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20 Solved End-to-End Big Data Projects with Source Code
Solved End-to-End Real World Mini Big Data Projects Ideas with Source Code For Beginners and Students to master big data tools like Hadoop and Spark. Last Updated: 14 Mar 2023
Ace your big data interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies. You will find several big data projects depending on your level of expertise- big data projects for students, big data projects for beginners, etc.
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Have you ever looked for sneakers on Amazon and seen advertisements for similar sneakers while searching the internet for the perfect cake recipe? Maybe you started using Instagram to search for some fitness videos, and now, Instagram keeps recommending videos from fitness influencers to you. And even if you’re not very active on social media, I’m sure you now and then check your phone before leaving the house to see what the traffic is like on your route to know how long it could take you to reach your destination. None of this would have been possible without the application of big data. We bring the top big data projects for 2021 that are specially curated for students, beginners, and anybody looking to get started with mastering data skills.
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A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. Before actually working on any big data projects, data engineers must acquire proficient knowledge in the relevant areas, such as deep learning, machine learning, data visualization, data analytics, etc.
Many platforms, like GitHub and ProjectPro, offer various big data projects for professionals at all skill levels- beginner, intermediate, and advanced. However, before moving on to a list of big data project ideas worth exploring and adding to your portfolio, let us first get a clear picture of what big data is and why everyone is interested in it.
Kicking off a big data analytics project is always the most challenging part. You always encounter questions like what are the project goals, how can you become familiar with the dataset, what challenges are you trying to address, what are the necessary skills for this project, what metrics will you use to evaluate your model, etc.
Well! The first crucial step to launching your project initiative is to have a solid project plan. To build a big data project, you should always adhere to a clearly defined workflow. Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation.
Understand the Business Goals
The first step of any good big data analytics project is understanding the business or industry that you are working on. Go out and speak with the individuals whose processes you aim to transform with data before you even consider analyzing the data. Establish a timeline and specific key performance indicators afterward. Although planning and procedures can appear tedious, they are a crucial step to launching your data initiative! A definite purpose of what you want to do with data must be identified, such as a specific question to be answered, a data product to be built, etc., to provide motivation, direction, and purpose.
The next step in a big data project is looking for data once you've established your goal. To create a successful data project, collect and integrate data from as many different sources as possible.
Here are some options for collecting data that you can utilize:
Connect to an existing database that is already public or access your private database.
Consider the APIs for all the tools your organization has been utilizing and the data they have gathered. You must put in some effort to set up those APIs so that you can use the email open and click statistics, the support request someone sent, etc.
There are plenty of datasets on the Internet that can provide more information than what you already have. There are open data platforms in several regions (like data.gov in the U.S.). These open data sets are a fantastic resource if you're working on a personal project for fun.
Data Preparation and Cleaning
The data preparation step, which may consume up to 80% of the time allocated to any big data or data engineering project, comes next. Once you have the data, it's time to start using it. Start exploring what you have and how you can combine everything to meet the primary goal. To understand the relevance of all your data, start making notes on your initial analyses and ask significant questions to businesspeople, the IT team, or other groups. Cleaning up your data is the next step. To ensure that data is consistent and accurate, you must review each column and check for errors, missing data values, etc.
Making sure that your project and your data are compatible with data privacy standards is a key aspect of data preparation that should not be overlooked. Personal data privacy and protection are becoming increasingly crucial, and you should prioritize them immediately as you embark on your big data journey. You must consolidate all your data initiatives, sources, and datasets into one location or platform to facilitate governance and carry out privacy-compliant projects.
Data Transformation and Manipulation
Now that the data is clean, it's time to modify it so you can extract useful information. Starting with combining all of your various sources and group logs will help you focus your data on the most significant aspects. You can do this, for instance, by adding time-based attributes to your data, like:
Acquiring date-related elements (month, hour, day of the week, week of the year, etc.)
Calculating the variations between date-column values, etc.
Joining datasets is another way to improve data, which entails extracting columns from one dataset or tab and adding them to a reference dataset. This is a crucial component of any analysis, but it can become a challenge when you have many data sources.
Visualize Your Data
Now that you have a decent dataset (or perhaps several), it would be wise to begin analyzing it by creating beautiful dashboards, charts, or graphs. The next stage of any data analytics project should focus on visualization because it is the most excellent approach to analyzing and showcasing insights when working with massive amounts of data.
Another method for enhancing your dataset and creating more intriguing features is to use graphs. For instance, by plotting your data points on a map, you can discover that some geographic regions are more informative than some other nations or cities.
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Build Predictive Models Using Machine Learning Algorithms
Machine learning algorithms can help you take your big data project to the next level by providing you with more details and making predictions about future trends. You can create models to find trends in the data that were not visible in graphs by working with clustering techniques (also known as unsupervised learning). These organize relevant outcomes into clusters and more or less explicitly state the characteristic that determines these outcomes.
Advanced data scientists can use supervised algorithms to predict future trends. They discover features that have influenced previous data patterns by reviewing historical data and can then generate predictions using these features.
Lastly, your predictive model needs to be operationalized for the project to be truly valuable. Deploying a machine learning model for adoption by all individuals within an organization is referred to as operationalization.
Repeat The Process
This is the last step in completing your big data project, and it's crucial to the whole data life cycle. One of the biggest mistakes individuals make when it comes to machine learning is assuming that once a model is created and implemented, it will always function normally. On the contrary, if models aren't updated with the latest data and regularly modified, their quality will deteriorate with time.
You need to accept that your model will never indeed be "complete" to accomplish your first data project effectively. You need to continually reevaluate, retrain it, and create new features for it to stay accurate and valuable.
If you are a newbie to Big Data, keep in mind that it is not an easy field, but at the same time, remember that nothing good in life comes easy; you have to work for it. The most helpful way of learning a skill is with some hands-on experience. Below is a list of Big Data project ideas and an idea of the approach you could take to develop them; hoping that this could help you learn more about Big Data and even kick-start a career in Big Data.
1. Build a Scalable Event-Based GCP Data Pipeline using DataFlow
Suppose you are running an eCommerce website, and a customer places an order. In that case, you must inform the warehouse team to check the stock availability and commit to fulfilling the order. After that, the parcel has to be assigned to a delivery firm so it can be shipped to the customer. For such scenarios, data-driven integration becomes less comfortable, so you must prefer event-based data integration.
This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow.
Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes:
Language Used: Python 3.7
Services: Cloud Composer, Google Cloud Storage (GCS), Pub-Sub, Cloud Functions, BigQuery, BigTable
Big Data Project with Source Code: Build a Scalable Event-Based GCP Data Pipeline using DataFlow
2. Snowflake Real-Time Data Warehouse Project for Beginners
Snowflake provides a cloud-based analytics and data storage service called "data warehouse-as-a-service." Work on this project to learn how to use the Snowflake architecture and create a data warehouse in the cloud to bring value to your business.
Data Description: For this project, you will create a sample database containing a table named ‘customer_detail.’ This table will include the details of the customers such as : First name, Last name, Address, City, and State.
Language Used: SQL
Packages/Libraries: Services: Amazon S3, Snowflake, SnowSQL, QuickSight
Source Code: Snowflake Real-Time Data Warehouse Project for Beginners
3. Data Warehouse Design for an E-commerce Site
A data warehouse is an extensive collection of data for a business that helps the business make informed decisions based on data analysis. For an e-commerce site, the data warehouse would be a central repository of consolidated data, from searches to purchases by site visitors. By designing such a data warehouse, the site can manage supply based on demand (inventory management), take care of their logistics, modify pricing for optimum profits and manage advertisements based on searches and items purchased. Recommendations can also be generated based on patterns in a given area or based on age groups, sex, and other similar interests. While designing the data warehouse, it is essential to keep some key aspects, such as how the data from multiple sources can be stored, retrieved, structured, modified, and analyzed. If you are a student looking for Apache Big Data projects, this is a perfect place to start since this project can be developed using Apache Hive .
Access Solution to Data Warehouse Design for an E-com Site
4. Web Server Log Processing
A web server log maintains a list of page requests and activities it has performed. Storing, processing, and mining the data on web servers can be done to analyze the data further. In this manner, webpage ads can be determined, and SEO (Search engine optimization) can also be done. A general overall user experience can be achieved through web-server log analysis. This kind of processing benefits any business that heavily relies on its website for revenue generation or to reach out to its customers. The Apache Hadoop open source big data project ecosystem with tools such as Pig, Impala, Hive, Spark, Kafka Oozie, and HDFS can be used for storage and processing.
Big Data Project using Hadoop with Source Code for Web Server Log Processing
5. Generating Movie/Song Recommendations
Streaming platforms can most easily appeal to their audience based on recommendations, and continuously generating recommendations suitable for a particular individual can maximize engagement on the platform. Streaming platforms today recommend content based on multiple approaches – based on previous watches, demographics, the newest and trending movies, searches, and ratings from other individuals who have watched a movie or listened to a particular song. The datasets must be gathered based on these factors to find patterns. Projects requiring the generation of a recommendation system are excellent intermediate Big Data projects. The use of Spark SQL to store the data and Apache Hive to process the data, along with a few applications of machine learning, can build the required recommendation system .
Learn more about Big Data Tools and Technologies with Innovative and Exciting Big Data Projects Examples.
6. Analysis of Airline Datasets
Large amounts of data from any site need to be processed and analyzed to become valuable to the business. This is another excellent choice if you are searching for Big Data analytics projects for engineering students. In the case of airlines, popular routes will have to be monitored so that more airlines can be available on those routes to maximize efficiency. Does the number of people flying across a particular path change over a day/week/month/year, and what factors can lead to these fluctuations? In addition, it is also necessary to closely observe delays – are older flights more prone to delays? When is the best time of the day/week/year/month to minimize delays? Focus on this data helps the airlines and the passengers using the airlines as well. You can use Apache Hive or Apache Impala to partition and cluster the data. Apache pig can be used for data preprocessing.
A simple big data project idea for students on how to perform analysis of airline datasets is here
7. Real-time Traffic Analysis
Traffic is an issue in many major cities, especially during some busier hours of the day. If traffic is monitored in real-time over popular and alternate routes, steps could be taken to reduce congestion on some roads. Real-time traffic analysis can also program traffic lights at junctions – stay green for a longer time on higher movement roads and less time for roads showing less vehicular movement at a given time. Real-time traffic analysis can help businesses manage their logistics and plan their commute accordingly for working-class individuals. Concepts of deep learning can be used to analyze this dataset properly.
8. Visualizing Wikipedia Trends
Human brains tend to process visual data better than data in any other format. 90% of the information transmitted to the brain is visual, and the human brain can process an image in just 13 milliseconds. Wikipedia is a page that is accessed by people all around the world for research purposes, general information, and just to satisfy their occasional curiosity. Raw page data counts from Wikipedia can be collected and processed via Hadoop. The processed data can then be visualized using Zeppelin notebooks to analyze trends that can be supported based on demographics or parameters. This is a good pick for someone looking to understand how big data analysis and visualization can be achieved through Big Data and also an excellent pick for an Apache Big Data project idea.
Visualizing Wikipedia Trends Big Data Project with Source Code .
9. Analysis of Twitter Sentiments Using Spark Streaming
Sentimental analysis is another interesting big data project topic that deals with the process of determining whether a given opinion is positive, negative, or neutral. For a business, knowing the sentiments or the reaction of a group of people to a new product launch or a new event can help determine the profitability of the product and can help the business to have a more extensive reach by getting an idea of the feel of the customers. From a political standpoint, the sentiments of the crowd toward a candidate or some decision taken by a party can help determine what keeps a specific group of people happy and satisfied. You can use Twitter sentiments to predict election results as well.
Sentiment analysis has to be done for a large dataset since there are over 180 million monetizable daily active users ( https://www.businessofapps.com/data/twitter-statistics/) on Twitter. The analysis also has to be done in real-time. Spark Streaming can be used to gather data from Twitter in real time. NLP (Natural Language Processing) models will have to be used for sentimental analysis, and the models will have to be trained with some prior datasets. Sentiment analysis is one of the more advanced projects that showcase the use of Big Data due to its involvement in NLP.
Access Big Data Project Solution to Twitter Sentiment Analysis
10. Analysis of Crime Datasets
Analysis of crimes such as shootings, robberies, and murders can result in finding trends that can be used to keep the police alert for the likelihood of crimes that can happen in a given area. These trends can help to come up with a more strategized and optimal planning approach to selecting police stations and stationing personnel. With access to CCTV surveillance in real-time, behavior detection can help identify suspicious activities. Similarly, facial recognition software can play a bigger role in identifying criminals. A basic analysis of a crime dataset is one of the ideal Big Data projects for students. However, it can be made more complex by adding in the prediction of crime and facial recognition in places where it is required.
Big Data Analytics Projects for Students on Chicago Crime Data Analysis with Source Code
11. Real-time Analysis of Log-entries from Applications Using Streaming Architectures
If you are looking to practice and get your hands dirty with a real-time big data project, then this big data project title must be on your list. Where web server log processing would require data to be processed in batches, applications that stream data will have log files that would have to be processed in real-time for better analysis. Real-time streaming behavior analysis gives more insight into customer behavior and can help find more content to keep the users engaged. Real-time analysis can also help to detect a security breach and take necessary action immediately. Many social media networks work using the concept of real-time analysis of the content streamed by users on their applications. Spark has a Streaming tool that can process real-time streaming data.
Access Big Data Spark Project Solution to Real-time Analysis of log-entries from applications using Streaming Architecture
12. Health Status Prediction
“Health is wealth” is a prevalent saying. And rightly so, there cannot be wealth unless one is healthy enough to enjoy worldly pleasures. Many diseases have risk factors that can be genetic, environmental, dietary, and more common for a specific age group or sex and more commonly seen in some races or areas. By gathering datasets of this information relevant for particular diseases, e.g., breast cancer, Parkinson’s disease, and diabetes, the presence of more risk factors can be used to measure the probability of the onset of one of these issues. In cases where the risk factors are not already known, analysis of the datasets can be used to identify patterns of risk factors and hence predict the likelihood of onset accordingly. The level of complexity could vary depending on the type of analysis that has to be done for different diseases. Nevertheless, since prediction tools have to be applied, this is not a beginner-level big data project idea.
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13. Analysis of Tourist Behavior
Tourism is a large sector that provides a livelihood for several people and can adversely impact a country's economy.. Not all tourists behave similarly simply because individuals have different preferences. Analyzing this behavior based on decision-making, perception, choice of destination, and level of satisfaction can be used to help travelers and locals have a more wholesome experience. Behavior analysis, like sentiment analysis, is one of the more advanced project ideas in the Big Data field.
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14. Detection of Fake News on Social Media
With the popularity of social media, a major concern is the spread of fake news on various sites. Even worse, this misinformation tends to spread even faster than factual information. According to Wikipedia, fake news can be visual-based, which refers to images, videos, and even graphical representations of data, or linguistics-based, which refers to fake news in the form of text or a string of characters. Different cues are used based on the type of news to differentiate fake news from real. A site like Twitter has 330 million users , while Facebook has 2.8 billion users. A large amount of data will make rounds on these sites, which must be processed to determine the post's validity. Various data models based on machine learning techniques and computational methods based on NLP will have to be used to build an algorithm that can be used to detect fake news on social media.
Access Solution to Interesting Big Data Project on Detection of Fake News
15. Prediction of Calamities in a Given Area
Certain calamities, such as landslides and wildfires, occur more frequently during a particular season and in certain areas. Using certain geospatial technologies such as remote sensing and GIS (Geographic Information System) models makes it possible to monitor areas prone to these calamities and identify triggers that lead to such issues. If calamities can be predicted more accurately, steps can be taken to protect the residents from them, contain the disasters, and maybe even prevent them in the first place. Past data of landslides has to be analyzed, while at the same time, in-site ground monitoring of data has to be done using remote sensing. The sooner the calamity can be identified, the easier it is to contain the harm. The need for knowledge and application of GIS adds to the complexity of this Big Data project.
16. Generating Image Captions
With the emergence of social media and the importance of digital marketing, it has become essential for businesses to upload engaging content. Catchy images are a requirement, but captions for images have to be added to describe them. The additional use of hashtags and attention-drawing captions can help a little more to reach the correct target audience. Large datasets have to be handled which correlate images and captions. This involves image processing and deep learning to understand the image and artificial intelligence to generate relevant but appealing captions. Python can be used as the Big Data source code. Image caption generation cannot exactly be considered a beginner-level Big Data project idea. It is probably better to get some exposure to one of the projects before proceeding with this.
17. Credit Card Fraud Detection
The goal is to identify fraudulent credit card transactions, so a customer is not billed for an item that the customer did not purchase. This can tend to be challenging since there are huge datasets, and detection has to be done as soon as possible so that the fraudsters do not continue to purchase more items. Another challenge here is the data availability since the data is supposed to be primarily private. Since this project involves machine learning, the results will be more accurate with a larger dataset. Data availability can pose a challenge in this manner. Credit card fraud detection is helpful for a business since customers are likely to trust companies with better fraud detection applications, as they will not be billed for purchases made by someone else. Fraud detection can be considered one of the most common Big Data project ideas for beginners and students.
18. GIS Analytics for Better Waste Management
Due to urbanization and population growth, large amounts of waste are being generated globally. Improper waste management is a hazard not only to the environment but also to us. Waste management involves the process of handling, transporting, storing, collecting, recycling, and disposing of the waste generated. Optimal routing of solid waste collection trucks can be done using GIS modeling to ensure that waste is picked up, transferred to a transfer site, and reaches the landfills or recycling plants most efficiently. GIS modeling can also be used to select the best sites for landfills. The location and placement of garbage bins within city localities must also be analyzed.
19. Customized Programs for Students
We all tend to have different strengths and paces of learning. There are different kinds of intelligence, and the curriculum only focuses on a few things. Data analytics can help modify academic programs to nurture students better. Programs can be designed based on a student’s attention span and can be modified according to an individual’s pace, which can be different for different subjects. E.g., one student may find it easier to grasp language subjects but struggle with mathematical concepts.
In contrast, another might find it easier to work with math but not be able to breeze through language subjects. Customized programs can boost students’ morale, which could also reduce the number of dropouts. Analysis of a student’s strong subjects, monitoring their attention span, and their responses to specific topics in a subject can help build the dataset to create these customized programs.
20. Visualizing Website Clickstream Data
Clickstream data analysis refers to collecting, processing, and understanding all the web pages a particular user visits. This analysis benefits web page marketing, product management, and targeted advertisement. Since users tend to visit sites based on their requirements and interests, clickstream analysis can help to get an idea of what a user is looking for. Visualization of the same helps in identifying these trends. In such a manner, advertisements can be generated specific to individuals. Ads on webpages provide a source of income for the webpage, and help the business publishing the ad reach the customer and at the same time, other internet users. This can be classified as a Big Data Apache project by using Hadoop to build it.
Big Data Analytics Projects Solution for Visualization of Clickstream Data on a Website
21. Real-time Tracking of Vehicles
Transportation plays a significant role in many activities. Every day, goods have to be shipped across cities and countries; kids commute to school, and employees have to get to work. Some of these modes might have to be closely monitored for safety and tracking purposes. I’m sure parents would love to know if their children’s school buses were delayed while coming back from school for some reason. Taxi applications have to keep track of their users to ensure the safety of the drivers and the users. Tracking has to be done in real-time, as the vehicles will be continuously on the move. Hence, there will be a continuous stream of data flowing in. This data has to be processed, so there is data available on how the vehicles move so that improvements in routes can be made if required but also just for information on the general whereabouts of the vehicle movement.
Access Big Data Projects Example Code to Real-Time Tracking of Vehicles
22. Analysis of Network Traffic and Call Data Records
There are large chunks of data-making rounds in the telecommunications industry. However, very little of this data is currently being used to improve the business. According to a MindCommerce study: “An average telecom operator generates billions of records per day, and data should be analyzed in real or near real-time to gain maximum benefit.” The main challenge here is that these large amounts of data must be processed in real-time. With big data analysis, telecom industries can make decisions that can improve the customer experience by monitoring the network traffic. Issues such as call drops and network interruptions must be closely monitored to be addressed accordingly. By evaluating the usage patterns of customers, better service plans can be designed to meet these required usage needs. The complexity and tools used could vary based on the usage requirements of this project.
23. Topic Modeling
The future is AI! You must have come across similar quotes about artificial intelligence (AI). Initially, most people found it difficult to believe that could be true. Still, we are witnessing top multinational companies drift towards automating tasks using machine learning tools.
Understand the reason behind this drift by working on one of our repository's most practical data engineering project examples .
Project Objective: Understand the end-to-end implementation of Machine learning operations (MLOps) by using cloud computing.
Learnings from the Project: This project will introduce you to various applications of AWS services . You will learn how to convert an ML application to a Flask Application and its deployment using Gunicord webserver. You will be implementing this project solution in Code Build. This project will help you understand ECS Cluster Task Definition.
Libraries: Flask, gunicorn, scipy, nltk, tqdm, numpy, joblib, pandas, scikit_learn, boto3
Services: Flask, Docker, AWS, Gunicorn
Source Code: MLOps AWS Project on Topic Modeling using Gunicorn Flask
24. MLOps on GCP Project for Autoregression using uWSGI Flask
Here is a project that combines Machine Learning Operations (MLOps) and Google Cloud Platform (GCP). As companies are switching to automation using machine learning algorithms, they have realized hardware plays a crucial role. Thus, many cloud service providers have come up to help such companies overcome their hardware limitations. Therefore, we have added this project to our repository to assist you with the end-to-end deployment of a machine learning project.
Project Objective: Deploying the moving average time-series machine-learning model on the cloud using GCP and Flask.
Learnings from the Project: You will work with Flask and uWSGI model files in this project. You will learn about creating Docker Images and Kubernetes architecture. You will also get to explore different components of GCP and their significance. You will understand how to clone the git repository with the source repository. Flask and Kubernetes deployment will also be discussed in this project.
Tech Stack: Language - Python
Services - GCP, uWSGI, Flask, Kubernetes, Docker
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1. Fruit Image Classification
This project aims to make a mobile application to enable users to take pictures of fruits and get details about them for fruit harvesting. The project develops a data processing chain in a big data environment using Amazon Web Services (AWS) cloud tools, including steps like dimensionality reduction and data preprocessing and implements a fruit image classification engine. The project involves generating PySpark scripts and utilizing the AWS cloud to benefit from a Big Data architecture (EC2, S3, IAM) built on an EC2 Linux server. This project also uses DataBricks since it is compatible with AWS.
Source Code: Fruit Image Classification
2. Airline Customer Service App
In this project, you will build a web application that uses machine learning and Azure data bricks to forecast travel delays using weather data and airline delay statistics. Planning a bulk data import operation is the first step in the project. Next comes preparation, which includes cleaning and preparing the data for testing and building your machine learning model. This project will teach you how to deploy the trained model to Docker containers for on-demand predictions after storing it in Azure Machine Learning Model Management. It transfers data using Azure Data Factory (ADF) and summarises data using Azure Databricks and Spark SQL. The project uses Power BI to visualize batch forecasts.
Source Code: Airline Customer Service App
3. Criminal Network Analysis
This fascinating big data project seeks to find patterns to predict and detect links in a dynamic criminal network. This project uses a stream processing technique to extract relevant information as soon as data is generated since the criminal network is a dynamic social graph. It also suggests three brand-new social network similarity metrics for criminal link discovery and prediction. The next step is to develop a flexible data stream processing application using the Apache Flink framework, which enables the deployment and evaluation of the newly proposed and existing metrics.
Source Code- Criminal Network Analysis
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Most executives prioritize big data projects that focus on utilizing data for business growth and profitability. But up to 85% of big data projects fail, mainly due to management's inability to properly assess project risks initially.
Here are some good practices for successful Big Data projects.
Set Definite Goals
Before building a Big Data project, it is essential to understand why it is being done. It is necessary to comprehend that the goal of a big data project is to identify solutions that boost the company's efficiency and competitiveness.
A Big Data project has every possibility of succeeding when the objectives are clearly stated, and the business problems that must be handled are accurately identified.
Select The Right Big Data Tools and Techniques
Traditional data management uses a client/server architecture to centralize data processing and storage on a single server. Big Data projects now involve the distribution of storage among multiple computers rather than its centralization in a single server to be successful.
Hadoop serves as a good example of this technology strategy. The majority of businesses employ this software implementation.
Ensure Sufficient Data Availability
Ensuring the data is available to individuals who want it is crucial when building a Big Data project. It is easier to persuade them of the significance of the data analyzed if the business's stakeholders are appropriately targeted and given access to the data.
Organizations constantly run their operations so that every department has its data. Every data collection process is kept in a silo, isolated from other groups inside the organization. The Big Data project won't be very productive until all organizational data is constantly accessible to people who require it. The connections and trends that appear can then be fully used.
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Explore a few more big data project ideas with source code on the ProjectPro repository. Get started and build your career in Big Data from scratch if you are a beginner, or grow it from where you are now. Remember, it’s never too late to learn a new skill, and even more so in a field with so many uses at present and, even then, still has so much more to offer. We hope that some of the ideas inspire you to develop your ideas. The Big Data train is chugging at a breakneck pace, and it’s time for you to hop on if you aren’t on it already!
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Why are big data projects important?
Big data projects are important as they will help you to master the necessary big data skills for any job role in the relevant field. These days, most businesses use big data to understand what their customers want, their best customers, and why individuals select specific items. This indicates a huge demand for big data experts in every industry, and you must add some good big data projects to your portfolio to stay ahead of your competitors.
What are some good big data projects?
Design a Network Crawler by Mining Github Social Profiles. In this big data project, you'll work on a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects.
Visualize Daily Wikipedia Trends using Hadoop - You'll build a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects.
Modeling & Thinking in Graphs(Neo4J) using Movielens Dataset - You will reconstruct the movielens dataset in a graph structure and use that structure to answer queries in various ways in this Neo4j big data project.
How long does it take to complete a big data project?
A big data project might take a few hours to hundreds of days to complete. It depends on various factors such as the type of data you are using, its size, where it's stored, whether it is easily accessible, whether you need to perform any considerable amount of ETL processing on the data, etc.
Are big data projects essential to land a job?
According to research, 96% of businesses intend to hire new employees in 2022 with the relevant skills to fill positions relevant to big data analytics. Since there is a significant demand for big data skills, working on big data projects will help you advance your career quickly.
What makes big data analysis difficult to optimize?
Optimizing big data analysis is challenging for several reasons. These include the sheer complexity of the technologies, restricted access to data centers, the urge to gain value as fast as possible, and the need to communicate data quickly enough. However, there are ways to improve big data optimization-
Reduce Processing Latency- Conventional database models have latency in processing because data retrieval takes a long time. Turning away from slow hard discs and relational databases further toward in-memory computing technologies allows organizations to save processing time.
Analyze Data Before Taking Actions- It's advisable to examine data before acting on it by combining batch and real-time processing. While historical data allows businesses to assess trends, the current data — both in batch and streaming formats — will enable organizations to notice changes in those trends. Companies gain a deeper and more accurate view when accessing an updated data set.
Transform Information into Decisions- Various data prediction methods are continually emerging due to machine learning. Big data software and service platforms make it easier to manage the vast amounts of big data by organizations. Large volumes of data are transformed into trends using machine learning. Businesses need to take full advantage of this technology.
How many big data projects fail?
According to a Gartner report, around 85 percent of Big Data projects fail. There can be various reasons causing these failures, such as
Lack of Skills- Most big data projects fail due to low-skilled professionals in an organization. Hiring the right combination of qualified and skilled professionals is essential to building successful big data project solutions.
Incorrect Data- Training data's limited availability and quality is a critical development concern. Data management teams must have internal protocols, such as policies, checklists, and reviews, to ensure proper data utilization.
Poor Team Communication- Often, the projects fail due to a lack of proper interaction between teams involved in the project deployment. Ensuring strong communication between teams adds value to the success of a project.
Undefined Project Goals- Another critical cause of failure is starting a project with unrealistic or unclear goals. It's always good to ask relevant questions and figure out the underlying problem.
What are the types of Big Data?
The three primary types of big data are:
Structured Data: Structured data refers to the data that can be analyzed, retrieved, and stored in a fixed format. Machines and humans are both sources of structured data. Machine-generated data encompasses all data obtained from sensors, websites, and financial systems. Human-generated structured data primarily consists of all information that a person enters into a computer, like his name or other private information.
Semi-structured Data: It is a combination of structured and unstructured data. It is usually the kind of data that does not belong to a specific database but has tags to identify different elements. Emails, CSV/XML/JSON format files, etc., are examples of semi-structured data.
Unstructured Data: Unstructured data refers to data that has an incomprehensible format or pattern. Unstructured data can either be machine-generated or human-generated based on its source. An example of unstructured data is the results of a google search with text, videos, photos, webpage links, etc.
How Big Data Works?
As discussed at the beginning of this blog, Big Data involves handling a company's digital information and implementing tools over it to identify hidden patterns in the data. To achieve that, a business firm needs to have the infrastructure to support different types of data formats and process them. You can build the proper infrastructure if you keep the following three main points that describe how big data works.
Integration: Sourcing data from different sources is fundamental in big data, and in most cases, multiple sources must be integrated to build pipelines that can retrieve data.
Management: The multiple sources discussed above must be appropriately managed. Since relying on physical systems becomes difficult, more and more organizations rely on cloud computing services to handle their big data.
Analysis: This is the most crucial part of implementing a big data project. Implementing data analytics algorithms over datasets assists in revealing hidden patterns that businesses can utilize for making better decisions.
What are the 7 V's of big data?
Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value are the seven V's that best define Big Data.
Volume- This is the most significant aspect of big data. Data is growing exponentially with time, and therefore, it is measured in Zettabytes, Exabytes, and Yottabytes instead of Gigabytes.
Velocity- The term "velocity" indicates the pace at which data can be analyzed and retrieved. Millions of social media articles, YouTube audio and videos, and photos posted every second should be available soon.
Variety- The term "variety" refers to various data sources available. It is one of the most challenging aspects of Big Data as the data available these days is primarily unstructured. Organizing such data is quite a difficult task in itself.
Variability- Variability is not the same as a variety, and "variability" refers to constantly evolving data. The main focus of variability is analyzing and comprehending the precise meanings of primary data.
Veracity- Veracity is primarily about ensuring that the data is reliable, which entails the implementation of policies to prevent unwanted data from gathering in your systems.
Visualization- "Visualization" refers to how you can represent your data to management for decision-making. Data must be easily readable, understandable, and available regardless of its format. Visual charts, graphs, etc., are a great choice to represent your data than excel sheets and numerical reports.
Value- The primary purpose of Big data is to create value. You must ensure your business gains value from the data after dealing with volume, velocity, variety, variability, veracity, and visualization- which consumes a lot of time and resources.
What are the uses of big data?
Big Data has a wide range of applications across industries -
Healthcare - Big data aids the healthcare sector in multiple ways, such as lowering treatment expenses, predicting epidemic outbreaks, avoiding preventable diseases by early discoveries, etc.
Media and Entertainment - The rise in social media and other technologies have resulted in large amounts of data generated in the media industry. Big data benefits this sector in terms of media recommendations, on-demand media streaming, customer data insights, targeting the right audience, etc.
Education - By adding to e-learning systems, big data solutions have helped overcome one of the most significant flaws in the educational system: the one-size-fits-all approach. Big data applications help in various ways, including tailored and flexible learning programs, re-framing study materials, scoring systems, career prediction, etc.
Banking - Data grows exponentially in the banking sector. The proper investigation and analysis of such data can aid in the detection of any illegal acts, such as credit/debit card frauds, enterprise credit risks, money laundering, customer data misuse, etc.
Transportation - Big data has often been applied to make transportation more efficient and reliable. It helps plan routes based on customer demands, predict real-time traffic patterns, improve road safety by predicting accident-prone regions, etc.
What is an example of Big Data?
A company that sells smart wearable devices to millions of people needs to prepare real-time data feeds that display data from sensors on the devices. The technology will help them understand their performance and customers' behavior.
What are the main components of a big data architecture?
The primary components of big data architecture are:
Sources of Data
Storage of Data
Ingestion of real-time messages
Datastore for performing Analytics
Analysis of Data and Reports Preparation
What are the different features of big data analytics?
Here are different features of big data analytics:
- Programs Data Analytics Data Science Programs Data Analytics Data Science
- Student experience Student experience Career services Mentor model Student experience Student experience Career services Mentor model
- data science /
- 6 interesting data science project ideas examples
6 Interesting Data Science Project Ideas and Examples
Many newcomers to data science spend a significant amount of time on theory and not enough on practical application. To make real progress along the path toward becoming a data scientist, it’s important to start building data science projects as soon as possible.
If you're thinking about putting together your own data science projects and don't know where to begin, it's a good idea to seek inspiration from others.
In this post, we’ll share data science project examples from data scientists that will help you understand what a completed project should look like. We’ll also provide some tips for creating your own interesting data science projects.
Data Science Projects
1. “eat, rate, love” — an exploration of r, yelp, and the search for good indian food ( beginner ).
When it comes time to choose a restaurant, many people turn to Yelp to determine which is the best option for the type of food they're in search of. But what happens if you're looking for a specific type of cuisine and there are many restaurants rated the same within a small radius? Which one do you choose? Robert Chen chose, as his capstone project, a way to further evaluate Yelp reviewers to determine if their reviews led to the best Indian restaurants.
Chen discovered while searching Yelp that there were many recommended Indian restaurants with close to the same scores. Certainly, not all the reviewers had the same knowledge of this cuisine, right? With this in mind, he took into consideration the following:
The number of restaurant reviews by a single person of a particular cuisine (in this case, Indian food). He was able to justify this parameter by looking at reviewers of other cuisines, such as Chinese food.
The apparent ethnicity of the reviewer in question. If the reviewer had an Indian name, he could infer that they might be of Indian ethnicity, and therefore more familiar with what constituted good Indian food.
He used Python and R programming languages.
His modification to the data and the variables showed that those with Indian names tended to give good reviews to only one restaurant per city out of the 11 cities he analyzed, thus providing a clear choice per city for restaurant patrons.
Yelp’s data has become popular among newcomers to data science. You can access it here
2. NFL Third and Goal Behavior ( Intermediate)
The intersection of sports and data is full of opportunities for aspiring data scientists. a lover of both, divya parmar decided to focus on the nfl for his capstone project during the data science course..
Divya’s goal: to determine the efficiency of various offensive plays in different tactical situations. Here’s a sample from Divya’s project write-up.
To complete his data science project on the NFL's 3rd down behavior, Divya followed these steps:
To investigate 3rd down behavior, he obtained play-by-play data from Armchair Analysis; the dataset was every play from the first eight weeks of this NFL season. Since the dataset was clean, and we know that 80 percent of the data analysis process is cleaning, he was able to focus on the essential data manipulation to create the data frames and graphs for my analysis.
He used R as his programming language of choice for analysis, as it is open source and has thousands of libraries that allow for vast functionality.
He loaded in his Csv file into RStudio (his software for the analysis). First, he wanted to look at offensive drives themselves, so he generated a drive number for each drive and attached it to individual plays dataset. With that, he could see the length of each drive based on the count of each drive number.
Then, he moved on to his main analysis of 3rd down plays. He created a new data frame, which only included 3rd down plays which were a run or pass (excluding field goals, penalties, etc). He added a new categorical column named “Distance,” which signified how many yards a team had to go to convert the first down.
Using conventional NFL definitions, he decided on this:
This hands-on project work was the most challenging part of the course for Divya, he said, but it allowed him to practice the different steps in the data science process:
Assessing the problem
Manipulating the data
Delivering actionable insights to stakeholders
You can access the data set Divya used here .
3. Who’s a Good Dog? Identifying Dog Breeds Using Neural Networks ( Intermediate )
Garrick Chu, chose to work on an image classification project for his final year, identifying dog breeds using neural networks. This project primarily leveraged Keras through Jupyter notebooks and tested the wide variety of skills commonly associated with neural networks and image data:
Working with large data sets
Effective processing of images (rather than traditional data structures)
Network design and tuning
Transfer learning (combining neural nets trained on different data sets)
Performing exploratory data analysis to understand model outputs that people can’t directly interpret
One of Garrick’s goals was to determine whether he could build a model that would be better than humans at identifying a dog's breed from an image. Because this was a learning task with no benchmark for human accuracy, once Garrick optimized the network to his satisfaction, he went on to conduct original survey research in order to make a meaningful comparison.
See more of Garrick’s work here . You can access the data set he used here .
4. Amazon vs. eBay Analysis ( Advanced )
Ever pulled the trigger on a purchase only to discover shortly afterward that the item was significantly cheaper at another outlet?
In support of a Chrome extension he was building, Chase Roberts decided to compare the prices of 3,500 products on eBay and Amazon. With his biases acknowledged, Chase walks readers of this blog post through his project, starting with how he gathered the data and documenting the challenges he faced during this process.
The results showed the potential for substantial savings. For his project, Chase built a shopping cart with 3.5k products to compare prices on eBay vs Amazon. Here's what he found:
The shopping cart has 3,520 unique items.
If you chose the wrong platform to buy each of these items (by always shopping at whichever site has a more expensive price), this cart would cost you $193,498.45. (Or you could pay off your mortgage.)
This is the worst-case scenario for the shopping cart.
The best-case scenario for our shopping cart, assuming you found the lowest price between eBay and Amazon on every item, is $149,650.94.
This is a $44,000 difference — or 23%!
Find out more about the project here .
5. Fake News! ( Advanced )
Another great idea for a data science project is looking at the common forms of fake news. These days, it’s hard enough for the average social media user to determine when an article is made up with an intention to deceive. So is it possible to build a model that can discern whether a news piece is credible? That’s the question a four-person team from the University of California at Berkeley attempted to answer with this project.
First, the team identified two common forms of fake news to focus on: clickbait (“shocking headlines meant to generate clicks to increase ad revenue”) and propaganda (“intentionally misleading or deceptive articles meant to promote the author’s agenda”).
To develop a classifier that would be able to detect clickbait and propaganda articles, these steps were followed:
The foursome scraped data from news sources listed on OpenSources
Preprocessed articles for content-based classification using natural language processing
Trained different machine learning models to classify the news articles
Created a web application to serve as the front end for their classifier
Find out more and try it out here .
6. Audio Snowflake ( Advanced )
When you think about data science projects, chances are you think about how to solve a particular problem, as seen in the examples above. But what about creating a project for the sheer beauty of the data? That's exactly what Wendy Dherin did.
The purpose of her Hackbright Academy project was to create a stunning visual representation of music as it played, capturing a number of components, such as tempo, duration, key, and mood. The web application Wendy created uses an embedded Spotify web player, an API to scrape detailed song data, and trigonometry to move a series of colorful shapes around the screen. Audio Snowflake maps both quantitative and qualitative characteristics of songs to visual traits such as color, saturation, rotation speed, and the shapes of figures it generates.
She explains a bit about how it works:
Each line forms a geometric shape called a hypotrochoid (pronounced hai-po-tro-koid).
Hypotrochoids are mathematical roulettes traced by a point P that is attached to a circle which rolls around the interior of a larger circle. If you have played with Spirograph, you may be familiar with the concept.
The shape of any hypotrochoid is determined by the radius a of the large circle, the radius b of the small circle, and the distance h between the center of the smaller circle and point P.
For Audio Snowflake, these values are determined as follows:
a: song duration
b: section duration
h: song duration minus section duration
Find out more here .
Bonus Data Sets for Data Science Projects
Here are a few more data sets to consider as you ponder data science project ideas:
VoxCeleb : an audio-visual data set consisting of short clips of human speech, extracted from interviews uploaded to YouTube.
Titanic : a classic data set appropriate for data science projects for beginners.
Boston Housing Data : a fairly small data set based on U.S. Census Bureau data that’s focused on a regression problem.
Big Mart Sales : a retail industry data set that can be used to predict store sales.
FiveThirtyEight : Nate Silver’s publication shares the data and code behind some of its articles and graphics so admirers can create stories and visualizations of their own.
Tips for Creating Cool Data Science Projects
Getting started on your own data science project may seem daunting at first, which is why at Springboard, we pair students with one-on-one mentors and student advisors who help guide them through the process.
When you start your data science project, you need to come up with a problem that you can use data to help solve. It could be a simple problem or a complex one, depending on how much data you have, how many variables you must consider, and how complicated the programming is.
Choose the Right Problem
If you're a data science beginner, it's best to consider problems that have limited data and variables. Otherwise, your project may get too complex too quickly, potentially deterring you from moving forward. Choose one of the data sets in this post, or look for something in real life that has a limited data set. Data wrangling can be tedious work, so it’s key, especially when starting out, to make sure the data you’re manipulating and the larger topic is interesting to you. These are challenging projects, but they should be fun!
Breaking Up the Project Into Manageable Pieces
Your next task is to outline the steps you’ll need to take to create your data science project. Once you have your outline, you can tackle the problem and come up with a model that may prove your hypothesis. You can do this in six steps:
Generate your hypotheses
Study the data
Clean the data
Engineer the features
Create predictive models
Communicate your results
Generate Your Hypotheses
After you have your problem, you need to create at least one hypothesis that will help solve the problem. The hypothesis is your belief about how the data reacts to certain variables. For example, if you are working with the Big Mart data set that we included among the bonus options above, you may make the hypothesis that stores located in affluent neighborhoods are more likely to see higher sales of expensive coffee than those stores in less affluent neighborhoods.
This is, of courses, dependent on you obtaining general demographics of certain neighborhoods. You will need to create as many hypotheses as you need to solve the problem.
Study the Data
Your hypotheses need to have data that will allow you to prove or disprove them. This is where you need to look in the data set for variables that affect the problem. In the Big Mart example, you'll be looking for data that will lead to variables. In the coffee hypothesis, you need to be able to identify brands of coffee, prices, sales, and the surrounding neighborhood demographics of each store. If you do not have the data, you either have to dig deeper or change your hypothesis.
Clean the Data
As much as data scientists prefer to have clean, ready-to-go data, the reality is seldom neat or orderly. You may have outlier data that you can't readily explain, like a sudden large, one-time purchase of expensive coffee in a store that is in a lower-income neighborhood or a dip in coffee purchases that you wouldn't expect during a random two-week period (using the Big Mart scenario). Or maybe one store didn't report data for a week.
These are all problems with the data that isn't the norm. In these cases, it's up to you as a data scientist to remove those outliers and add missing data so that the data is more or less consistent. Without these changes, your results will become skewed and the outlier data will affect the results, sometimes drastically.
With the problem you're trying to solve, you aren't looking for exceptions, but rather you're looking for trends. Those trends are what will help predict profits at the Big Mart stores.
Engineer the Features
At this stage, you need to start assigning variables to your data. You need to factor in what will affect your data. Does a heatwave during the summer cause coffee sales to drop? Does the holiday season affect sales of high-end coffee in all stores and not just middle-to-high-income neighborhoods? Things like seasonal purchases become variables you need to account for.
You may have to modify certain variables you created in order to have a better prediction of sales. For example, maybe the sales of high-end coffee isn't an indicator of profits, but whether the store sells a lot of holiday merchandise is. You'd have to examine and tweak the variables that make the most sense to solve your problem.
Create Your Predictive Models
At some point, you'll have to come up with predictive models to support your hypotheses. For example, you'll have to design code that will show that when certain variables occur, you have a flux in sales. For Big Mart, your predictive models might include holidays and other times of the year when retail sales spike. You may explore whether an after-Christmas sale increases profits and if so, by how much. You may find that a certain percentage of sales earn more money than other sales, given the volume and overall profit.
Communicate Your Results
In the real world, all the analysis and technical results that you come up with are of little value unless you can explain to your stakeholders what they mean in a way that’s comprehensible and compelling. Data storytelling is a critical and underrated skill that you must develop. To finish your project, you’ll want to create a data visualization or a presentation that explains your results to non-technical folks.
Learn online from an industry-driven curriculum for a career in data.
Students in our semester-based bachelor’s and master’s degree programs complete a final capstone project which synthesizes and applies information from their coursework. Students serve in a consultant role, identifying a business need of the partner site/organization, assessing it, analyzing the data, and providing recommendations for implementation that are grounded in research and best practices.
Capstone projects take place at host site and can be completed on site or remotely as needed by the host. In some cases, students can complete their capstone through their current place of employment.
Capstone students build relationships and gain experience with their host organization–while working directly with stakeholders to solve a timely business problem or question.
Explore our database of completed capstone projects or refer to the capstone course description for your program of interest to learn more about the capstone process.
3M Menomonie Fibers Water Use Analysis and Reduction Proposal
A case study on machine learning assisted compression: using autoencoders for image compression on commodity hardware, a case study on modeling an nba expansion starting five, a case study on recommendation systems using implicit feedback, a case study on stock trading sentiment analysis, a case study on utilizing predictive analytics in cpm applications, a comparative assessment of recommendation systems and its ethical implications, a dedicated program manager increases employee engagement, a deep dive into deep learning and its applications, a look at the connection between a facility’s delivery volume, service results, and preventable accident performance.
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This repository contains toy implementations of a data science project using Cookie Cutter Data Science type templates.
Checkout out different branches for different examples.
- skeleton : skeleton code for a simple example
- titanic : Titanic ML classifier, with how to deal with API tokens.
- EDA : a generic EDA that creates autogenerated reports via notebooks.
- nn_regression : Training neural network regressor, with local dataset and anaconda requirements setup.
- think_stats : Statistical analysis with notebook usage
14 Popular Data Science Project Ideas for Beginners
The best way to get good at Data Science tools and technologies, as a beginner, is to build projects that solve real-world problems. Keeping that in mind, in this blog, we will take a look at the Top 14 Data Science Projects Ideas that you can undertake to upskill yourself.
As a beginner, it can be extremely daunting to understand Data Science, have a good understanding of the concepts involved, and gain hands-on experience in them. One of the best ways to become good at Data Science or anything creative is by deliberately practicing the acquired skills to reinforce them in your brain. For this, you may have to work on various projects but, as a beginner, it can be quite difficult to choose not-very-complicated Data Science projects—some projects may be difficult to implement and some may not help you push yourself to the limits. If all this sounds familiar to you, then this blog is for you.
In this blog, we will discuss the best projects in Data Science for beginners to try out and expand their knowledge and skill set. These Data Science project ideas will also help you get a taste of how to deal with real-world Data Science problems.
This blog will discuss the following topics:
Recommendation System Project
Data analysis project, sentiment analysis project, fraud detection project, image classification project, image caption generator project in python, chatbot project in python, brain tumor detection with data science, traffic sign recognition, fake news detection, forest fire prediction, human action recognition, classifying breast cancer, gender detection and age prediction, tips for a good data science project.
Check out our Data Science Project Tutorial Video on YouTube designed especially for Beginners:
Data Science Project Ideas
Without delay, let us start exploring the most interesting Data Science projects for beginners.
A recommendation system is one of the most important aspects of any content-based application such as blog, e-commerce website, streaming platform, etc. A recommendation system suggests new content to users from the site’s content library or database based on what the users have already viewed and liked. A recommendation system needs data about users and their activities on the site as well as information about the content so that it can be classified and recommended to the users based on their tastes and preferences. A project-based recommendation system is also one of the most popular Data Science project ideas.
These systems can be built by using the following techniques:
- Collaborative filtering: In this technique, the system generates recommendations for users based on other users who have viewed and liked similar things. This technique is good but can end up generating bad recommendations as the users who were used for generating recommendations may have changed their opinion about a movie they had liked in the past, which might lead the engine to recommend a movie that a user similar to you may not like right now. Moreover, the geographical and cultural context of users may make them consider the recommendations to be undesirable.
- Content-based filtering: In this technique, the system generates recommendations for users by recommending content similar to what the users have previously viewed and liked. This technique is much more stable and consistent than collaborative filtering as it relies on the users’ own preferences as well as on the attributes of the available content, which do not usually change over time.
This is one of the most interesting projects. There are many other techniques that are quite advanced and complicated, but these two techniques would be enough for you to build your own recommendation engine as a beginner. You can train the engine to be used for recommending movies, blog posts, products, etc.
- Movie or web show recommendation system
- Product recommendation system
- Blog post recommendation system
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Data analysis is one of the core skills that is needed by a data scientist . In data analysis, you take some data and try to gain insights from it by analyzing it in order to make better decisions. One of the ways in which we can simplify the analysis is by generating visualizations that can be interpreted easily. The scope of data analysis is vast but this is one of the most useful Data Science projects.
Today, data is considered more important than oil. All companies store data about their users and how they interact with the products. This data allows companies to craft better policies and features that help solve customer problems and attract more user engagement with the platform.
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For example, if you are working on the data of an e-commerce company and find that users from a particular country buy only specific kinds of products, then you can use this information to get a better understanding of why it is happening and to generate better product recommendations for more engagement.
Companies, such as Uber, Amazon, Flipkart, etc., use data analysis to create better offers and generate better quotes to meet customer expectations in the best way possible. It is one of the projects in Data Science that many companies implement in their own ways.
For Data Science projects on data analysis, you can use e-commerce datasets or datasets from ride-hailing apps, such as Uber, Lyft, etc.
- Analysis of cab and weather data
- Analysis of store sales data
- Generate offers using association rule mining
Master the skills to become a top Data Scientist by enrolling for Intellipaat’s Data Science Online Course .
Sentiment analysis is used to add emotional intelligence to systems. It is one of the projects in Data Science that people start with when they wish to learn how to process text. For example, when a user types in a comment on a video or blog post, sentiment analysis can be used to determine if the comment is appreciative, disparaging, critical, etc. These can also be used to classify emails, messages, reviews, queries, etc.
One of the major applications of these kinds of Data Science projects can be seen on public platforms, such as Twitter, Reddit, etc., where users post things that are tagged to indicate the type of content contained in them, i.e., positive or negative, with the help of sentiment analysis. This technique helps companies to understand, process, and tag even unstructured text.
These projects on sentiment analysis can be quite useful for various companies. Sentiment analysis can also be used to analyze and make sense of reviews, complaints, queries, emails, product descriptions, etc. For instance, you can use sentiment analysis to generate tags for such content as being negative, positive, neutral, etc.
Use Cases :
- For classifying emails as positive or negative
- For labeling tweets as positive or negative
- For categorizing emotions on an audio based on speech patterns
Fraud detection is one of the most important Data Science projects and also one of the most challenging for final-year students. With many forms of online and digital transactions being used widely, the chances of them being fraudulent are increasingly high. Since any form of digital transaction generates data regarding current and previous transactions, as well as customer purchase records, you can use these data and Data Science techniques to identify if the transactions are potentially fraudulent.
Any transaction done digitally is bound to create some data. When a customer uses a digital medium to make a payment, you can use this generated data with the trained model to flag the transaction as potentially fraudulent, which can later be dealt with and reviewed. This is one of the most important projects to practice in case you wish to be able to build Machine Learning models based on data about user activity.
Large amounts of money are being digitally transferred every day; thus, you should be able to classify if these records are fraudulent or not. To do this, you have to create models that are trained on the data collected from previous transactions. These models use and analyze factors such as the amount transferred, the location it is transferred from, the location to which it is transferred, etc. These factors are taken into account when new transactions take place, and then, based on these factors, they are flagged as fraudulent or authentic transactions.
- Credit card fraud detection
- Transaction records fraud detection
Preparing for job interviews? Go through our list of most-asked questions on our blog on Data Science Interview Questions and Answers .
Image classification is one of the Data Science projects that can be used to classify and tag images based on their content. Image classification is widely used in the fields of science, security, etc. This is also among the most important applications of Data Science as it is very difficult to classify images with traditional application programming. Earlier, a lot of time and research was required to generate complicated rules and image transformations to classify images, and the result was still quite prone to errors. With Data Science, you can create models by training them with a lot of labeled images. These models can then generate Machine Learning classification rules on their own, and you can feed new images to be classified by the classification rules.
In Data Science projects like these kinds of classifications can be done by using several algorithms, and it is better to use several algorithms to find the one that performs the best for your dataset . You will have to make sure to use a large collection of images with good resolution for training and testing purposes. Image classification also requires you to have a good grasp of fundamental image concepts and manipulation techniques such as image reshaping, resizing, edge detection, etc.
Courses you may like
- Digit recognition system
- Facial detection system
- Gender and age detection system
Any social media application that allows storing and sharing images lets users provide captions to those images. The captions are given to provide more context and necessary information about the images. The captions also help in things such as Search Engine Optimization (SEO), content ranking, etc. In blogs, having a caption or good description of what a particular image contains can be very helpful for the readers. Captions also help with accessibility and allow screen reader software to help people with disabilities get a better understanding of the content of the image. Generating these captions can be one of the most challenging Data Science projects.
However, in many cases, generating captions is a long and tedious process, especially when there are a lot of images. To solve this issue, you can generate captions based on what is actually shown in the image. The captions will serve as descriptions of what the images have in them, e.g., a man surfing, a dog smiling, etc.
To do this, you need to understand and use neural networks , especially convolutional neural networks (CNNs) and long short-term memory (LSTM). There are a lot of large datasets available to do this task such as Flickr8k dataset. If training a new model is not possible on your current machine, then you can use the available pretrained models as well. Image Caption Generator is one of the best Data Science projects to understand how to process images using neural networks.
- Twitter hashtag generator for images
- Facebook image caption generator
- Blog post image alt-text generator
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Chatbots are one of the most essential parts of any customer-centric app of the day. They help in the better tracking of customer issues, faster issue resolution, and generating commands using normal text. For example, many bots on platforms, such as Slack and GitHub , allow you to perform certain tasks just by writing and sending them requirements in the chat box. Chatbots also help customers get resolutions to their grievances without any human interaction. For example, food delivery apps, such as Uber Eats and DoorDash, use chatbots to assist users to resolve common issues including refunds, missing food items, incorrect items, etc.
There are two types of chatbots:
- Domain-specific chatbots: A domain-specific chatbot is a chatbot that can be used to answer questions based on a particular domain, such as healthcare, engineering, etc. So, it needs to be customized quite effectively to suit our needs.
- Open-domain chatbots: An open-domain chatbot is a chatbot that can be used to ask questions about any domain, which means that it does not require careful customizations. However, it does need a large volume of data from where to learn.
Data Science projects like these make extensive use of Natural Language Processing (NLP). Implementing a chatbot requires a good grasp of concepts related to NLP, access to a dataset that contains the patterns that you need to find and the responses that you have to return to the user.
- Customer care using a chatbot
- Customer feedback using a chatbot
- Quote generation using a chatbot
There are many applications of Data Science in the healthcare field as well. One of these is brain tumor detection. In this project, you will take a lot of labeled images of MRI scans and train a model using them. Once the model is well-trained, you will use it to check an MRI image to see if there is any chance of detection of a brain tumor.
To implement these kinds of Data Science projects, you need access to MRI scan images of the human brain. Thankfully, there are datasets available on Kaggle. All you have to do is use these images to train your model so that, when fed with similar images, it can classify them as detecting a brain tumor or not. Though such models do not completely remove the need for a consultation from a domain expert, they do help doctors get a quick second opinion.
- Brain tumor detection using MRI images
- Brain tumor detection using vital information
- Brain tumor detection using patient history
Nowadays, one of the most popular applications of Data Science is self-driving cars. Although a self-driving car could be very difficult and expensive to work with, you can implement a specific and important feature needed in a self-driving car, which is traffic sign recognition.
In this project, you will use images of different traffic signs and label them, depicting what the signs are indicating. The more images there are, the more accurate the model will be, though it will take longer to train the model. You will start by using convolutional neural networks (CNNs) to build the model with images that are labeled with what is being indicated by a specific traffic sign. Your model will learn with the help of these images and labels. Next, when a new image is given as the input, the model will be able to classify it.
- Gesture recognition system
- Sign language translator
- Product quality checking system
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A recent study done by MIT claims that fake news spreads six times faster than real news. Fake news is becoming a great source of trouble in all spheres of life. It leads to a lot of problems around the globe, ranging from political polarization, violence, and propagation of misinformation to religious and cultural conflicts. It is also troubling that more and more unverified sources of information, especially social media platforms, are gaining traction; this is doubly concerning as these platforms do not have systems in place to distinguish between fake news and real news.
To tackle a problem like this, especially on a smaller scale, you can use a dataset that contains fake news and real news labeled in the form of textual information. You can use NLP and techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer. This allows you to enter some text from a news article to get a label that tells if it is fake news or real news. It is important to note that these labels may not be 100 percent accurate, but they can give a good approximation to know what is correct or real.
- Fake news checker
- Fact checker
- Information verification system
Building a forest fire prediction model can be a great data science project. Forest fire or wildfire are known to be uncontrollable and capable of causing a large amount of damage. You can apply k-means clustering to manage wildfires as well as assume their disrupted nature. It will also help to spot the major fire hotspots and their severity.
This model can also be useful in the proper allocation of resources. Meteorological data can be used to search for specific periods and seasons for wildfires to increase the accuracy of the model.
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This model will attempt to execute classification based on human actions. The human action recognition model will analyze short videos of human beings performing specific actions.
This Data Science project will require the use of a complex neural network that is trained on a specific dataset containing short videos. Accelerometer data is associated with the dataset. First, the accelerometer data conversion is performed along with a time-sliced representation. The Keras library is then used to train, validate, and test the network based on these datasets.
Breast cancer cases are on the rise, and early detection is the best possible way to take suitable measures. A breast cancer detection system can be built by using Python. You can use the Invasive Ductal Carcinoma (IDC) dataset carrying the histology images for cancer-inducing malignant cells. The model can be trained based on this dataset.
Some useful Python libraries that will be helpful for this Data Science project are NumPy, Keras, TensorFlow, OpenCV, Scikit-learn, and Matplotlib.
Gender Detection and Age Prediction with OpenCV is an impressive Data Science project idea that can easily grab a recruiter’s attention if it is on your resume. This real-time Machine Learning project is based on computer visioning.
Through this project, you will come across the practical application of convolutional neural networks (CNNs). Eventually, you will also get the opportunity to implement models that are trained by Tal Hassner and Gil Levi for Adience dataset collection. This collection contains unfiltered faces and working with them will help with gender and age classification.
The project may also require the use of files such as .pb, .prototxt, .pbtxt, and .caffemodel. This project is very practical, and the model can detect any age and gender via an image using single face detection.
While gender and age ranges can be classified with this model, due to various factors, such as makeup, poor lighting, or unusual facial expressions, the accuracy of the model can become a challenge. Therefore, a classification model instead of a regression model can be used.
Now, let us discuss some key aspects of a good Data Science project:
- Language: You can use any programming language of your choice, whatever you are comfortable with and is familiar to you. Just make sure that the language you are using is a popular one so that other people can collaborate and understand your code and can help you with it. But still, some of the most popular languages for data science are R and Python. Data Science projects in Python are especially useful as it is more widely used than R.
- Datasets: You can get datasets from several sources, but make sure that you are using a large enough dataset that does not contain a lot of errors and incorrect data. In case your dataset has many errors, try removing those errors or use another dataset. To get good datasets, try using Kaggle or UCI Machine Learning Repository.
- Visualizations: Before training your model, try to get a good understanding of the dataset through visualization . You can find useful information, including correlated columns, bias, etc., in your dataset through visualizations. If any issue is found in your dataset, such as the dataset being skewed, biased, or having outliers, try rectifying the same before proceeding.
- Data cleaning: Make sure that the data you are using is clean and usable. The reason is that the data with a lot of errors will lead to a terrible performance of the model.
- Data transformation: In case you use multiple datasets from different sources, it can be difficult to merge them as they can be quite different from each other. For example, different datasets may end up using different formats for dates, different measurement units based on specific geographical locations, etc.; so, you may have to transform the data to make it standardized to train your model.
- Validation: Try to validate your model’s accuracy by using multiple slices of your dataset with the help of techniques such as stratified k-folds cross-validation to get a more accurate performance from your model. If you find issues, try digging deeper to rectify them.
In this blog, we have discussed the most relevant real-time Data Science projects as well as some tips for beginners to be able to better utilize their skills and tackle some real-world problems using various datasets. Hopefully, this blog was helpful and informative to you.
You can also explore this Data Science course in Pune to know more about Data Science projects!
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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
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13 Ultimate Big Data Project Ideas & Topics for Beginners 
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Big Data Project Ideas
Big Data is an exciting subject. It helps you find patterns and results you wouldn’t have noticed otherwise. This skill highly in demand , and you can quickly advance your career by learning it. So, if you are a big data beginner, the best thing you can do is work on some big data project ideas. But it can be difficult for a beginner to find suitable big data topics as they aren’t very familiar with the subject.
We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting big data project ideas which beginners can work on to put their big data knowledge to test. In this article, you will find top big data project ideas for beginners to get hands-on experience on big data
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However, knowing the theory of big data alone won’t help you much. You’ll need to practice what you’ve learned. But how would you do that?
You can practice your big data skills on big data projects. Projects are a great way to test your skills. They are also great for your CV. Especially big data research projects and data processing projects are something that will help you understand the whole of the subject most efficiently.
Read : Big data career path
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What are the areas where big data analytics is used.
Before jumping into the list of big data topics t hat you can try out as a beginner, you need to understand the areas of application of the subject. This will help you invent your own topics for data processing projects once you complete a few from the list. Hence, let’s see what are the areas where big data analytics is used the most. This will help you navigate how to identify issues in certain industries and how they can be resolved with the help of big data as big data research projects.
- Banking and Safety:
The banking industry often deals with cases of card fraud, security fraud, ticks and such other issues that greatly hamper their functioning as well as market reputation. Hence to tackle that, the securities exchange commission aka SEC takes the help of big data and monitors the financial market activity.
This has further helped them manage a safer environment for highly valuable customers like retail traders, hedge funds, big banks and other eminent individuals in the financial market. Big data has helped this industry in the cases like anti-money laundering, fraud mitigation, demand enterprise risk management and other cases of risk analytics.
- Media and Entertainment industry
It is needless to say that the media and entertainment industry heavily depends on the verdict of the consumers and this is why they are always required to put up their best game. For that, they require to understand the current trends and demands of the public, which is also something that changes rapidly these days.
To get an in-depth understanding of consumer behaviour and their needs, the media and entertainment industry collects, analyses and utilises customer insights. They leverage mobile and social media content to understand the patterns at a real-time speed.
The industry leverages Big data to run detailed sentiment analysis to pitch the perfect content to the users. Some of the biggest names in the entertainment industry such as Spotify and Amazon Prime are known for using big data to provide accurate content recommendations to their users, which helps them improve their customer satisfaction and, therefore, increases customer retention.
- Healthcare Industry
Even though the healthcare industry generates huge volumes of data on a daily basis which can be ustilised in many ways to improve the healthcare industry, it fails to utilise it completely due to issues of usability of it. Yet there is a significant number of areas where the healthcare industry is continuously utilising Big Data.
The main area where the healthcare industry is actively leveraging big data is to improve hospital administration so that patients can revoke best-in-class clinical support. Apart from that, Big Data is also used in fighting lethal diseases like cancer. Big Data has also helped the industry to save itself from potential frauds and committing usual man-made errors like providing the wrong dosage, medicine etc.
Similar to the society that we live in, the education system is also evolving. Especially after the pandemic hit hard, the change became even more rapid. With the introduction of remote learning, the education system transformed drastically, and so did its problems.
On that note, Big Data significantly came in handy, as it helped educational institutions to get the insights that can be used to take the right decisions suitable for the circumstances. Big Data helped educators to understand the importance of creating a unique and customised curriculum to fight issues like students not being able to retain attention.
It not only helped improve the educational system but to identify the student’s strengths and channeled them right.
- Government and Public Services
Likewise the field of government and public services itself, the applications of Big Data by them are also extensive and diverse. Government leverages big data mostly in areas like financial market analysis, fraud detection, energy resource exploration, environment protection, public-health-related research and so forth.
The Food and Drug Administration (FDA) actively uses Big Data to study food-related illnesses and disease patterns.
- Retail and Wholesale Industry
In spite of having tons of data available online in form of reviews, customer loyalty cards, RFID etc. the retail and wholesale industry is still lacking in making complete use of it. These insights hold great potential to change the game of customer experience and customer loyalty.
Especially after the emergence of e-commerce, big data is used by companies to create custom recommendations based on their previous purchasing behaviour or even from their search history.
In the case of brick-and-mortar stores as well, big data is used for monitoring store-level demand in real-time so that it can be ensured that the best-selling items remain in stock. Along with that, in the case of this industry, data is also helpful in improving the entire value chain to increase profits.
- Manufacturing and Resources Industry
The demand for resources of every kind and manufactured product is only increasing with time which is making it difficult for industries to cope. However, there are large volumes of data from these industries that are untapped and hold the potential to make both industries more efficient, profitable and manageable.
By integrating large volumes of geospatial and geographical data available online, better predictive analysis can be done to find the best areas for natural resource explorations. Similarly, in the case of the manufacturing industry, Big Data can help solve several issues regarding the supply chain and provide companies with a competitive edge.
- Insurance Industry
The insurance industry is anticipated to be the highest profit-making industry but its vast and diverse customer base makes it difficult for it to incorporate state-of-the-art requirements like personalized services, personalised prices and targeted services. To tackle these prime challenges Big Data plays a huge part.
Big data helps this industry to gain customer insights that further help in curating simple and transparent products that match the recruitment of the customers. Along with that, big data also helps the industry analyse and predict customer behaviours and results in the best decision-making for insurance companies. Apart from predictive analytics, big data is also utilised in fraud detection.
What problems you might face in doing Big Data Projects
Big data is present in numerous industries. So you’ll find a wide variety of big data project topics to work on too.
Apart from the wide variety of project ideas, there are a bunch of challenges a big data analyst faces while working on such projects.
They are the following:
Limited Monitoring Solutions
You can face problems while monitoring real-time environments because there aren’t many solutions available for this purpose.
That’s why you should be familiar with the technologies you’ll need to use in big data analysis before you begin working on a project.
A common problem among data analysis is of output latency during data virtualization. Most of these tools require high-level performance, which leads to these latency problems.
Due to the latency in output generation, timing issues arise with the virtualization of data.
The requirement of High-level Scripting
When working on big data analytics projects, you might encounter tools or problems which require higher-level scripting than you’re familiar with.
In that case, you should try to learn more about the problem and ask others about the same.
Data Privacy and Security
While working on the data available to you, you have to ensure that all the data remains secure and private.
Leakage of data can wreak havoc to your project as well as your work. Sometimes users leak data too, so you have to keep that in mind.
Knowledge Read: Big data jobs & Career planning
Unavailability of Tools
You can’t do end-to-end testing with just one tool. You should figure out which tools you will need to use to complete a specific project.
When you don’t have the right tool at a specific device, it can waste a lot of time and cause a lot of frustration.
That is why you should have the required tools before you start the project.
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Too Big Datasets
You can come across a dataset which is too big for you to handle. Or, you might need to verify more data to complete the project as well.
Make sure that you update your data regularly to solve this problem. It’s also possible that your data has duplicates, so you should remove them, as well.
While working on big data projects, keep in mind the following points to solve these challenges:
- Use the right combination of hardware as well as software tools to make sure your work doesn’t get hampered later on due to the lack of the same.
- Check your data thoroughly and get rid of any duplicates.
- Follow Machine Learning approaches for better efficiency and results.
- What are the technologies you’ll need to use in Big Data Analytics Projects:
We recommend the following technologies for beginner-level big data projects:
- Open-source databases
- C++, Python
- Cloud solutions (such as Azure and AWS)
- R (programming language)
Each of these technologies will help you with a different sector. For example, you will need to use cloud solutions for data storage and access.
On the other hand, you will need to use R for using data science tools . These are all the problems you need to face and fix when you work on big data project ideas.
If you are not familiar with any of the technologies we mentioned above, you should learn about the same before working on a project. The more big data project ideas you try, the more experience you gain.
Otherwise, you’d be prone to making a lot of mistakes which you could’ve easily avoided.
So, here are a few Big Data Project ideas which beginners can work on:
Read : Career in big data and its scope.
Big Data Project Ideas: Beginners Level
This list of big data project ideas for students is suited for beginners, and those just starting out with big data. These big data project ideas will get you going with all the practicalities you need to succeed in your career as a big data developer.
Further, if you’re looking for big data project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some big data project ideas that will strengthen your base and allow you to climb up the ladder.
We know how challenging it is to find the right project ideas as a beginner. You don’t know what you should be working on, and you don’t see how it will benefit you.
That’s why we have prepared the following list of big data projects so you can start working on them: Let’s start with big data project ideas.
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1. classify 1994 census income data.
One of the best ideas to start experimenting you hands-on big data projects for students is working on this project. You will have to build a model to predict if the income of an individual in the US is more or less than $50,000 based on the data available.
A person’s income depends on a lot of factors, and you’ll have to take into account every one of them.
You can find the data for this project here .
2. Analyze Crime Rates in Chicago
Law enforcement agencies take the help of big data to find patterns in the crimes taking place. Doing this helps the agencies in predicting future events and helps them in mitigating the crime rates.
You will have to find patterns, create models, and then validate your model.
You can get the data for this project here .
3. Text Mining Project
This is one of the excellent deep learning project ideas for beginners. Text mining is in high demand, and it will help you a lot in showcasing your strengths as a data scientist. In this project, you will have to perform text analysis and visualization of the provided documents.
You will have to use Natural Language Process Techniques for this task.
You can get the data here .
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Big data project ideas: advanced level, 4. big data for cybersecurity.
This project will investigate the long-term and time-invariant dependence relationships in large volumes of data. The main aim of this Big Data project is to combat real-world cybersecurity problems by exploiting vulnerability disclosure trends with complex multivariate time series data. This cybersecurity project seeks to establish an innovative and robust statistical framework to help you gain an in-depth understanding of the disclosure dynamics and their intriguing dependence structures.
5. Health status prediction
This is one of the interesting big data project ideas. This Big Data project is designed to predict the health status based on massive datasets. It will involve the creation of a machine learning model that can accurately classify users according to their health attributes to qualify them as having or not having heart diseases. Decision trees are the best machine learning method for classification, and hence, it is the ideal prediction tool for this project. The feature selection approach will help enhance the classification accuracy of the ML model.
6. Anomaly detection in cloud servers
In this project, an anomaly detection approach will be implemented for streaming large datasets. The proposed project will detect anomalies in cloud servers by leveraging two core algorithms – state summarization and novel nested-arc hidden semi-Markov model (NAHSMM). While state summarization will extract usage behaviour reflective states from raw sequences, NAHSMM will create an anomaly detection algorithm with a forensic module to obtain the normal behaviour threshold in the training phase.
7. Recruitment for Big Data job profiles
Recruitment is a challenging job responsibility of the HR department of any company. Here, we’ll create a Big Data project that can analyze vast amounts of data gathered from real-world job posts published online. The project involves three steps:
- Identify four Big Data job families in the given dataset.
- Identify nine homogeneous groups of Big Data skills that are highly valued by companies.
- Characterize each Big Data job family according to the level of competence required for each Big Data skill set.
The goal of this project is to help the HR department find better recruitments for Big Data job roles.
8. Malicious user detection in Big Data collection
This is one of the trending deep learning project ideas. When talking about Big Data collections, the trustworthiness (reliability) of users is of supreme importance. In this project, we will calculate the reliability factor of users in a given Big Data collection. To achieve this, the project will divide the trustworthiness into familiarity and similarity trustworthiness. Furthermore, it will divide all the participants into small groups according to the similarity trustworthiness factor and then calculate the trustworthiness of each group separately to reduce the computational complexity. This grouping strategy allows the project to represent the trust level of a particular group as a whole.
9. Tourist behaviour analysis
This is one of the excellent big data project ideas. This Big Data project is designed to analyze the tourist behaviour to identify tourists’ interests and most visited locations and accordingly, predict future tourism demands. The project involves four steps:
- Textual metadata processing to extract a list of interest candidates from geotagged pictures.
- Geographical data clustering to identify popular tourist locations for each of the identified tourist interests.
- Representative photo identification for each tourist interest.
- Time series modelling to construct a time series data by counting the number of tourists on a monthly basis.
10. Credit Scoring
This project seeks to explore the value of Big Data for credit scoring. The primary idea behind this project is to investigate the performance of both statistical and economic models. To do so, it will use a unique combination of datasets that contains call-detail records along with the credit and debit account information of customers for creating appropriate scorecards for credit card applicants. This will help to predict the creditworthiness of credit card applicants.
11. Electricity price forecasting
This is one of the interesting big data project ideas. This project is explicitly designed to forecast electricity prices by leveraging Big Data sets. The model exploits the SVM classifier to predict the electricity price. However, during the training phase in SVM classification, the model will include even the irrelevant and redundant features which reduce its forecasting accuracy. To address this problem, we will use two methods – Grey Correlation Analysis (GCA) and Principle Component Analysis. These methods help select important features while eliminating all the unnecessary elements, thereby improving the classification accuracy of the model.
BusBeat is an early event detection system that utilizes GPS trajectories of periodic-cars travelling routinely in an urban area. This project proposes data interpolation and the network-based event detection techniques to implement early event detection with GPS trajectory data successfully. The data interpolation technique helps to recover missing values in the GPS data using the primary feature of the periodic-cars, and the network analysis estimates an event venue location.
Yandex.Traffic was born when Yandex decided to use its advanced data analysis skills to develop an app that can analyze information collected from multiple sources and display a real-time map of traffic conditions in a city.
After collecting large volumes of data from disparate sources, Yandex.Traffic analyses the data to map accurate results on a particular city’s map via Yandex.Maps, Yandex’s web-based mapping service. Not just that, Yandex.Traffic can also calculate the average level of congestion on a scale of 0 to 10 for large cities with serious traffic jam issues. Yandex.Traffic sources information directly from those who create traffic to paint an accurate picture of traffic congestion in a city, thereby allowing drivers to help one another.
- Predicting effective missing data by using Multivariable Time Series on Apache Spark
- Confidentially preserving big data paradigm and detecting collaborative spam
- Predict mixed type multi-outcome by using the paradigm in healthcare application
- Use an innovative MapReduce mechanism and scale Big HDT Semantic Data Compression
- Model medical texts for Distributed Representation (Skip Gram Approach based)
Learn: Mapreduce in big data
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In this article, we have covered top big data project ideas . We started with some beginner projects which you can solve with ease. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your big data skills, you need to get your hands on these big data project ideas.
Working on big data projects will help you find your strong and weak points. Completing these projects will give you real-life experience of working as a data scientist.
If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore .
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How can one create and validate models for their projects?
To create a model, one needs to find a suitable dataset. Initially, data cleaning has to be done. This includes filling missing values, removing outliers, etc. Then, one needs to divide the dataset into two parts: the Training and the Testing dataset. The ratio of training to testing is preferably 80:20. Algorithms like Decision tree, Support Vector Machine (SVM), Linear and Logistic Regression, K- Nearest Neighbours, etc., can be applied. After training, testing is done using the testing dataset. The model's prediction is compared to the actual values, and finally, the accuracy is computed.
What is the Decision tree algorithm?
A Decision tree is a classification algorithm. It is represented in the form of a tree. The partitioning attribute is selected using the information gain, gain ratio, and Gini index. At every node, there are two possibilities, i.e., it could belong to either of the classes. The attribute with the highest value of information gain, Gini index or gain ratio is chosen as the partitioning attribute. This process continues until we cannot split a node anymore. Sometimes, due to overfitting of the data, extensive branching might occur. In such cases, pre-pruning and post-pruning techniques are used to construct the tree optimally.
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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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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
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Capstone projects show your readiness for using data science… ... Here are some ways to get more leads, either in the form of project ideas or datasets to
In spite of having tons of data available online in form of reviews, customer loyalty cards, RFID etc. the retail and wholesale industry is still lacking in
Here are the best data science project ideas with source code: ... Explore the complete implementation of Data Science Project Example – Speech Emotion
Project 3 – Drowsiness Detection using Computer Vision · Project 4 – Gesture Recognition using Computer Vision · Project 5 – Team Selection using
It involves pulling data (usually from the web) and compiling it into a usable format. While there's no shortage of great data repositories