my project I found Itamar's case study on Kaggle using R as well, a helpful resource.
Tableau portion I used Navneet Singh's Tableau Dashboard as inspiration.
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The Best 150 Capstone Project Topic Ideas
10 May 2022
❔What is a Capstone Project?
Capstone Project Ideas:
- 💾Computer Science
- 🎒High School Education
- 💻Information Technology
✅Capstone Writing: 10 Steps
The long path of research works ahead, and you can’t find any capstone project ideas that would be interesting and innovative? The task can seem even more challenging for you to feel all the responsibility of this first step. The top 150 capstone ideas presented below aim to make a choice not so effort-consuming.
With the list of the capstone project topics we've picked for you, you'll be covered in major subjects. Continue reading, and you'll get ideas for capstone projects in information technology, nursing, psychology, marketing, management, and more.
What is a Capstone Project?
Educational institutions use the capstone project to evaluate your understanding of the course on various parameters. For the students, the work on the project gives an excellent opportunity to demonstrate their presentation, problem-solving and soft skills. Capstone projects are normally used in the curriculum of colleges and schools. Also called a senior exhibition or a culminating project, these assignments are given to finish the academic course.
This assignment has several different objectives, among which are the following:
- to encourage independent planning,
- to learn to meet up deadlines,
- to practice a detailed analysis,
- to work in teams.
It's not that easy to pick the right capstone paper topic. The problem intensifies as each student or separate teams have to work on a single assignment which has to be unique. The best capstone project ideas may possibly run out. However, whatever topic you opt for, you’d better start your preparation and research on the subject as early as possible.
Need help with writing capstone project?
Get your paper written by a professional writer
Amazing Capstone Project Ideas for Nursing Course
Studying nursing is challenging, as it requires a prominent theoretical foundation and is fully practical at the same time. You should have to do thorough research and provide evidence for your ideas, but what to start with? The preparation for your capstone project in nursing won’t be so overwhelming if you make use of these capstone title ideas:
- Innovation and Improvement in Nursing
- Vaccination Chart Creation
- The Role of Nurses in Today's Society
- Shortage in Nursing and Its Effects on Healthcare
- Evidential Practices and Their Promotion in Nursing
- Global Changes in the Approach to Vaccination
- Top Emergency Practices
- Preventive Interventions for ADHD
- Quality of Nursing and Hospital Personnel Shifts: The Interrelation
- Ways to Prevent Sexually Transmitted Diseases
- Brand New Approaches in Diagnostics in the Nursing Field
- Diabetes Mellitus in Young Adults: Prevention and Treatment
- Healthcare in Ambulances: Methods of Improvement
- Postpartum Depression Therapy
- The Ways to Carry a Healthy Baby
Attractive Computer Science Capstone Project Ideas
Computer science is so rapidly developing that you might easily get lost in the new trends in the sphere. Gaming and internet security, machine learning and computer forensics, artificial intelligence, and database development – you first have to settle down on something. Check the topics for the capstone project examples below to pick one. Decide how deeply you will research the topic and define how wide or narrow the sphere of your investigation will be.
- Cybersecurity: Threats and Elimination Ways
- Data Mining in Commerce: Its Role and Perspectives
- Programming Languages Evolution
- Social Media Usage: How Safe It Is?
- Classification of Images
- Implementation of Artificial Intelligence in Insurance Cost Prediction
- Key Security Concerns of Internet Banking
- SaaS Technologies of the Modern Time
- Evolvement of Mobile Gaming and Mobile Gambling
- The Role of Cloud Computing and IoT in Modern Times
- Chatbots and Their Role in Modern Customer Support
- Computer Learning Hits and Misses
- Digitalization of Education
- Artificial Intelligence in Education: Perspectives
- Software Quality Control: Top Modern Practices
Build your thesis statement
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Several High School Education Capstone Project Ideas for Inspiration
High school education is a transit point in professional education and the most valuable period for personal soft skills development. No wonder that the list of capstone project ideas in high school education involves rather various topics. They may range from local startup analysis and engineer’s career path to bullying problems. It’s up to you to use the chosen statement as the ready capstone project title or just an idea for future development.
- A Small Enterprise Business Plan
- Advantages and Disadvantages of Virtual Learning in Schools
- Space Tourism: The Start and Development
- Pros and Cons of Uniforms and Dress Codes
- What is Cyberbullying and How to Reduce It
- Becoming a Doctor: Find Your Way
- Career in Sports: Pros and Cons
- How to Eliminate the Risks of Peer Pressure
- Ensuring Better Behaviours in Classroom
- Cutting-Edge Technologies: NASA versus SpaceX
- The Reverse Side of Shyness
- Stress in High School and the Ways to Minimize It
- How to Bring Up a Leader
- Outdated Education Practices
- Learning Disabilities: What to Pay Attention to in Children’s Development
Capstone Project Topics in Information Technology – Search for Your Best
Information technology is a separate area developed on the basis of computer science, and it might be challenging to capture the differences between them. If you hesitate about what to start with – use the following topics for capstone project as the starting point for your capstone research topics.
- Types of Databases in Information Systems
- Voice Recognition Technology and Its Benefits
- The Perspectives of Cloud Computing
- Security Issues of VPN Usage
- Censorship in Internet Worldwide
- Problems of Safe and Secure Internet Environment
- The Cryptocurrency Market: What Are the Development Paths?
- Analytics in the Oil and Gas Industry: The Benefits of Big Data Utilization
- Procedures, Strengths and Weaknesses in Data Mining
- Networking Protocols: Safety Evaluation
- Implementation of Smart Systems in Parking
- Workplace Agile Methodology
- Manual Testing vs. Automated Testing
- Programming Algorithms and the Differences Between Them
- Strengths and Weaknesses of Cybersecurity
Psychology Capstone Project Ideas
Society shows increasing attention to mental health. The range of issues that influence human psychology is vast, and the choice may be difficult. You’ll find simple capstone project ideas to settle on in the following list.
- The Impact of Abortion on Mental Health
- Bipolar Disorder and Its Overall Effects on the Life Quality
- How Gender Influences Depression
- Inherited and Environmental Effects on Hyperactive Children
- The Impact of Culture on Psychology
- How Sleep Quality Influences the Work Performance
- Long- and Short-Term Memory: The Comparison
- Studying Schizophrenia
- Terrorist’s Psychology: Comprehension and Treatment
- The Reasons for Suicidal Behaviour
- Aggression in Movies and Games and Its Effects on Teenagers
- Military Psychology: Its Methods and Outcomes
- The Reasons for Criminal Behavior: A Psychology Perspective
- Psychological Assessment of Juvenile Sex Offenders
- Do Colours Affect The Brain?
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Capstone Project Ideas for Management Course
Studying management means dealing with the most varied spheres of life, problem-solving in different business areas, and evaluating risks. The challenge starts when you select the appropriate topic for your capstone project. Let the following list help you come up with your ideas.
- Innovative Approaches in Management in Different Industries
- Analyzing Hotels Customer Service
- Project Manager: Profile Evaluation
- Crisis Management in Small Business Enterprises
- Interrelation Between Corporate Strategies and Their Capital Structures
- How to Develop an Efficient Corporate Strategy
- The Reasons For Under-Representation of Managing Women
- Ways to Create a Powerful Public Relations Strategy
- The Increasing Role of Technology in Management
- Fresh Trends in E-Commerce Management
- Political Campaigns Project Management
- The Risk Management Importance
- Key Principles in the Management of Supply Chains
- Relations with Suppliers in Business Management
- Business Management: Globalization Impact
Capstone Project Ideas for Your Marketing Course
Marketing aims to make the business attractive to the customer and client-oriented. The variety of easy capstone project ideas below gives you the start for your research work.
- How to Maximize Customer Engagement
- Real Businesses Top Content Strategies
- Creation of Brand Awareness in Online Environments
- The Efficiency of Blogs in Traffic Generation
- Marketing Strategies in B2B and B2C
- Marketing and Globalization
- Traditional Marketing and Online Marketing: Distinguishing Features
- How Loyalty Programs Influence Customers
- The Principles of E-Commerce Marketing
- Brand Value Building Strategies
- Personnel Metrics in Marketing
- Social Media as Marketing Tools
- Advertising Campaigns: The Importance of Jingles
- How to Improve Marketing Channels
- Habitual Buying Behaviours of Customers
Best Capstone Engineering Project Ideas
It’s difficult to find a more varied discipline than engineering. If you study it – you already know your specialization and occupational interest, but the list of ideas below can be helpful.
- How to Make a Self-Flying Robot
- How to Make Robotic Arm
- Biomass Fuelled Water Heater
- Geological Data: Transmission and Storage
- Uphill Wheelchairs: The Use and Development
- Types of Pollution Monitoring Systems
- Operation Principles of Solar Panels
- Developing a Playground for Children with Disabilities
- The Car with a Remote-Control
- Self-Driving Cars: Future or Fantasy?
- The Perspectives of Stair-Climbing Wheelchair
- Mechanisms of Motorized Chains
- How to Build a Car Engine
- Electric Vehicles are Environment-Friendly: Myth or Reality?
- The Use of Engineering Advancements in Agriculture
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Capstone Project Ideas for MBA
Here you might read some senior capstone project ideas to help you with your MBA assignment.
- Management Strategies for Developing Countries Businesses
- New App Market Analysis
- Corporate Downsizing and the Following Re-Organization
- How to Make a Business Plan for a Start-Up
- Relationships with Stakeholders
- Small Teams: Culture and Conflict
- Organization Managing Diversity
- What to Pay Attention to in Business Outsourcing
- Business Management and Globalization
- The Most Recent HR Management Principles
- Dealing with Conflicts in Large Companies
- Culturally Differentiated Approaches in Management
- Ethical Principles in Top-Tier Management
- Corporate Strategy Design
- Risk Management and Large Businesses
Capstone Project Ideas for an Accounting Course
Try these ideas for your Capstone Project in Accounting – and get the best result possible.
- How Popular Accounting Theories Developed
- Fixed Assets Accounting System
- Accounting Principles in Information Systems
- Interrelation Between Accounting and Ethical Decision-Making
- Ways to Minimize a Company’s Tax Liabilities
- Tax Evasion and Accounting: Key Principles
- Auditing Firm Accounting Procedures
- A New Accounting Theory Development
- Accounting Software
- Top Three World Recessions
- Accounting Methods in Proprietorship
- Accounting Standards Globally and Locally
- Personal Finance and the Recession Effect
- Company Accounting: Managerial Principles and Functions
- Payroll Management Systems
Capstone Writing: 10 Essential Steps
Be it a senior capstone project of a high school pupil or the one for college, you follow these ten steps. This will ensure you’ll create a powerful capstone paper in the outcome and get the best grade:
- One of the tips to choose a topic that your professors would be interested in is picking a subject in the course of your classes. Make notes during the term and you will definitely encounter an appropriate topic.
- Opt for a precise topic rather than a general one. This concerns especially business subjects.
- Have your capstone project topic approved by your professor.
- Conduct a thorough information search before developing a structure.
- Don’t hesitate to do surveys; they can provide extra points.
- Schedule your time correctly, ensuring a large enough time gap for unpredictable needs.
- Never avoid proofreading – this is the last but not least step before submission.
- Stick up to the topic and logical structure of your work.
- Get prepared to present your project to the audience, learn all the essential points, and stay confident.
- Accept feedback open-mindedly from your teacher as well as your peers.
Preparation of a powerful capstone project involves both selection of an exciting topic and its in-depth examination. If you are interested in the topic, you'll be able to demonstrate to your professor a deep insight into the subject. The lists of ideas above will inspire you and prepare you for the successful completion of your project. Don’t hesitate to try them now!
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26 Data Analytics Project Ideas and Datasets (2022)
Data analytics projects help you build your portfolio and land interviews. However, it’s not enough to just do an interesting analytics project. You also have to market your project to ensure it gets found.
The first step in starting any data analytics project is to come up with an interesting problem to investigate. Then, you need to find a dataset to analyze the problem. Some of the best categories for data analytics project ideas include:
- Python Analytics Projects - Python allows you to scrape interesting data, as well as perform analysis with pandas dataframes and SciPy libraries.
- Housing Analytics Projects - Housing data is readily available, or you can create your own dataset, and there are a variety of interesting analyses you can perform.
- Sports and NBA Analytics Projects - Sports data can be easily scraped, and using player and game stats, you can analyze strategies and performance.
- Data Visualization Projects - Visualizations allow you to create graphs, charts, etc, to tell a story about the data.
- Beginner Analytics Projects - For early-career data analysts, beginner projects help you practice new skills.
A data analytics portfolio is a powerful tool for landing an interview. But how can you build one effectively?
Start with a data analytics project and build your portfolio around it. A data analytics project involves taking a dataset and analyzing it in a specific way to showcase results. Not only do they help you build your portfolio, but analytics projects also help you:
- Learn new tools and techniques
- Work with complex datasets
- Practice packaging your work and results
- Prep for a case study and take-home interviews
- Give you inbound interviews from hiring managers that have read your blog post!
Python Data Analytics Projects
Python is a powerful tool for data analysis projects. Whether you’re web scraping data - on sites like the New York Times and Craigslist- or you’re conducting Exploratory Data Analysis (EDA) on Uber trips, here are three Python data analytics project ideas to try:
1. Enigma Transforming CSV file Take-Home
This take-home challenge - which requires 1-2.5 hours to complete - is a Python script writing task. You’re asked to write a script to transform input CSV data to desired output CSV data. A take-home like this is good practice for the type of Python take-homes that are asked of data analysts, data scientists, and data engineers.
As you work through this practice challenge, focus specifically on the grading criteria, which include:
- How well do you solve the problems
- The logic and approach you take to solving them
- Your ability to produce, document, and comment on code
- Ultimately, the ability to write clear and clean scripts for data preparation.
2. Wedding Crunchers
Todd W. Schneider’s Wedding Crunchers is a great example of a data analysis project using Python. Essentially, Todd scraped wedding announcements from the New York Times, and performed analysis on the data, finding interesting tidbits like:
- Distribution of common phrases
- Average age trends of brides and grooms
- Demographic trends
Using the data and his analysis Schneider created a lot of cool visuals, like this:
How you can do it: Follow the example of Wedding Crunchers. Choose a news or media source, scrape titles and text, and analyze the data for trends. Here’s a tutorial for scraping news APIs with Python.
3. Scraping Craigslist
Craigslist is a great data source for an analytics project, and there is a wide range of things you can analyze. One of the most common listings is for apartments.
Riley Predum created a handy tutorial that walks you through the steps of using Python and Beautiful Soup to scrape the data to pull apartment listings, and then was able to do some pretty cool analysis of pricing by neighborhood and price distributions. When graphed, his analysis looked like this:
How you can do it: Follow the tutorial to learn how to scrape the data using Python. Some analysis ideas: Look at apartment listings for another area, analyze used car prices for your market, or check out what used items sell on Craigslist.
4. Uber Trip Analysis
Here’s an interesting project from Aman Kharwal: An analysis of Uber trip data from NYC. The project used this Kaggle dataset from FiveThirtyEight , containing nearly 20 million Uber pickups. There are a lot of angles to analyze this dataset, like popular pickup times or the busiest days of the week.
Here’s a data visualization on pickup times by hour of the day from Aman:
How you can do it: This is a data analysis project idea if you’re prepping for a case study interview. You can emulate this one, using the dataset on Kaggle, or you can use these similar taxies and Uber datasets on data.world, including one for Austin, TX.
5. Twitter Sentiment Analysis
Twitter is the perfect data source for an analytics project, and you can perform a wide range of analyses based on Twitter datasets. Sentiment analysis projects are great for practicing beginner NLP techniques.
One option would be to measure sentiment in your dataset over time like this:
How you can do it: This tutorial from Natassha Selvaraj provides step-by-step instructions to do sentiment analysis in Twitter. Or see this tutorial from the Twitter developer forum . For data, you can scrape your own or pull some from these free datasets .
6. Home Pricing Predictions
This project was featured in our list of Python data science projects . With this project, you can take the classic California Census dataset , and use it to predict home prices by region, zip code, or details about the house.
Python can be used to produce some great visualizations, like this heat map of price by location:
How you can do it: Because this dataset is so well known, there are a lot of helpful tutorials to learn how to predict price in Python . Then, once you’ve learned the technique, you can start practicing it on a variety of datasets like stock prices, used car prices, or airfare.
Rental and Housing Data Analytics Project Ideas
There’s a ton of accessible housing data online, e.g. sites like Zillow and Airbnb, and these datasets are perfect for analytics and EDA projects.
If you’re interested in price trends in housing, market predictions, or just want to analyze the average home prices for a specific city or state, jump into these projects:
7. Airbnb Data Analytics Take-Home Assignment
- Overview: Analyze the provided data and make product recommendations to help increase bookings in Rio de Janeiro.
- Time Required: 6 hours
- Skills Tested: Analytics, EDA, growth marketing, data visualization
- Deliverable: Summarize your recommendations in response to the questions above in a Jupyter Notebook intended for the Head of Product and VP of Operations (who is not technical).
This take-home is a classic product case study. You have booking data for Rio de Janeiro, and you must define metrics for analyzing matching performance and make recommendations to help increase the number of bookings.
This take-home includes grading criteria, which can help direct your work. Assignments are judged on the following:
- Analytical approach and clarity of visualizations
- Your data sense and decision-making, as well as the reproducibility of the analysis
- Strength of your recommendations
- Your ability to communicate insights in your presentation
- Your ability to follow directions
8. Zillow Housing Prices
Check out Zillow’s free datasets. The Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.
Here’s an analytics project based in R that might give you some direction. The author analyzes Zillow data for Seattle, looking at things like the age of inventory (days since listing), % of homes that sell for a loss or gain, and list price vs. sale price for homes in the region:
How you can do it: There are a ton of different ways you can use the Zillow dataset. Examine listings by region, explore individual list price vs. sale price, or take a look at the average sale price over the average list price by city.
9. Inside Airbnb
On Inside Airbnb , you’ll find data from Airbnb that has been analyzed, cleaned, and aggregated. You’ll find data for dozens of cities around the world, including number of listings, calendars for listings, and reviews for listings.
Here’s a look at a project from Agratama Arfiano examining Airbnb data for Singapore. There are a lot of different analyses you can do, including finding the number of listings by host or listings by neighborhood. Arfiano has produced some really great visualizations for this project, like the following:
How you can do it: Download the data from Inside Airbnb, then choose a city for analysis. You can look at the price, listings by area, listings by the host, the average number of days a listing is rented, and much more.
10. Car Rentals
Have you ever wondered which cars are the most rented? Curious how fares change by make and model? Check out the Cornell Car Rental Dataset on Kaggle. Kushlesh Kumar created the dataset, which features records on 6,000+ rental cars. There are a lot of interesting questions you can answer with this dataset: Fares by make and model, fares by city, inventory by city, and much more. Here’s a cool visualization from Kushlesh:
How you can do it: Using the dataset, you could analyze rental cars by make and model, a specific location, or analyze specific car manufacturers. Another option: Try a similar project with these datasets: Cash for Clunkers cars , Carvana sales data or used cars on eBay.
11. Analyzing NYC Property Sales
This real estate dataset shows every property that sold in New York City between September 2016 and September 2017. You can use this data (or a similar dataset you create) for a number of projects, including EDA, price predictions, regression analysis, and data cleaning.
A beginner analytics project you can try would with this data would be a missing values analysis project like:
How you can do it: There are a ton of helpful Kaggle notebooks you can browse to learn how to: perform price predictions, do data cleaning tasks, or do some interesting EDA with this dataset.
Sports and NBA Data Analytics Projects
Sports data analytics projects are fun if you’re a fan, and also, because there are numerous free data sources available like Pro-Football-Reference and Basketball-Reference. These sources allow you to pull numerous statistics and build your own unique dataset to investigate a problem.
12. NBA Data Analytics Project
Check out this NBA data analytics project from Jay at Interview Query. Jay analyzed data from Basketball Reference (a great source, by the way) to determine the impact of the 2-for-1 play in the NBA. The idea: In basketball, the 2-for-1 play refers to the strategy that at the end of a quarter, a team aims to shoot the ball with between 25 and 36 seconds on the clock. That way the team that shoots first has time for an additional play while the opposing team only gets one response. (You can see the source code on GitHub).
The main metric he was looking for was the differential gain between the score just before the 2-for-1 shot and the score at the end of the quarter. Here’s a look at a differential gain:
How you can do it: Read this tutorial on scraping Basketball Reference data. You can analyze in-game statistics, play career statistics, playoff performance, and much more. One option would be to analyze a player’s high school ranking vs. their success in the NBA. Or you could visualize a player’s career.
13. Olympic Medals Analysis
This is a great dataset for a sports analytics project. Featuring 35,000 medals awarded since 1896, there’s plenty of data to analyze, and it’s great for identifying performance trends by country and sport. Here’s an interesting visualization from Didem Erkan:
How you can do it: Check out the Olympics medals dataset. Angles you might take for analysis include: Medal count by country (as in this visualization ), medal trends by country, e.g. how U.S. performance evolved during the 1900s, or even grouping countries by region to see how fortunes have risen or faded over time.
14. Soccer Power Rankings
FiveThirtyEight is a wonderful source of sports data; they have NBA datasets, as well as data for the NFL and NHL. The site uses its Soccer Power Index (SPI) ratings for predictions and forecasts, but it’s also a good source for analysis and analytics projects. To get started, check out Gideon Karasek’s breakdown of working with the SPI data.
How you can do it: Check out the SPI data. Questions you might try to answer include: How has a team’s SPI changed over time, comparisons of SPI amongst various soccer leagues, and goals scored vs. goals predicted?
15. Home Field Advantage Analysis
Does home-field advantage matter in the NFL? Can you quantify how much it matters? First, gather data from Pro-Football-Reference.com. Then you can perform a simple linear regression model to measure the impact.
There are a ton of projects you can do with NFL data. One would be to determine WR rankings, based on season performance .
How you can do it: See this Github repository on performing a linear regression to quantify home field advantage .
16. Daily Fantasy Sports
Creating a model to perform in daily fantasy sports requires you to:
- Predict which players will perform best based on matchups, locations, and other indicators
- Build a roster based on a “salary cap” budget
- Determine which players will have the top ROI during the given week
If you’re interested in fantasy football, basketball, or baseball, this would be a great project.
How you can do it: Check out the Daily Fantasy Data Science course , if you want a step-by-step look.
Data Visualization Projects
All of the datasets we’ve mentioned would make for amazing data visualization projects. To cap things off we are highlighting three more ideas for you to use as inspiration that potentially draws from your own experiences or interests!
17. Supercell Data Scientist Pre-Test
This is a classic SQL/data analytics take-home. You’re asked to explore, analyze, visualize and model Supercell’s revenue data. Specifically, the dataset contains user data and transactions tied to user accounts.
You must answer questions about the data, like which countries produce the most revenue. Then, you’re asked to create a visualization of the data, as well as apply machine learning techniques to it.
18. Visualize Your Favorite Book
Books are full of data, and you can create some really amazing visualizations using the patterns from them. Take a look at this project by Hanna Piotrowska, turning an Italo Calvo book into cool visualizations. The project features visualizations of word distributions, themes and motifs by chapter, and a visualization of the distribution of themes throughout the book:
How you can do it: This Shakespeare dataset , which features all of the lines from his plays, would be great for recreating this type of project. Another option: Create a visualization of your favorite Star Wars script .
19. Visualizing Pollution
This project by Jamie Kettle visualizes plastic pollution by country, and it does a scarily good job of showing just how much plastic waste enters the ocean each year. Take a look for inspiration:
How you can do it: There are dozens of pollution datasets on data.world . Choose one and create a visualization that shows the true impact of pollution on our natural environments.
20. Visualizing Top Movies
There are a ton of great movie and media datasets on Kaggle: The Movie Database 5000 , Netflix Movies and TV Shows , Box Office Mojo data , etc. And just like their big-screen debuts, movie data makes for great visualizations.
Take a look at this visualization of the Top 100 movies by Katie Silver , which features top movies based on box office gross and the Oscars each received:
How you can do it: Take a Kaggle movie dataset, and create a visualization that shows: Gross earnings vs. average IMDB rating, Netflix shows by rating, or visualization of top movies by the studio.
21. Gender Pay Gap Analysis
Salary is a subject everyone is interested in and it makes a great subject for visualization. One idea: Take this dataset from the U.S. Bureau of Labor Statistics , and create a visualization looking at the gap in pay by industry.
You can see an example of a gender pay gap visualization on InformationIsBeautiful.net:
How you can do it: You can re-create the gender pay visualization, and add your own spin. Or use salary data to visualize, fields with the fastest growing salaries, salary differences by cities, or data science salaries by the company .
Beginner Data Analytics Projects
Projects are one of the best ways for beginners to practice data science skills, including visualization, data cleaning, and working with tools like Python and pandas.
22. Relax Predicting User Adoption Take-Home
This data analytics take-home assignment, which has been given to data analysts and data scientists at Relax Inc., asks you to dig into user engagement data. Specifically, you’re asked to determine who an “adopted user” is, which is a user who has logged into the product on three separate days in at least one seven-day period.
Once you’ve identified adopted users, you’re asked to surface factors that predict future user adoption.
How you can do it: Jump into the Relax take-home data. This is an intensive data analytics take-home challenge, which the company suggests you spend 12 hours on (although you’re welcome to spend more or less). This is a great project for practicing your data analytics EDA skills, as well as surfacing predictive insights from a dataset.
23. Data Cleaning Practice
This Kaggle Challenge asks you to clean data , and perform a variety of data cleaning tasks. This is a great beginner data analytics project, that will provide hands-on experience performing techniques like handling missing values, scaling and normalization, and parsing dates.
How you can do it: You can work through this Kaggle Challenge, which includes data. Another option, however, would be to choose your own dataset that needs to be cleaned, and then work through the challenge and adapt the techniques to your own dataset.
24. Skilledup Messy Product Data Analysis Take-Home
This data analytics take-home from Skilledup, asks participants to perform analysis on a dataset of product details that is formatted inconveniently. This challenge provides an opportunity to show your data cleaning skills, as well as your ability to perform EDA and surface insights from an unfamiliar dataset. Specifically, the assignment asks you to consider one product group, named Books.
Each product in the group is associated with categories. Of course, there are tradeoffs to categorization, and you’re asked to consider these questions:
- Is there redundancy in the categorization?
- How can redundancy be identified and removed?
- Is it possible to reduce the number of categories dramatically by sacrificing relatively few category entries?
How you can do it: You can access this EDA takehome on Interview Query. Open the dataset and perform some EDA to familiarize yourself with the categories. Then, you can begin to consider the questions that are posed.
25. Marketing Analytics Exploratory Data Analysis
This marketing analytics dataset on Kaggle includes customer profiles, campaign successes and failures, channel performance, and product preferences. It’s a great tool for diving into marketing analytics, and there are a number of questions you can answer from the data like:
- What factors are significantly related to the number of store purchases?
- Is there a significant relationship between geographical regional and success of a campaign?
- How does the US compare to the rest of the world in terms of total purchases?
How you can do it: This Kaggle Notebook from user Jennifer Crockett is a great place to start, which includes a lot of great visualizations and analyses (like the one above).
If you want to take it a step further, there’s a lot of statistical analysis you can perform as well.
26. UFO Sightings Data Analysis
The UFO Sightings dataset is a fun one to dive into, and it contains data from more than 80,000 sightings over the last 100 years. This is a great source for a beginner EDA project, and you can draw a lot of insights out like where sightings are reported most frequently sightings in the US vs the world, and more.
How you can do it: Jump into the dataset on Kaggle. There are a number of notebooks you can check out with helpful code snippets. If you’re looking for a challenge, one user created an interactive map with sighting data .
More Analytics Project Resources
If you are still looking for inspiration, see our compiled list of free datasets which features sites to search for free data, datasets for EDA projects and visualizations, as well as datasets for machine learning projects.
You should also read our guide on the data analyst career path , how to build a data science project from scratch and list of 30 data science project ideas .
Customer Service Analysis
Why Learn Data Analytics
Data Analytics is an integral part of every business in Singapore. As market competition stiffens, top organisations are turning to data analytics to identify new market opportunities and provide insights to make critical business decisions.
To thrive in today’s business landscape, it is crucial to pick up industry tools such as Excel, SQL, and Tableau. Learn how to analyse and make insightful conclusions from raw data. Develop visualisation skills to create dashboards and effectively share your findings.
With Data Analytics skills, you will gain an in-demand and marketable skillset that can be applied to virtually any industry or profession.
Kickstart a career in Data Analytics with the beginner-friendly Data Analytics Bootcamp by Vertical Institute. The course allocates 3-4 instructional team members per class to ensure dedicated peer and mentor support for your Data Analytics journey that includes data visualisation projects . You will graduate with an IBF-accredited Data Analytics certification, as well as a polished, portfolio-ready Capstone Project Ideas for Data Analytics to showcase your analytical and visualization skills.
Our graduates from this data analytics course have come from GIC, GovTech, LinkedIn, JP Morgan, UBS, Citibank, Oracle, Lazada, ExxonMobil, and more.
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Top 8 Exciting Data Analytics Project Ideas & Topics [For Freshers]
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
Doing data Science courses has been one of the best and most reassuring career options of this generation for quite some time now. If you are an aspiring data scientist, you should be focusing more on improving your technical capabilities. By doing so, you will be increasing your skill level as a data scientist. The best way to practice your art is to take up personal projects to boost your knowledge, skills , and confidence.
Analyzing data also plays a significant role in your career growth. It is mostly about discovering new insights that can help with your decision making process. Even if you ask a veteran analyst, he will tell you that the intuition we see as consumers results from hard work. And around 80% of all data analytics assignments start with the evaluation of data. So, a data scientist needs to know more about data analysis and its types.
Rest assured, as time progresses; you will develop the necessary skills needed to collect data and produce reports based on your findings. You should also be able to:
- Clean the web data
- Execute exploratory analysis
- Flush cluttered datasets
- Visually communicate your results.
However, the most crucial part of becoming a skilled data scientist is working on various projects that focus on data scraping, exploratory analysis, and data visualization. So, let’s get started. Here are some of the project ideas that you will need to build up your job profile as a data scientist.
Table of Contents
Data Scraping Project Ideas
1. movie data collection.
This beginner project will help you gain the necessary skills needed for a data scientist. Its primary aim is to collect and extract data for further analysis. For that purpose, you can use the IMDB website to gather information about popular movies, TV shows, actors, etc. The format for this website is relatively consistent and makes it easier to attain data for analysis. Besides, the project has great potential when it comes to data collection.
2. Job Websites
Nowadays, scraping data from job portals are used for training beginner data scientists. It is because these websites contain standard data types. You can also maximize your learning capabilities through different online tutorial sessions. The main objective is to collect data and information about job titles, companies, locations, skills, etc. This project has an excellent aptitude for further visualization enhancements, such as comparing and mapping out the difference between talents and companies.
3, Online Shopping Sites
Another way to improve your necessary data analytics skill set is to scrap product and cost data from online shopping sites. For example, you can collect data and information about the trending Bluetooth headsets on Flipkart. And the collected data is analyzed further for processing the information you need for the project. It is wiser to start experimenting and analyzing data that uses more straightforward algorithms first. And then, pave your way to getting comfortable with intricate data design.
4. Social Media Platforms
A beginner level data analyst is expected to scrape data from social media websites. For instance, you can collect data from unconventional sites like Reddit or Twitter. Searching for keywords, upvotes, user data, etc., is all possible in Reddit, giving you ample resources for further investigation.
The website has gained popularity over the past years for its straightforwardness and content creation. As a data analyst, you can compare and analyze popular keywords with upvoted content. You can also take it a step further with exploratory analysis to check for any correlation between them.
Exploratory Data Analysis Project Ideas
1. global suicide scale.
The next step in improving your data scientist skills is to carry out exploratory data analysis on the data structure, patterns, and characteristics. For example, analyze the datasets that cover the numbers of suicide cases happening in different countries.
Also, find information on almost everything you get your hands on, ranging from the year, gender, the age to population and GDP. After completing the data collection process, try to see if any patterns involve suicide rates. If you get better at analyzing data, you can evaluate the percentages based on the rise or fall in suicide rates.
2. UN World Happiness Report
Compared to the previous project, this assignment involves the World Happiness report. This particular report keeps track of six main factors that measure happiness around the world. The six factors are life expectancy, economy, social support, lack of corruption, freedom, and generosity. Multiple questions can pop into your mind based on the report, which is an excellent exercise to expand your data analyst skills.
The first step will be to collect and extract the data needed for your project. You can find the report to be well-organized and consistent, making it easier for analysis. The main focus here will be to observe the patterns and data structure used to design the world report. Probing for more information is the best way to perform a complete analysis.
Utilizing the right dataset will give you room to enhance your technical skills. If you find yourself drawing a blank when it comes to complex structures, try resetting the analysis to your advantage. Make it simple, clear and concise to extract the necessary information needed to achieve your project goals.
Related: Top Data Science Project Ideas
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Data visualization project ideas, 1. covid-19 world report.
Apart from scraping, tiding, and analyzing the data, we have to find the means to communicate our results visually. In this case, we will be inspecting the Covid-19 health report. If you visit some famous sites like Kaggle, you get access to several thousands of Covid-19 datasets. The next step would be to collect data and scrap it. Tidy up the collected data for further investigation. Organized datasets make it easier for the analyst to visualize the results.
You can also perform various comparisons between different countries based on the number of active cases vs. the number of recovered patients. Producing charts and graphs are the critical elements needed for visualizing the results. And if you want to dive deeper, look for some online tutorials that can help you.
It does not matter whether you are interested in actors or brand culture. What matters is that Instagram has a unique set of data and information on various topics, making it a perfect instrument for visualization. The available options for analyzing this social media platform are boundless.
You can track the changes in the most followed accounts in real-time. Creating and developing bar charts based on the gathered information can help achieve your project goals. Advertising plays an essential role in this social media platform. Even comparing the company brands with popular brands will be an excellent exercise to amp up your tech skills.
Also Read: Top Data Analytics Project Ideas
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After mastering the necessary skills needed for data scraping, exploratory analysis, and data visualization, you can look forward to improving your data analyzing abilities further. You can start by taking up machine learning projects. Some of the projects include sentiment analysis, predictive analysis, and many more.
A vital element to take away from this post is that practice makes it perfect. So, try spending time on more straightforward projects at first to get comfortable with algorithms that are frequently used on datasets. Then, climb your way to taking up bugger projects that can help you grow in the industry.
If you are curious about learning data science to be in front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
What problems you might face while doing a Data mining project?
In addition to the broad range of project ideas, data analysts face a number of challenges while working on these projects. 1. One of the main issues you'll face when it comes to monitoring real-time environments is that there aren't many suitable solutions. You should familiarise yourself with the various technologies you'll need when working on a big data project. 2. One of the most common data analysis issues is how long it takes to process data after virtualization is completed. More commonly, latency issues occur because of high-level performance demands, and most of these tools require it. 3. Higher-level scripting may be required when continuing to work on big data analytics projects, particularly if you're encountering tools or problem situations that you haven't used before 4. Inadequate security leads to leaks of confidential data, which has disastrous consequences for both your project and your work. Of can happen, so you must always be cognizant of this. 5. End-to-end testing can't be done with just one tool. Make sure you determine which software will be required to accomplish a particular project. 6. Occasionally, you'll find a dataset too large for you to manage. Alternatively, you may need to validate more data to finish the project.
What are some Data Analysis Projects?
Some good data analysis projects are – 1. Classify 1994 Census Income Data. 2. Analyze Crime Rates in Chicago. 3. Health status prediction. 4. Anomaly detection in cloud servers. 5. Malicious user detection in Big Data collection. 6. Tourist behaviour analysis. 7. Credit Scoring. 8. Electricity price forecasting.
What are some good tools to manage big data?
To be successful in the Big Data industry, you must acquire these technologies. 1. The Apache Storm software is used for handling data streams in real-time. Java and Clojure are used, and integration with any computer language is possible. 2. MongoDB is indeed an open-source NoSQL database similar to modern databases. 3. Cassandra is used for managing massive quantities of data across several servers, with a distributed database management system. 4. In comparison to other Big Data technologies, Cloudera is among the fastest and most secure. 5. Refining data, converting it into different formats, and cleaning data are among the numerous applications for which OpenRefine is widely used.
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28 Data Analysis Projects to Boost Your Skills [2023 Guide]
In this article
What’s the Point of a Data Analysis Project?
Data analysis projects for beginners, intermediate data analysis projects, advanced data analysis projects, what skills should you focus on with your data analysis project, how to present and promote your data analytics projects, data analysis project faqs.
Data analytics projects showcase the analytics process, from finding data sources to cleaning and processing data. If you’re searching for your first data analysis job, projects allow you to gain experience using different data analytics tools and techniques. The best projects answer unexpected questions and explore relationships that aren’t immediately intuitive. In this post, we’ll tell you how to create data analytics projects that make you immediately hirable.
Doing data analysis projects is critical to landing a job, as they show hiring managers that you have the skills for the role. Professionals in this field must master a myriad of skills, from data cleaning and data visualization , as well as programming languages like SQL, R, and Python. A data analysis project can demonstrate your aptitude with all of these skills. Furthermore, personal projects are a great way to practice a variety of data analysis techniques , especially if you lack real-world experience.
Projects are an excellent way to gain experience with the end-to-end data analysis process, especially if you’re new to the field of data analysis. Here are some great project ideas for beginners:
Web scraping is the extraction of data—such as images, user reviews, or product descriptions—from web pages. This information is first collected, then formatted. Web scraping can be done by writing custom scripts in Python, or by using an API or web scraping tool such as ParseHub. Here are two popular ways to practice web scraping:
Reddit is a popular repository for web scraping because of the sheer amount of data available— from qualitative data in posts and comments to user metadata and engagement with each post.
Subreddits on Twitter enable you to extract posts on specific topics. PRAW is a Python package you can use to access Reddit’s API to scrape the subreddits you’re interested in (a Reddit account is required to get an API key). You can then extract data from one or more subreddits at a time. If you’d rather not scrape your own data, you can find Reddit datasets on data.world .
If you’re interested in real estate, you can use Python to scrape data on real-estate properties , then create a dashboard to analyze the “best” properties based on data points like property taxes, population, schools, and public transportation. There are two main Python libraries for data scraping: Scrapy and BeautifulSoup. You can also use the Zillow API to obtain real estate and mortgage data.
Exploratory Data Analysis
Another great project for beginners is to do an exploratory data analysis (EDA), which is the probing of a dataset to summarize its main characteristics. EDA helps determine which statistical techniques are appropriate for a given dataset. Here are some projects where you can work on your EDA chops:
McDonald’s Nutrition Facts
McDonald’s food items are often controversial because of their high fat and sodium content. Using this dataset from Kaggle, you can perform a nutrition analysis of every menu item, including salads, beverages, and desserts. First, import the CSV file in Python. Then, categorize items according to factors like sugar and fiber content. Then you can model the results using bar and pie charts, scatter plots, and heatmaps. For this project, you’ll need the Numpy, Pandas, and Seaborn libraries.
World Happiness Report
The World Happiness Report surveys happiness levels around the globe. This project , from a student at Pennsylvania State University, uses SQLite, a popular database engine, to analyze the difference in happiness levels between the North and South hemispheres.
Global Suicide Rates
While there are countless datasets concerning suicide rates, this dataset created by Siddarth Sudhakar contains data from the United Nations Development Program, the World Bank, Kaggle, and the World Health Organization. Import the data into Python and use the Pandas library to explore the data. From there, you can summarize the data features. For example, you can uncover the relationship between suicide rates and GDP per capita.
Visualizations communicate trends, outliers, and patterns in your data. So if you’re new to the field, and looking for a data analysis project, then creating visualizations is a great place to start. Select graphs that are ideal for the story you’re trying to tell. Bar charts and line charts succinctly illustrate changes over time, while pie charts model part-to-whole comparisons. Meanwhile, bar charts and histograms show the distribution of data. Here are some great data visualization projects for beginners:
Pollution in the United States
The Environmental Protection Agency releases annual data on air quality trends. This dataset from Kaggle features EPA pollution data from 2000–2016 in one CSV file. You can visualize this data using the Python Seaborn library or the OpenAir package in R. For example, you can model changes in emissions concentrations according to time, day of the week, or month. You can also use a heatmap to find the most polluted times of the year in a given area.
Data visualizations are a great way to illustrate historical events, such as the spread of the printing press or trends in coffee production and consumption . This visualization by Harvard Business School depicts the largest US companies in the year 1955. A second analysis in 2015 shows how much has changed. There is also an abundance of datasets available on World War II. This Kaggle dataset features data on weather conditions during the war, which had a major influence on the success of an invasion.
Modern telescopes and satellites produce digital images that are perfect for data visualization. This dataset from data.world shows future asteroids poised to pass near Earth within the next 12 months, as well as those that have made a close approach within the last 12 months. You can view live visualizations based on the dataset here to inspire your own analysis. You can also use this resource to find the asteroid orbital classes for each data point (eg: asteroid, apollo, centaur).
This project on KDNuggets makes use of Jupyter notebooks and IPython to analyze Instagram data. Regular Python works fine, but you may not be able to display the images in your notebook. You can use Instagram data to compare the popularity of two political candidates, like this project , or perform a time series analysis on a public figure’s popularity before and after a major event.
Sentiment analysis (AKA “opinion mining”) entails using natural language processing (NLP) to determine how people feel about a product, public figure, or political party, for example. Each input is assigned a sentiment score, which classifies it as positive, negative, or neutral. You’ll definitely want to hone this skill to land a job in data analysis. Here are some great projects to add to your portfolio:
Twitter Sentiment Analysis
Social media posts can be classified according to polarity or emotion-specific keywords. The Apache NiFi GetTwitter processor obtains real-time tweets and ingests them into a messaging queue so you can obtain posts about a trending topic or hashtag. Alternatively, use Twitter’s Recent Search Endpoint . Once you’ve generated your dataset, you can determine sentiment scores using Microsoft Azure’s Text Analytics Cognitive Service , which identifies key phrases and entities such as people, places, and organizations.
Audience Reviews on Google
Google reviews are a great resource for customer feedback, and also make for a great data analysis project. The Google My Business API lets you extract reviews and work with location data. In this project on Medium, data enthusiast Nikita Bhole used Python to perform a sentiment analysis on user reviews from the Google Playstore. She then used Pandas profiling to perform an exploratory data analysis to find variables, interactions, correlations, and missing values. Next, she used TextBlob to calculate a sentiment score based on sentiment polarity and subjectivity.
Quora Question Pairing
Quora is one of the most popular question-and-answer websites in the world, making it ripe for data analysis. In a recent Kaggle challenge , users were tasked with using advanced NLP to classify duplicate question pairs. For example, the queries “What is the most populous state in the USA?” and “Which state in the United States has the most people?” should not exist separately on Quora. This dataset from Quora contains over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line contains a duplicate pair. In this project conducted by a group of NYU students, a basic linear model known as an n-gram was used to build a set of features to be used in a natural language understanding (NLU) model. Then they used scikit’s Support Vector Machine (SVM) implementation module for their experiments with word embedding.
Data cleaning is the process of fixing or removing incorrect, corrupted, duplicate, or incomplete data within a dataset. Messy data leads to unreliable outcomes. Cleaning data is an essential part of data analysis, and demonstrating your data cleaning skills is key to landing a job. Here are some projects to test out your data cleaning skills:
Airbnb Open Data (New York)
Airbnb’s open API lets you extract data on Airbnb stays from the company’s website. Alternatively, you can use this existing Kaggle dataset for Airbnb stays in New York City in 2019. Both data files include all the information needed to find out more about hosts and geographical availability, both of which are necessary metrics to make predictions and draw conclusions.
YouTube Videos Statistics
The top trending videos on YouTube provide an itinerant window into the current cultural zeitgeist. This dataset from Kaggle contains several months of data on daily trending YouTube videos from different countries. This includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count. Once cleaned, you could use this data for:
- Sentiment analysis
- Categorizing YouTube videos based on their comments and statistics.
- Analyzing what factors affect how popular a YouTube video will be
- Statistical analysis over time
This project , from the book Data Science in Education Using R , analyzes this dataset compilation from the US Department of Education Website to uncover federal data on students with disabilities. You can prepare the data for analysis by cleaning the variable names. Then, you can explore the dataset by visualizing student demographics.
If you’re at the intermediate level and want to advance your data analysis career, you’ll want to improve your skills in data mining, data science, data collection, data cleaning, and data visualization. Here are some great projects to add to your portfolio:
Data Mining and Data Science
Data mining is the process of turning raw data into useful information. Here are some data mining projects that you can do to advance your career as a data analyst :
Speech recognition programs identify spoken words and convert them into text. To do this in Python, install a speech recognition package such as Apiai , SpeechRecognition , or Watson-developer-cloud . This project , which is called DeepSpeech, is an open-source speech-to-text engine using Google’s TensorFlow.
Anime Recommendation System
While streaming recommendation engines are useful, why not build a recommendation engine for a niche genre? This crowd-sourced dataset from Kaggle contains information on user preference data from 73,516 users on 12,294 anime shows. You can categorize similar shows based on reviews, characters, and synopses to build different recommendation algorithms.
A chatbot uses speech recognition to understand text inputs (chat messages) and generate responses. You can build a chatbot using the Natural Language Toolkit (NLTK) library in Python. Chatterbot is an open-source machine learning dialog engine on Github that lets anyone contribute dialog. Each time a user enters a statement, the library saves the text they entered. As Chatterbot receives more input, it learns to provide more varied responses with increasing accuracy.
Data Collection, Cleaning, and Visualization
Data collection is the process of gathering, measuring, and analyzing data from a variety of sources to answer questions, solve business problems, and investigate hypotheses. An effective data analysis project shows proficiency in all stages of the data analysis process, from identifying data sources to visualizing data. Here’s a project to advance your data collection, cleaning, and visualization skills:
Apple Watch Workout Analysis
The Apple Watch collects different types of workout data, including total calories burned, distance (for walking and running), average heart rate, and average pace. Using processed data, you can create visualizations such as rolling mean step count or step counts by days of the week, as seen in this project by full-stack engineer Mark Koester.
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Ready for a more senior-level data analysis position? Here are some projects you can add to your portfolio:
Machine learning enables computers to continuously make predictions based on the available data without being explicitly programmed to do so. These algorithms use historical data as input to predict new output values. Here are some common machine learning projects you can try out:
Machine learning uses models for fraud detection that continuously learn to detect new threats. This project for credit card fraud detection uses Amazon SageMaker to train supervised and unsupervised machine learning models, which are then deployed using Amazon SageMaker-managed endpoints.
Movie Recommendation System
Recommendation engines use data from user preferences and browsing history. To build a movie recommender, you can use this dataset from MovieLens, which contains 105,339 ratings applied to over 103,000 movies. Follow each step in more detail here.
Wine Quality Prediction
Wine classifiers make recommendations based on the chemical qualities of wine, such as density or acidity. This project on Kaggle uses the following three classifier models to predict the quality of wine:
- Random Forest Classifier
- Stochastic Gradient Descent Classifier
- Support Vector Classifier (SVC)
Pandas is also a useful library for this type of data analysis, while Numpy is good for working with arrays. Finally, you can use Seaborn and Matplotlib to visualize the data.
To build a Netflix-inspired recommendation engine, create an algorithm that uses item-based collaborative filtering which establishes similarities between products based on user ratings. This project establishes filtering capabilities across IMDB ratings, metatags, actors, genre, language, year of release, and so on. To generate your own dataset, you can download publicly available subsets of IMDb data.
Natural Language Processing
Natural language processing (NLP) is a branch of AI that helps computers interpret and manipulate natural language in the form of text and audio. Try adding some of these NLP projects to your portfolio to land a more senior-level position:
You can build a web application that translates news from one language to another using Python. In this project , data scientist Abubakar Abid used the Newspaper3k , a Python library that lets you scrape almost any news site. Then, he used the HuggingFaceTransformers , a state-of-the-art natural language model, to translate and summarize news articles from English to Arabic (you can choose another target language if desired). Finally, Abid deployed the Gradio library to build a web-based demo where he tried out the algorithm on different topics.
Autocomplete and Autocorrect
You can build a neural network in Python to autocomplete sentences and detect grammatical errors. This project on Github uses an LSTM model to autocomplete Python code to reduce the number of keystrokes required to write code. The model is trained after tokenizing Python code, which is more efficient than character-level prediction with byte-pair encoding.
Deep learning is concerned with neural networks comprising three or more layers. These artificial neural networks are inspired by the structure and function of the human brain. Practice your deep learning skills with these projects:
Breast Cancer Classification
Breast cancer classification is a binary classification problem that works by categorizing biopsy photographs as benign or malignant. This project uses a convolutional neural network (CNN) to identify high-level features in the input images and implement matrix computations to infer a feature map.
Image classification models can be trained to recognize specific objects or features. You can build one using a CNN in Keras with Python. This project uses the CIFAR-10 dataset, a popular computer vision dataset consisting of 60,000 images with 10 different classes. The dataset is already available in the datasets module of Keras, so you can directly import it from keras.datasets.
Gender and Age Detection
An advanced Python project, this model uses OpenCV and a CNN with three convolutional layers to guess the gender and age of a person in an image using the Adience dataset.
Regardless of your level or skillset, data analysts can always improve on the following skills:
SQL is mainly used for storing and retrieving data from databases, writing queries, and modifying the schema (structure) of a database system. In your data analysis project, be sure to make use of some of the most important SQL commands, such as SELECT, DELETE, CREATE DATABASE, INSERT INTO, ALTER DATABASE, CREATE TABLE, and CREATE INDEX.
While data analysts don’t need to have advanced coding skills, the ability to program in R or Python lets you use more advanced data science techniques such as machine learning and natural language processing.
Data Cleaning Skills
Data cleaning is the process of preparing data for analysis by removing or modifying data that is incomplete, duplicated, incorrect, or improperly formatted. Fixing spelling and syntax errors, standardizing naming conventions, and correcting mistakes are key skills.
As a data analyst, it’s important to communicate your findings with strong visuals that appeal to both technical and non-technical stakeholders. To visualize your data effectively, you need to know the specific use cases for each type of visual, from bar charts to histograms and more.
Data analysts use Excel and other spreadsheet tools to sort, filter, and clean their data. Excel is also a useful tool for doing simple calculations (eg: SUMIF and AVERAGEIF) or combining data using VLOOKUP.
Related Read: 65 Excel Interview Questions for Data Analysts
Familiarity With Machine Learning, AI, and Natural Language Processing
Data analysts with machine learning skills are incredibly valuable, even though machine learning is not an expected skill for most data analyst jobs. While data analytics is primarily concerned with data modeling and applied statistics, machine learning algorithms go a step further in obtaining insights and predicting future trends.
A good data analytics portfolio showcases your abilities. Each project should articulate the value of the data product or model you’ve built. Describe the technical challenge and how you overcame it successfully, what tools you leveraged and why, and explain your findings using well-chosen visuals.
Your portfolio should feature a diverse collection of projects, including exploratory data analysis projects, a data cleaning project, a project that uses SQL, and data visualization projects. Promote your projects by uploading them on Github. If you use Tableau for data visualization, set your project to ‘Public’ so that it is searchable online by potential employers.
Can You Include Your Projects on Your Resume?
If you lack real-world experience, projects are a great way to show off your skills. List each project the way you would a job. Briefly describe the scope of the project, the technical challenges you faced, and the outcome.
How Long Do Data Analysis Projects Take To Complete?
Projects can take anywhere from one or two weeks to several months to complete. It depends on the size and complexity of your dataset, processing time, how much data cleaning is required, and whether or not you decide to use machine learning and AI.
What Do You Learn From Data Analysis Projects?
Personal projects provide the opportunity to experience the end-to-end data analysis process, from EDA to data visualization. Projects also give you a chance to generate your own datasets, frame problem statements, and choose the right visuals to illustrate your findings.
Since you’re here… Interested in a career in data analytics? You will be after scanning this data analytics salary guide . When you’re serious about getting a job, look into our 40-hour Intro to Data Analytics Course for total beginners, or our mentor-led Data Analytics Bootcamp —there’s a job guarantee.
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Our Best Picks Of Capstone Project Ideas for Information Technology
Updated 01 Feb 2023
A capstone project, capstone experience, senior exhibition, or culminating project is an essential academic assignment for students. This culminating experience is meant for undergraduates in their final year at their school or academic program.
These projects come in many different forms, but they all require a long-term investment before the final presentation. Selecting a topic can be difficult, especially when looking for information technology ones. It can be explained by the fact that this branch of science is developing faster than ever (and then the majority of others do). There are a lot of capstone project ideas for information technology you can cover, but which one is a nice choice? In order to help you with the process, we’ve decided to give you some titles for inspiration. Looking to write your paper? Our essay writers are here to help!
How to Choose an Engaging Topic for Your IT Project
Capstone projects are very important for students. They help students develop critical thinking, learn how to solve problems, improve their communication skills, research, teamwork, presentation skills, etc. It also builds up the student’s resume and demonstrates learning.
This is why capstone projects are so important. But choosing the right idea for your project can be challenging, mainly because it holds such value. At the same time, it will be your focus for a long time. Here are some tips for choosing IT capstone project ideas.
Take the time to brainstorm information technology capstone project ideas
Now that you’re getting ready to start your information technology capstone project, you’ve probably finished most of your other obligations. The first thing you need to think about is all the information technology classes and topics that were interesting to you.
Did you want to learn more about specific topics? Were there some topics you learned quickly? Try to remind yourself of exciting topics and go through your notes to see if you haven’t missed anything. Shortlist all of the potential topics.
Go through potential topics!
Once you’ve finished your list, you need to see which ones have potential. See what is discussed in those topics, the latest research, something related to them, etc. See if you can find information that looks promising.
Are there any subjects within those topics that spark controversy or leave room for discussion? At the same time, remove all the topics that are limited or are above your knowledge.
Narrow your topic down
If you are interested in data mining, you shouldn’t try to cover the whole topic from the start until now. Instead of that, you should focus on a single area or aspect of data mining that seems interesting to you. For example, you can talk about the latest trends in predictive analytics.
120 Capstone Project Ideas for Information Technology
Use one of these information technology capstone project examples as your topic or inspiration. Get affordable and high quality legal essay writing service here.
- Home Surveillance and Automation
- iPhone SMS Notification Systems
- Using GSM Technologies for Detecting Theft
- POS Apps and Their Use
- Business Use Cases for Accounting Apps
- Time Tracking Solutions for Office Productivity
- SMS Doorbell Notification Tools
- Wireless Technologies for Surveillance
- Online Learning Systems and Their Relevance
- Online Apps For Business Management
- Improving Nursing Education With a Healthcare System
- Project Management Tracking Systems
- Biometric Security Systems
- Most Important Tools For Managing Data Security
- Fleet Management Software
- How The Internet Works: The Basics
- Introduction to Data Mining
- Emergency Vehicle Notification Systems
- Benefits of Data Mining
- What are Data Breaches and How to Prevent Them
- Improving Data Security
- Benefits of Car Transportation Systems
- Ecommerce Pricing Control Systems
- Apps for Monitoring Temperature
- Mobile Event Planners
- Alarm SMS Notification Systems
- Mobile Learning: How it Works
- Project Management Systems
- Machine Learning and Big Data
- University Networks
- ARPANET: How The Internet Started
- Latest Ecommerce Technologies
- Cre Principles of Cybersecurity
- Information Technology in the 21st century
- Java Programming: The Basics
- Software Testing 101
- Introduction to Software Quality Assurance
- Employee Productivity Apps
- Onboarding Systems and HR
- Cloud-Based Software vs. On-Site Solutions
- Benefits of Cloud Solutions
- Hotel PMS Systems
- Smart Technology in the Hotel Industry
- IoT and Data
- New IoT Gadgets
- What is Artificial Intelligence
- What is Machine Learning
- Robotics Systems
- Neural Networks
- How Has Technology Changed Our Lives
- Censorship on the Internet
- Should the Internet be Free?
- Email Marketing Tools
- Web Scrapers
- How Machine Learning Learns
- How is AI Affecting us Today
- New Tech Innovations for the Near Future
- What is Virtual Reality
- How are Clouds Technologies Used for Data Storage
- Mobile Apps That Improve Education
- How The Internet Will Develop
- Software as a Service
- Platform as a Service
- Infrastructure as a Service
- SaaS, PaaS, & IaaS: Main Differences
- How Google Ranks Websites
- 3D Visualization in Architecture
- Essential Skills 3D Artists Need
- 3D Rendering Tools
- DMaaS - Data Mining as a Service
- Augmented Reality
- VR vs. AR: Differences Explained
- Automation Systems in the 21st century
- Data Management for Big Companies
- Essential Data Analyst Skills
- AI and Gamification
- Data Mining for Employee Evaluation
- Data Mining in Healthcare
- Machine Learning for OTAs
- Facebook and Data
- Censorship on Social Media
- How Social Media Filters out Content
- Fingerprint Authentication
- IoT Sensors
- Web Ordering Systems
- How to Prevent Phishing Attacks
- Using Web Scraping for Research
- Web Proxies
- Virtual Private Networks
- Differences Between VPNs and Proxies
- How Proxies Enhances Web Scraping
- Home Automation Systems
- Smart Homes
- Warehouse Management Systems
- How Voice Recognition Systems Work
- QR Code Check-ins
- Smart Systems in Cars
- Should Cars Have More Computers or Less?
- Solar Grass Cutters With Automation
- Voice Controlled Tools
- CRM Software
- Online Training Software Benefits
- ChatBots: Introduction
- Complex Chat Bots
- Using ML for Cybersecurity
- Machine LEarning in Ecommerce
- Elearning and Gamification
- How Games Have Improved Digital Technologies
- How Far Have Video Games Gone?
- Home Voice Assistants
- How Technology Helps Disabled People
- Apps for Child Monitoring
- Nutrition Tools
- Semantic Web & Big Data
- Autonomous Systems Today
- Is The Internet Helping us?
- Blockchain and Crypto
- How Bitcoin Works
- Edge Computing Explained
We hope these capstone project topics have inspired you to develop your own perfect topic.
Steps for Writing a Winning IT Capstone Project
Submit your project proposal..
When developing your custom capstone project for information technology list, consider including these points in your proposal:
- Your topic and motivation behind writing
- Your background about the subject
- Outline resources for the topic
- How do you plan on executing the paper
- Talk about your goals
Get all the necessary research.
Prepare everything you might need for the topic, be it computer science capstone project ideas or IT. Find all the sources, including textbooks, titles, literature, online resources, links, and so on.
Create a structure.
With a clear structure, you will organize your information correctly and have a good flow. The most common elements included in the structure are titles, introduction, literature review, references, methodology, discussions, recommendations, and conclusion.
The main focus of your project should be the thesis statement. Leave the introduction for the end, and make sure to double-check your research and your conclusions. In the end, proofread the text and try to improve your sections.
Get ready to defend your project.
Read your work multiple times, check out the research, and be prepared to defend all the conclusions and statements you’ve made.
Are You Having Trouble With Your IT Capstone Project Ideas?
It’s 2022, and you don’t have to torture yourself when you can get professional capstone project help . Our writers at EduBirdie can help you brainstorm topics and give you valuable advice on how to approach your project. Contact us today!
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Excel will be used for the data cleaning process and for removing errors from data. R programming language will be used for analysis and visualization.
Note: SQL, Tableau can be used for data manipulation and data visualization here as well, but using R all functions were performed.
Bellabeat is a high-tech manufacturer of health-focused products. As a junior data analyst working with marketing analyst team at Bellabeat, I have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. I have performed analysis on data to give recommendations.
The data set is Fitbit Fitness Tracker Data taken from Kaggle which contains personal fitness trackers from thirty Fitbit users. It contains 18 CSV files
What are some trends in smart device usage?
How could these trends apply to Bellabeat customers?
How could these trends help influence Bellabeat’s marketing strategy?
• Urška Sršen: Bellabeat’s co-founder and Chief Creative Officer
• Sando Mur: Mathematician and Bellabeat’s cofounder; a key member of Bellabeat’s executive team
• Bellabeat’s marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Bellabeat’s marketing strategy.
FitBit Fitness Tracker Data (CC0: Public Domain, data set made available through Mobius)
There is no demographic data about users, hence women data cannot be extracted. Overall, data is 6 years old and would not be helpful in a fast moving technology market.
• We can see higher weight people(above 120kg) are more sedentary. so we should target specifically below 65kg but between 90 and 120kg people are very or fairly active - seems like high weight people are trying to lose weight and exercise more than normal people but they have less (very active distance) which means they run/jog less and are using indoor activities to stay active such as gym
• less than 70kg as they are active but won’t be willing to pay a lot because they are not passionate, they have more active distance though meaning they run/walk more. however, between 90 and 120kg people are passionate and would be willing to spend more money
• People are most active in the start of month and middle of month while data only collected for April and May.
• Step total decreases with weight, above 100kg steps decline. People who take more calories have more steps there is a linear relationship, while active people sleep less than 400 minutes and people between 80 and 100kg take most calories.
• Bellabeat’s marketing team can encourage users by educating and equipping them with knowledge about fitness benefits, suggest different types of exercises, calories intake and burn rate information on Bellabeat’s application.
• Most people use fitbit to track steps and calories burned, people don’t use to track sleep much. I will suggest focusing on steps, calories more than sleep in application
• The relation between steps taken vs calories burned and very active minutes vs calories burned shows positive correlation. So, this can be a good marketing strategy.
• If users want to lose weight, it’s probably a good idea to control daily calorie consumption. Bellabeat’s can suggest some ideas for low-calorie lunch and dinner.
• The Bellabeat app can recommend reducing sedentary time.
In their final semester of the UW Data Science program, students are required to take DS 785 , the capstone course. Below are example capstone projects to give you an idea of the types of opportunities available to our students.
Using Mock Draft Data to Create a Player Availability Dashboard for the NFL Draft
A Practical Data Science Application: Developing Prediction Models for Product Inventory Reduction and Ongoing Monitoring to Create Efficiency
An In-Depth Review Customer Segmentation, Recommendation Systems, and the Benefits of Combined Use
Time-series forecasting of maple tree sap harvesting.
Comparative Study on Employee Turnover
The Development of Feed Type Classification Algorithms for a Commercial Testing Laboratory
Daily Driving Route Optimization for Small Businesses Using Metaheuristics
Cost Analysis of a Local Union’s Digital Transformation
Examining and predicting the university of wisconsin’s system library ebook usage.
Advertisement campaign targeting attributes recommendation engine
Qlik Application Creation for Deeper Analysis of Department of Defense Budget
Exploring Rural Road Crash Data with Statistical Models
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Master of Science in Analytics
- Capstone Projects
As a culminating experience, Master of Science in Analytics (MScA) students put into practice the knowledge and skills they have learned during their coursework by completing a capstone project. The project is a degree requirement and is completed during the last two quarters of their program. It provides students and project sponsors the opportunity to develop and implement a data science solution to address problems the organization is trying to solve, enhance their analytics capabilities, and explore potential employment partnerships. There is no cost associated with sponsoring a project.
The project spans two 10-week quarters. Students may begin their Capstone experience in any quarter, with most students starting in the Spring quarter. The expectation is that students will work in teams of four.
MScA Capstone Project Learning Objectives
- Develop the ability to design an analytics research project
- Frame a business problem in a way that can be addressed using data science
- Identify the analytics tool or algorithm that will address this problem
- Develop a methodological framework to produce a practical solution
- Implement the analytics methodology
- Communicate the findings of the research effectively in both written and oral presentations
Capstone Sponsor Incentives
- Derive benefits from a unique capstone process for data science
- Your business problems will be addressed by student teams trained to generate not just actionable insights from data, but to provide business value
- Get access to a symbiotic pool of top student talent with world class teachers and researchers
- Talent growth and acquisition opportunities for your businesses
Interested in Sponsoring a Capstone Project?
Get in touch with us to submit your idea for a collaboration or ask us questions about how the partnership process works.
Selected Capstone Projects
Copd readmission and cost reduction assessment.
UChicago Analytics students built data models and evaluated them across different frameworks. They determined that the resulting model is capable of rank-ordering readmission risk and allowing for flexibility in applying interventions to prevent readmission.
An NFL Ticket Pricing Study: Optimizing Revenue Using Variable And Dynamic Pricing Methods
UChicago Analytics students found a way for an NFL team to implement ticket pricing that responds to changing factors and gives the team the chance to fill more seats.
Using Image Recognition To Identify Yoga Poses
Master of Science in Analytics students built an app that uses a one-step neural network to examine images of yoga poses and recognize the poses in order to provide feedback to the app's yoga-practicing user.
Using Image Recognition to Measure the Speed of a Pitch
One capstone team developed an app that applied image recognition algorithms to measure the speed of a pitched baseball. Their app captured video, isolated the pitched ball, calculated the velocity of the pitch, and displayed this measurement so that users would be able to measure the speed of a pitch with their smartphones.
Real-Time Credit Card Fraud Detection
Credit card fraud puts consumers' identities at risk while credit card providers are forced to cover fraudulent charges. A team of analytics students carefully studied this problem: they created synthetic data that represented a large population of credit card users and were able to build a model that catches credit card fraud in real time.
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8 Awesome Data Science Capstone Projects from Praxis Business School
It is not the strongest or the most intelligent who will survive but those who can best manage change.
Evolution is the only way anything can survive in this universe. And when it comes to industry relevant education in a fast evolving domain like Machine Learning and Artificial Intelligence – it is necessary to evolve or you will simply perish (over time).
I have personally experienced this first hand while building Analytics Vidhya. It still amazes me to see where we started and where we are today. During this period, there have been several ups and downs, several product launches, product re-launches and what not! But one thing has been a constant in our story – constant evolution!
So, when I got an invite to be a judge on the panel judging Capstone projects done by students of PGP in Data Science with ML & AI program at Praxis Business School, the same school where I had reviewed the program almost 4 years back – I was curious. I was curious to see and learn how their evolution had panned out.
My interaction with the students four years ago was quite different from my experience sitting in a panel of judges for Capstone projects. You get to see the final outcome coming from a rigorous program as opposed to just having a classroom interaction. This is like the proof of the pudding!
I was hoping to find out answers to 2 broad questions in the process:
- How has the program evolved over the years?
- What kind of projects are students currently doing and how industry relevant were they?
With those questions in mind – I boarded an early morning flight to Bengaluru and was in the Praxis campus by 9:00 a.m. Since the evaluations were supposed to start at 10:30 a.m., I had some time on my hand.
I used this time to catch up with the course faculty Gourab Nath , and other judges of our esteemed panel – Suresh Bommu (Advanced Analytics Practice Head at Wipro Limited) and Rudrani Ghosh (Director at American Express Merchant Recommender and Signal Processing team).
I also grabbed some authentic South Indian breakfast in the process. 🙂
Program Details and Capstone Projects
For people who are not aware – Praxis Business School offers a year-long program – PGP in Data Science with ML & AI at both its campuses – Kolkata and Bengaluru. The program is structured in a manner where the first 9 months are spent in the classroom with in-house and industry faculty and the last 3 months are spent as an intern with an industry partner.
The Capstone project happens before the internship actually starts. So, students spent a total of 9 months in the classroom and had been doing these projects for the last 3 months (month 6 – month 9 in the curriculum).
How has the Program Evolved over the Years?
The last time I had visited Praxis was in 2015 and I was dead sure that the program would have evolved. The question was how much? In which direction? What are the key takeaways for the students and how are the students from Praxis doing in the real world?
So, let me share my findings based on the interaction with Gourab and the rest of the panel.
How Much has the Program Evolved? In which Direction?
The first noticeable change was the name of the program itself. Back in 2015, the Program was called PGP in Business Analytics as most of the material in the course was related to Business Analytics and Statistical Modelling.
Over time, the program has evolved a lot – I was surprised to see the number of topics that are covered in the program. Here is a screenshot of topics covered in the curriculum, picked directly from their site:
The program has clearly evolved a lot. It not only includes Machine Learning and Deep Learning, but also Big Data Tools and Business-Focused topics. As far as I can see – the program has evolved a lot and has become a comprehensive course for data scientists.
What are the key takeaways for the students undergoing the program?
I think the best way to judge this is to look at the projects. So – I held this off and the projects were sufficient proof by themselves.
Needless to say, I was pretty excited by these discussions and with the context of this evolution – I was ready for what the rest of the day was supposed to be.
Here are the views of Gourab Nath, part of the judging panel and Assistant Professor of Praxis’ Data Science Program:
Collection of images is a challenging task for projects that involves topics like face recognition. Previously we were using an approach which was a little time-consuming. So, this time we decided to take a more systematic approach to collect the images that can massively same time of our participants. The teams working on such projects designed and developed an easy-to-handle application for facial image collection. A participant was requested to sit in front of the computer where we had the software running and all he/she needed to do was to enter his/her names and press a capture button to start the image collection process.
The students at Praxis Business School are highly encouraged not to be hugely dependent on the tools and the packages and focus more on writing algorithms. This approach helps them to code better no matter what programming languages they use.
Capstone Projects by Current Passing out batch at Praxis Business School
A glance at the list of projects confirmed my views until now. I could see projects on Machine Learning, Natural Language Processing (NLP) and Computer Vision (CV).
More importantly – it looked like these projects were not based on some open datasets. The problems mentioned were unique and I was not aware of many open datasets addressing these problems. Now, I was curious and excited to see what students have and how they have done.
Here’s the list of Capstone Projects done by students at Praxis Business School:
- Detection of Spam Reviews
- Opinion Mining on Mobile Phone Features
- Drowsiness Detection using Computer Vision
- Gesture Recognition using Computer Vision
- Team Selection using Computer Vision
- Attendance Tracking System using Computer Vision
- Recommender System for Fashion Apparel
- Nearest Document Search
Just to put things in perspective – most of the students presenting to us did not have any knowledge of predictive modeling and machine learning till July 2018 – when they started with the program.
Details of the Capstone Projects
Let’s look at each capstone project in a bit more detail to understand what it was about plus the tools and techniques used in each project.
Project 1 – Detection of Spam Reviews
Customer reviews have a huge influence on potential buyers of any product. A number of false reviews may drive the influence either in a positive direction or a negative direction. Any of these cases may make the customers take wrong decisions and the trustworthiness of the online opinions could be an issue.
In this project, we investigate opinion spam in reviews.
Note that this problem is different from email spam classification. Email spam usually refers to unsolicited commercial advertisements to attract people towards some products or services and hence they usually contain some prominent features.
Our specific problem is more challenging because untruthful opinion spam is much harder to deal with. These kinds of spamming material can be carefully crafted and made indistinguishable.
Techniques: Shingle Method, n-grams, Feature Extraction
Project 2 – Opinion Mining on Mobile Phone Features
You open amazon.com and find that lots of customers have given great reviews about a well-branded mobile phone you are interested in. You wonder – are these good reviews due to the camera of the phone? Or, how good is the battery of the phone? And what about the display?
While the number of reviews is really large and its almost impractical for the readers to go through all of them for evaluating the product, answers to these kinds of questions can be really helpful in making useful decisions.
In this project, our focus is to identify various features of a mobile phone that the customers are talking about in their reviews and mine the customers’ opinion on these features.
Further, we focus on identifying the polarity of these opinions and summarize the reviews. Finally, we develop a user-interface that summarizes the opinions about the features of the phone and rank the customer reviews based on its utility. We also propose an architecture that can perform the same on the reviews of any mobile phones.
Tools: Python [Packages: NLTK, SpaCy, sklearn], Wix.com (for the website creation)
Techniques : Fuzzy Matching, POS tagging, Association Rules Mining, Compactness Pruning, Redundancy Pruning, identifying sentiments based on the word list and weights in AFINN and WordNet
Check out a demonstration of this project below:
Project 3 – Drowsiness Detection using Computer Vision
How many times has this happened to you – you started a movie on your computer at night and fell asleep in the middle of it? And when you woke up the next day, you simply have no clue about how far you watched it? Happens to the best of us.
In this project, we focus on developing an application that will be able to detect if you are asleep and automatically pause the video for you. The system waits to see if you wake up in the next 30 minutes. In case you don’t, it will save a snapshot of the screen, close all the windows and shut down your computer automatically.
Tool: Python, Open CV, Tensorflow, Keras
Techniques: Viola-Jones algorithm on Rapid Object Detection using a Boosted Cascade of Simple Features, Inception V3, LSTM
Project 4 – Gesture Recognition using Computer Vision
Picture this – you are watching a video on your computer but are feeling way too lazy to use the mouse or the keyboard to control the video player. Sounds familiar?
We have a solution for you!
In this project, we focus on making the computer recognize some special gestures which will enable one to control a video player by just using those gestures.
For example, showing your palm in front of the system will enable the pause and the un-pause function. You will also be able to control the volume, fast forward a video or rewind it. You will also be able to do a wide range of other things like changing the slides of your PPT, changing pages, scrolling, etc. without grabbing your mouse or keyboard.
Techniques: Green Screen (for background subtraction), Single-Shot Multi-box Detector (SSD)
Project 5 – Team Selection using Computer Vision
Students are asked to create teams for their projects or their assignments, which is of course a very common thing in every school and college. The class representative (CR) creates a Google spreadsheet and shares it with everyone.
Students, after deciding who they want to team up with, populate the spreadsheet with the names of their team members. But the CR must remember the rules given by their Professor – the team size should be three and every team must have one female member at least.
So, the CR checks the restrictions and if everything is fine, he/she shares it with the Professor. This is one way to do it.
Or, you can do it the smart way.
You stand with your teams in front of the computer, the computer checks the restrictions, recognizes you, and fills in the database with your names and photos.
But remember, the computer won’t allow you to register if the constraints are not satisfied or when at least one of the members in your team is already registered as members of any other team. So, you cannot fool it!
Techniques: VGG-NET 19, HOG Detector
Project 6 – Attendance Tracking System using Computer Vision
In this project, we developed a system to record class attendance using computer vision.
After a faculty enters the system using a password and sets the period, the camera opens up to capture the picture of the class. The number of snapshots of the class is first passed through a face detector followed by a face recognizer.
After the system recognizes the students, it updates the attendance spreadsheet and saves the captured image in its respective image directory – labeling it by the date and time of the day. The unidentified students are marked as absent.
Techniques: Haar Cascade Classifier, HOG, Siamese Model (One Shot Learning), kNN
Project 7 – Recommender System for Fashion Apparel
The use of a recommender system in e-commerce companies is a highly targeted approach that can generate a high conversion rate. These systems help customers discover the products which they might be interested in and will likely purchase.
In this project, we have created a recommender system for a small fashion apparel industry that: Allows the customers to search by the image of a product Gives a personalized recommendation to the heavy buyers, and Displays the most frequently purchased item for the selected item
Techniques: kNN, Collaborative Filtering, Content-Based Filtering, Autoencoders
Here’s a demo video of this project:
Project 8 – Nearest Document Search
In this project, we have created a nearest document search engine for News reading. The application will not just recommend you related news but also give you the sentiment and highlight important words associated with the news. If the news is big and you do not want to read the full news, fair enough, this app will have a summarized version ready for you.
Techniques: kNN, KDTree, Word Cloud, Lex Rank Summarizer
How relevant were these projects for the Industry?
One of the most critical questions I had was – are these projects industry relevant? Bridging the gap between academia and industry has been a significant challenge in data science. It turns out the answer is quite comprehensive.
In the last 4 years, the number of companies hiring has increased 4 times (from 15 in 2015 to 60 in 2018-19) and the average salary has doubled (5LPA in 2015 to 9LPA in 2018-19).
So, here are the thoughts of my fellow panelists on this topic:
“I am very impressed on the scope, objectives, and contents of the capstone projects executed by Praxis students. The majority of the projects are around the application of deep learning concepts which they have learned as a part of the course work. The entire project execution and development activities were well planned and organized. Starting from defining the problem statement, challenges, real-time application and finally presenting the results.” – Suresh Bommu, Advanced Analytics Practice Head at Wipro Limited
“What really stood out for me was the effort put in by students in attempting to create an end-to-end product with a UI as well as the variety of projects and its extended application.” – Rudrani Ghosh, Director at American Express Merchant Recommender and Signal Processing team
Key Takeaways from the day
I loved the day and would live it again without second thoughts. But there were a few things which stood out for me:
- There was a stark difference in the projects which students were doing currently. In a period of 9 months, they have completed learning the subject and have completed a Capstone project. This would not have been possible without the efforts of students themselves and the faculty members.
- Most of these projects exposed students to the perils of design thinking, creating and collecting the dataset and cleaning it. I just loved this aspect. I am sure the students realised that building a deep learning model is far easier than actually collecting the data for it.
- I also loved the way students presented their projects. They created video teasers and demo sessions to bring out the work they had done.
It was great to see the high level of projects presented by these students. As I mentioned, I was glad to see the students picking up challenging problems on not openly available datasets.
At the end of the day, I had to rush back to the airport. Day trips to Bengaluru are bad! And the fact that I had to rush through projects for a few students only made it worse. I would have loved to spend more than a day – the Energy of the class, the faculty and the judges was infectious 🙂 Looking at these projects – I can confidently say that Praxis Business School continues to offer one of the best full time program in Machine Learning and Deep Learning in India.
About the Author
Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 10 years in field of Data Science. His work experience ranges from mature markets like UK to a developing market like India. During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python and Matlab.
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The MS Analytics p rogram c apstone is a career enhancer. S tudents utilize real data from their organization (or another) and partner with a project coach to build a predictive model. The individual project is completed over the five semester s. Graduates have earned significant raises because of the ir capstone projects . The Cohort of 2021 reported an average annual estimated value of their capstone of $18.2 M.
Examples of capstone projects submitted by students according to industry:
Social Impact Analytics
Capstone Project Frequently Asked Questions
1. Do I have to know what my capstone project will be when I apply?
We will begin discussing the capstone project in orientation, but you won’t have to submit an initial topic proposal until the end of the first semester.
2. Will my data be secure?
Your data never leaves you and is not stored on university servers. We recommend you anonymize, rescale, and sanitize your data. Your project coach can sign a non-disclosure agreement to further ensure confidentiality.
3. How large does my dataset have to be?
The answer really depends on your business problem/question. Our definition of big data is n=all. You will discuss data with your project coach to determine what variables are needed.
4. Will I be required to publish my project or present it?
Students typically do not publish their findings due to company confidentiality agreements. Peer presentations are made, but specific variables and findings do not have to be shared as we are interested in the process of modeling more than the specific outcomes.
5. Is the capstone project individual or group?
Our capstone project is an individual project specific to each student and his/her organization or interests. It is a unique feature of our program which is both time and resource intensive, but yields enormous return on investment.
6. What makes a good capstone project?
Think about what keeps your boss up at night? What is her/his (or your) bonus based on? Typically a project that is interesting to your employer makes a great project.
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Implementing Data Science Capstone Project Ideas Is What We’re Good at
Writing a nice-looking data science capstone is a number one task for those who deal with programming/software development and are interested in statistics and big data investigation. That is a paper that depicts what you can perform and what your potential is. If you possess a great interest in big data and artificial intelligence and additionally can boast of your superb skills, then it’ll be easier for you to cope with this project.
However, if you face some difficulties, it’s time to ask for experts’ help. You won’t regret asking our writers to craft a paper for you as we always masterfully present the achieved outcomes for the audience, whether it relates to data modeling, coding, or software engineering. Our main mission is to show your strong analytical skills together with profound performance in computer science, statistics, and mathematics.
When choosing a topic for your capstone data science paper, its initial background may refer to various directions or have any purposes and reasons. Some capstone ideas can be useful for making data-driven organizational decisions, coping with some business problems, or fixing and automating the workflow of a company. Thus, our impeccable writing experts will help you with the goals necessary to achieve and the useful dataset that you need to present to the audience to look decent.
So, let’s review what data science capstone project ideas our specialists can offer you:
- Sentiment analysis – you can detect what emotions, opinions, and feelings prevail regarding a particular service execution or general activity of a company. It looks like doing a survey to find out the way people estimate a person’s or corporate performance. Here we prefer using the R programming language and the ‘janeaustenr’ package.
- Chatbot creation – it’s a perfect idea for a project and is crucial for business workflow. Its great advantage is promoting business automation in interacting with clients and minimizing manpower usage, which saves money. You can create a domain-specific or open-domain chatbot, using the Python language, depending on the purposes you want to achieve.
- Image caption generator – it’s a useful option that simplifies the recognition of handwriting images just after scanning pictures.
Generally, there’s a vast number of worthy projects, but remember that it’s better to choose a topic that interests you and would bring some benefit to society or business. In any case, be sure our service will benefit your candidacy and will vouch for your successful graduation.
Let Us Use Great Approaches to Capstone Project Data Science Creation
When to start crafting a data science capstone project, it’s essential to think over well what topic and ideas to describe to get approval from the academic mentor and catch the examination board’s attention. However, it may be quite problematic and daunting to choose what data analysis to perform to represent your potential and look relevant at the same time.
If you’re doing such a project for the first time, fear not – our helpful capstone writers will do the necessary things for you:
- We’ll select a ‘comfortable’ topic for discussion and conduct a comprehensive analysis. Also, we’ll prepare all the needed information to execute statistics modeling and apply machine learning tools.
- Your writer will arrange the dataset in columns and rows to make it easy to look at and able to present clear statistics and meaningful correlation between the given numbers.
- Our editing team will remove unstructured and messy data. The initial data needs extensive featurization for applying predictive modeling.
- Using a systematic approach, we gather the info and remove unnecessary columns.
- We’ll explore the data and find solutions to the essential issues concerning the chosen topic.
By implementing such concepts, we’ll show your solid technical skills and prove you’re a great analytical thinker who can use various approaches and programming tools for data evaluation.
We’ll Provide Top Data Science Capstone Projects for Your Success
Nowadays data science is growing in its popularity, moreover, artificial intelligence innovations broaden its implementation in a great number of industries. So, a future data scientist has to be well-prepared and highly competitive to get a positive assessment of their skills and professionalism.
Therefore, our writing team pays great attention to your relevant skills that could bring some advantage when dealing with a capstone project data science to demonstrate your most winning properties, great command of numerous programming techniques, data management, and strong business intelligence as well.
While composing a capstone, we show your interest in this area, technological and programming expertise, and experience in the usage of Hive, AWS, Spark Tools, Python, R, or SAS. To represent the information properly and make it clear, we apply convincing analytical properties, strong technical background, and impressive data structuring skills.
Expert Support in Data Science Capstone Final Project Performing
If you like programming and computer science, possess enough skills and academic background to move further but have a weak understanding of doing a worthy data science capstone final project, feel free to ask our capstone writing experts for help.
We specialize in dataset structuring via programming, coding, and making fitting algorithms, so you’ll be assured of the top quality of any data science capstone projects you need to deal with. In any case, we strive to show your persuasion skills and traits that will bring you an advantage.
The intent of our Capstone project is to observe the effect of Twitter sentiment on Crypto prices. The coins of interest are Bitcoin, Ethereum, and Litecoin. The data for sentiment analysis is pulled from Twitter. The Crypto data is comprised of static files showing minute based Crypto price changes relative to the coins listed above.
We'll then share nine project ideas that will help you build your portfolio from scratch, focusing on three key areas: Data scraping, exploratory analysis, and data visualization. We'll cover: What should you include in your data analytics portfolio? Data scraping project ideas Exploratory data analysis project ideas
Capstones are standalone projects meant to integrate, synthesize, and demonstrate all your data science knowledge in a multi-faceted way. Capstone projects show your readiness for using...
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Below are some data analytics capstone projects that you can develop to gain data analytics skills. Handwritten digit recognition Fake news detection House price prediction using machine learning Data analytics using R Movie recommendation system Social media sentiment analysis Speech emotion recognition Gender age detection 7 Lawrence C.
The search for capstone project ideas on pharmacy implies developing common issues from the field. Pharmacists are medical employees who communicate with patients more often than others. The friendly attitude and opportunity to help a visitor make them work tirelessly.
All Projects — Analytics Capstone All Capstone Projects (2017-2021) AB-INBEV A Data-Driven Approach to Forecasting the U.S. Beer Industry Assortment Optimization Suggested Order Quantities ACCENTURE Natural Language Processing for Customer Experience Evaluation AIR CANADA Business Churn Projection and Prediction
View my full code on my Github for this capstone project here. Load all of the libraries I used: tidyverse, lubridate, hms, data.table Uploaded all of the original data from the data source divytrip into R using read_csv function to upload all individual csv files and save them in separate data frames.
Attractive Computer Science Capstone Project Ideas Computer science is so rapidly developing that you might easily get lost in the new trends in the sphere. Gaming and internet security, machine learning and computer forensics, artificial intelligence, and database development - you first have to settle down on something.
Some of the best categories for data analytics project ideas include: Python Analytics Projects - Python allows you to scrape interesting data, as well as perform analysis with pandas dataframes and SciPy libraries.
The course allocates 3-4 instructional team members per class to ensure dedicated peer and mentor support for your Data Analytics journey that includes data visualisation projects. You will graduate with an IBF-accredited Data Analytics certification, as well as a polished, portfolio-ready Capstone Project Ideas for Data Analytics to showcase ...
Exploratory Data Analysis Project Ideas 1. Global Suicide Scale The next step in improving your data scientist skills is to carry out exploratory data analysis on the data structure, patterns, and characteristics. For example, analyze the datasets that cover the numbers of suicide cases happening in different countries.
Here are some great project ideas for beginners: Web Scraping Web scraping is the extraction of data—such as images, user reviews, or product descriptions—from web pages. This information is first collected, then formatted. Web scraping can be done by writing custom scripts in Python, or by using an API or web scraping tool such as ParseHub.
Capstone projects are very important for students. They help students develop critical thinking, learn how to solve problems, improve their communication skills, research, teamwork, presentation skills, etc. It also builds up the student's resume and demonstrates learning. This is why capstone projects are so important.
Google-Capstone-Project. Excel will be used for the data cleaning process and for removing errors from data. R programming language will be used for analysis and visualization. Note: SQL, Tableau can be used for data manipulation and data visualization here as well, but using R all functions were performed. Bellabeat is a high-tech manufacturer ...
Below are example capstone projects to give you an idea of the types of opportunities available to our students. Search Filter Capstone Keyword Search: Clear Filters Hide All filters Capstone Statuses Prospective (1) Completed (89) Capstone Categories Healthcare (13) Hospitality (2) Finance (7) Marketing (6) Manufacturing (3) Transportation (5)
MScA Capstone Project Learning Objectives. Frame a business problem in a way that can be addressed using data science. Identify the analytics tool or algorithm that will address this problem. Develop a methodological framework to produce a practical solution. Communicate the findings of the research effectively in both written and oral ...
Here's the list of Capstone Projects done by students at Praxis Business School: Detection of Spam Reviews Opinion Mining on Mobile Phone Features Drowsiness Detection using Computer Vision Gesture Recognition using Computer Vision Team Selection using Computer Vision Attendance Tracking System using Computer Vision
The MS Analytics program capstone is a career enhancer. Students utilize real data from their organization (or another) and partner with a project coach to build a predictive model. The individual project is completed over the five semesters. Graduates have earned significant raises because of their capstone projects.
Some capstone ideas can be useful for making data-driven organizational decisions, coping with some business problems, or fixing and automating the workflow of a company. Thus, our impeccable writing experts will help you with the goals necessary to achieve and the useful dataset that you need to present to the audience to look decent.