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This course is part of the IBM Data Analyst Professional Certificate
IBM Data Analyst Capstone Project
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About this Course
By completing this final capstone project you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of an Associate Data Analyst who has recently joined the organization and be presented with a business challenge that requires data analysis to be performed on real-world datasets.
You will perform the various tasks that professional data analysts do as part of their jobs, including: - Data collection from multiple sources - Data wrangling and data preparation - Exploratory data analysis - Statistical analysis and data mining - Data visualization with different charts and plots, and - Interactive dashboard creation. The project will culminate with a presentation of your data analysis report for various stakeholders in the organization. The report will include an executive summary, your analysis, and a conclusion. You will be assessed on both your work for the various stages in the Data Analysis process, as well as the final deliverable. As part of this project you will demonstrate your proficiency with using Jupyter Notebooks, SQL, Relational Databases (RDBMS), Business Intelligence (BI) tools like Cognos, and Python Libraries such as Pandas, Numpy, Scikit-learn, Scipy, Matplotlib, Seaborn and others. This project is a great addition to your portfolio and an opportunity to showcase your Data Analytics skills to prospective employers.
It is highly recommend that you have completed all of the courses in the IBM Data Analyst Professional Certificate prior to starting this course.
Could your company benefit from training employees on in-demand skills?
What you will learn
Apply different techniques to collect and wrangle data
Showcase your Data Analysis and Visualization skills
Create a data analysis report and a compelling presentation
Demonstrate proficiency with various Python Libraries
Skills you will gain
- Python Programming
- Data Analysis
- SQL and RDBMS
- Data Visualization (DataViz)
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
For more information about IBM visit: www.ibm.com
See how employees at top companies are mastering in-demand skills
Syllabus - What you will learn from this course
Data Collection is the first step in solving any analysis problem and can be collected in many formats and from many sources. In the first module of the Capstone, we will collect data by scraping the internet and using web APIs.
In this module, you will be focusing on the cleaning of your dataset with various techniques. With these techniques you will be identifying duplicate rows, finding missing values, and normalizing the data.
Exploratory Data Analysis
In this module, begin working with the cleaned dataset from the previous module. You will now begin to analyze the dataset to find the distribution of data, presence of outliers and the correlation between different columns.
In module 4 of the Capstone, you will be required to create visualizations using the developer survey data. The visualizations you create should highlight the distribution of data, relationships between data, the composition of data, and comparison of data.
Building A Dashboard
In this module, you will create a dashboard using IBM Cognos Analytics. This platform will give you the ability to create various charts while assembling a dashboard that is appealing and easy to understand. Your dashboard will contain your data analysis, which should be intuitive and allow for the drill-down of data.
Final Assignment: Present Your Findings
You have analyzed the data in the previous modules, and now it is time to demonstrate your storytelling skills. In this module, you will create a compelling story that helps to clarify your analysis in an easy-to-understand presentation.
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TOP REVIEWS FROM IBM DATA ANALYST CAPSTONE PROJECT
I love its video and reading materials, thank you all for making such course, i can definitely make a career out of it, i just recommend you all to give you time in this course.
I consider that the Capstone project is well designed. It is challenging to complete, making it rewarding. Thank you.
Comprehensive and straightforward. Love every aspect especially the labs and peer reviews.
The course is great and the tools used in this course are those widely used by data analysts in real life.
About the IBM Data Analyst Professional Certificate
Gain the job-ready skills for an entry-level data analyst role through this Professional Certificate from IBM and position yourself competitively in the thriving job market for data analysts, which will see a 20% growth until 2028 (U.S. Bureau of Labor Statistics).
Power your data analyst career by learning the core principles of data analysis and gaining hands-on skills practice. You’ll work with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics, gaining practical experience with data manipulation and applying analytical techniques. This Professional Certificate does not require any prior programming or statistical skills, and is suitable for learners with or without college degrees. All you need to get started is basic computer literacy, high school math, comfort working with numbers, willingness to learn, and a desire to enrich your profile with valuable skills. Upon successful completion of this program, you’ll have analyzed real-world datasets, created interactive dashboards, and presented reports to share your findings, giving you the confidence and the portfolio to begin a career as an associate or junior data analyst. You’ll also build the foundation for other data disciplines such as data science or data engineering. This program is ACE® recommended—when you complete, you can earn up to 12 college credits.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
More questions? Visit the Learner Help Center .
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Ibm-data-analyst-capstone-project, documents and data used to create my capstone project for ibm data analyst professional certificate (completed january 2022), capstone project objective.
You have recently been hired as a Data Analyst by a global IT and business consulting services firm that is known for their expertise in IT solutions and their team of highly experienced IT consultants. In order to keep pace with changing technologies and remain competitive, your organization regularly analyzes data to help identify future skill requirements. As a Data Analyst, you will be assisting with this initiative and have been tasked with collecting data from various sources and identifying trends for this year's report on emerging skills. Your first task is to collect the top programming skills that are most in demand from various sources including: Job postings, Training portals and Surveys.
Once you have collected enough data, you will begin analyzing the data and identify insights and trends that may include the following: What are the top programming languages in demand? What are the top database skills in demand? What are the popular IDEs?
You will begin by scraping internet web sites and accessing APIs to collect data in various formats like .csv files, excel sheets, and databases. Once this is completed, you will make that data ready for analysis using data wrangling techniques. When the data is ready you will then want to apply statistical techniques to analyze the data. Then bring all of your information together by using IBM Cognos Analytics to create your dashboard. And finally, show off your storytelling skills by sharing your findings in a presentation. You will be evaluated using quizzes in each module as well as the final project presentation.
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Applied Data Science Capstone Cousera Answers | IBM Data Science Professional Certifications
Hello Peers, Today we are going to share all week assessment and quizzes answers of Applied Data Science Capstone , the IBM Data Science Professional course launched by Coursera for totally free of cost✅✅✅. This is a certification course for every interested student.
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Coursera , India’s biggest learning platform which launched millions of free courses for students daily. These courses are from various recognized universities, where industry experts and professors teach in a very well manner and in a more understandable way.
Here, you will find Applied Data Science Capstone Exam Answers in Bold Color which are given below.
These answers are updated recently and are 100% correct ✅ answers of all week, assessment and final exam answers of Applied Data Science Capstone from Coursera Free Certification Course.
The Battle of Neighborhoods
In this module, you will start working on the capstone project. You will clearly define a problem and discuss the data that you will be used to solve the problem.
- Define a problem for your capstone project.
- Finding the data that you will use for the capstone project.
Clearly define a problem or an idea of your choice, where you would need to leverage the Foursquare location data to solve or execute. Remember that data science problems always target an audience and are meant to help a group of stakeholders solves a problem, so make sure that you explicitly describe your audience and why they would care about your problem.
This submission will eventually become your Introduction/Business Problem section in your final report. So I recommend that you push the report (having your Introduction/Business Problem section only for now) to your Github repository and submit a link to it.
Describe the data that you will be used to solve the problem or execute your idea. Remember that you will need to use the Foursquare location data to solve the problem or execute your idea. You can absolutely use other datasets in combination with the Foursquare location data. So make sure that you provide adequate explanation and discussion, with examples, of the data that you will be using, even if it is only Foursquare location data.
This submission will eventually become your Data section in your final report. So I recommend that you push the report (having your Data section) to your Github repository and submit a link to it.
A Tale of Two cities – Clustering the Neighbourhoods of London and Paris
A Tale of Two cities, a novel written by Charles Dickens was set in London and Paris which takes place during the French Revolution. These cities were both happening then and now. A lot has changed over the years and we now take a look at how the cities have grown.
London and Paris are quite a popular tourist and vacation destinations for people all around the world. They are diverse and multicultural and offer a wide variety of experiences that are widely sought after. We try to group the neighborhoods of London and Paris respectively and draw insights into what they look like now.
2. Business Problem
The aim is to help tourists choose their destinations depending on the experiences that the neighborhoods have to offer and what they would want to have. This also helps people make decisions if they are thinking about migrating to London or Paris or even if they want to relocate neighborhoods within the city. Our findings will help stakeholders make informed decisions and address any concerns they have including the different kinds of cuisines, provision stores, and what the city has to offer.
3. Data Description
We require geographical location data for both London and Paris. Postal codes in each city serve as a starting point. Using Postal codes we use can find out the neighborhoods, boroughs, venues, and their most popular venue categories.
To derive our solution, We scrape our data from https://en.wikipedia.org/wiki/List_of_areas_of_London
This wikipedia page has information about all the neighborhoods, we limit it to London.
- borough : Name of Neighbourhood
- town : Name of borough
- post_code : Postal codes for London.
This wikipedia page lacks information about the geographical locations. To solve this problem we use ArcGIS API
3.2 ArcGIS API
ArcGIS Online enables you to connect people, locations, and data using interactive maps. Work with smart, data-driven styles and intuitive analysis tools that deliver location intelligence. Share your insights with the world or specific groups.
More specifically, we use ArcGIS to get the geo locations of the neighborhoods of London. The following columns are added to our initial dataset which prepares our data.
- latitude : Latitude for Neighbourhood
- longitude : Longitude for Neighbourhood
To derive our solution, We leverage JSON data available at https://www.data.gouv.fr/fr/datasets/r/e88c6fda-1d09-42a0-a069-606d3259114e
The JSON file has data about all the neighborhoods in France, we limit it to Paris.
- postal_code : Postal codes for France
- nom_comm : Name of Neighbourhoods in France
- nom_dept : Name of the boroughs, equivalent to towns in France
- geo_point_2d : Tuple containing the latitude and longitude of the Neighbourhoods.
3.4 Foursquare API Data
We will need data about different venues in different neighbourhoods of that specific borough. In order to gain that information, we will use “Foursquare” locational information. Foursquare is a location data provider with information about all manner of venues and events within an area of interest. Such information includes venue names, locations, menus, and even photos. As such, the foursquare location platform will be used as the sole data source since all the stated required information can be obtained through the API.
After finding the list of neighborhoods, we then connect to the Foursquare API to gather information about venues inside each and every neighborhood. For each neighbourhood, we have chosen the radius to be 500 meters.
The data retrieved from Foursquare contained information of venues within a specified distance of the longitude and latitude of the postcodes. The information obtained per venue is as follows:
- Neighbourhood : Name of the Neighbourhood
- Neighbourhood Latitude : Latitude of the Neighbourhood
- Neighbourhood Longitude : Longitude of the Neighbourhood
- Venue : Name of the Venue
- Venue Latitude : Latitude of Venue
- Venue Longitude : Longitude of Venue
- Venue Category : Category of Venue
Based on all the information collected for both London and Paris, we have sufficient data to build our model. We cluster the neighbourhoods together based on similar venue categories. We then present our observations and findings. Using this data, our stakeholders can take the necessary decision.
We will be creating our model with the help of Python so we start off by importing all the required packages.
- Pandas : To collect and manipulate data in JSON and HTMl and then data analysis
- requests : Handle http requests
- matplotlib : Detailing the generated maps
- folium : Generating maps of London and Paris
- sklearn : To import Kmeans which is the machine learning model that we are using.
The approach taken here is to explore each of the cities individually, plot the map to show the neighborhoods being considered, and then build our model by clustering all of the similar neighborhoods together and finally plot the new map with the clustered neighborhoods. We draw insights and then compare and discuss our findings.
4.1 Data Collection
In the data collection stage, we begin with collecting the required data for the cities of London and Paris. We need data that has the postal codes, neighborhoods, and boroughs specific to each of the cities.
To collect data for London, we scrape the List of areas of London wikipedia page to take the 2nd table using the following code:
The data looks like this:
To collect data for Paris, we download the JSON file containg all the postal codes of France from https://www.data.gouv.fr/fr/datasets/r/e88c6fda-1d09-42a0-a069-606d3259114e
Using Pandas we load the table after reading the JSON file:
4.2 Data Preprocessing
For London, We replace the spaces with underscores in the title.The borough column has numbers within square brackets that we remove using:
For Paris, we break down each of the nested fields and create the dataframe that we need:
4.3 Feature Selection
For both of our datasets, we need only the borough, neighbourhood, postal codes and geolocations (latitude and longitude). So we end up selecting the columns that we need by:
4.4 Feature Engineering
Both of our Datasets actually contain information related to all the cities in the country. We can narrow down and further process the data by selecting only the neighbourhoods pertaining to ‘London’ and ‘Paris’
Looking over our London dataset, we can see that we don’t have the geolocation data. We need to extrapolate the missing data for our neighbourhoods. We perform this by leveraging the ArcGIS API . With the Help of ArcGIS API we can get the latitude and longitude of our London neighbourhood data.
Defining London arcgis geocode function to return latitude and longitude
Passing postal codes of london to get the geographical co-ordinates
We proceed with Merging our source data with the geographical co-ordinates to make our dataset ready for the next stage
As for our Paris dataset, we don’t need to get the geo coordinates using an external data source or collect it with the ArcGIS API call since we already have it stored in the geo_point_2d column as a tuple in the df_paris dataframe.
We just need to extract the latitude and longitude for the column:
We then create our Paris dataset with the required information:
Note: Both the datasets have been properly processed and formatted. Since the same steps are applied to both the datasets of London and Paris, we will be discussing the code for only the London dataset for simplicity.
4.5 Visualizing the Neighbourhoods of London and Paris
Now that our datasets are ready, using the Folium package, we can visualize the maps of London and Paris with the neighbourhoods that we collected.
Neighbourhood map of London:
Neighbourhood map of Paris:
Now that we have visualized the neighbourhoods, we need to find out what each neighbourhood is like and what are the common venue and venue categories within a 500m radius.
This is where Foursquare comes into play. With the help of Foursquare we define a function which collects information pertaining to each neighbourhood including that of the name of the neighbourhood, geo-coordinates, venue and venue categories.
Resulting data looks like:
4.6 One Hot Encoding
Since we are trying to find out what are the different kinds of venue categories present in each neighbourhood and then calculate the top 10 common venues to base our similarity on, we use the One Hot Encoding to work with our categorical datatype of the venue categories. This helps to convert the categorical data into numeric data.
We won’t be using label encoding in this situation since label encoding might cause our machine learning model to have a bias or a sort of ranking which we are trying to avoid by using One Hot Encoding.
We perform one hot encoding and then calculate the mean of the grouped venue categories for each of the neighbourhoods.
4.7 Top Venues in the Neighbourhoods
In our next step, We need to rank and label the top venue categories in our neighborhood.
Let’s define a function to get the top venue categories in the neighbourhood
There are many categories, we will consider top 10 categories to avoid data skew.
Defining a function to label them accurately
Getting the top venue categories in the neighbourhoods of London
4.8 Model Building – KMeans
Moving on to the most exicitng part – Model Building! We will be using KMeans Clustering Machine learning algorithm to cluster similar neighbourhoods together. We will be going with the number of clusters as 5.
Our model has labelled each of the neighbourhoods, we add the label into our dataset.
We then join London_merged with our neighbourhood venues sorted to add latitude & longitude for each of the neighborhood to prepare it for visualization.
4.9 Visualizing the clustered Neighbourhoods
Our data is processed, missing data is collected and compiled. The Model is built. All that’s remaining is to see the clustered neighbourhoods on the map. Again, we use Folium package to do so.
We drop all the NaN values to prevent data skew
Map of clustered neighbourhoods of London:
Map of clustered neighbourhoods of Paris
4.9.1 Examining our Clusters
We could examine our clusters by expanding on our code using the Cluster Labels column:
5. Results and Discussion
The neighbourhoods of London are very mulitcultural. There are a lot of different cusines including Indian, Italian, Turkish and Chinese. London seems to take a step further in this direction by having a lot of restaurants, bars, juice bars, coffee shops, Fish and Chips shops, and Breakfast spots. It has a lot of shopping options too with that of the Flea markets, flower shops, fish markets, Fishing stores, clothing stores. The main modes of transport seem to be Buses and trains. For leisure, the neighborhoods are set up to have lots of parks, golf courses, zoos, gyms, and Historic Sites. Overall, the city of London offers a multicultural, diverse, and certainly entertaining experience.
Paris is relatively small in size geographically. It has a wide variety of cusines and eateries including French, Thai, Cambodian, Asian, Chinese, etc. There are a lot of hangout spots including many Restaurants and Bars. Paris has a lot of Bistros. Different means of public transport in Paris include buses, bikes, boats or ferries. For leisure and sightseeing, there are a lot of Plazas, Trails, Parks, Historic sites, clothing shops, Art galleries, and Museums. Overall, Paris seems like a relaxing vacation spot with a mix of lakes, historic spots, and a wide variety of cusines to try out.
The purpose of this project was to explore the cities of London and Paris and see how attractive it is to potential tourists and migrants. We explored both the cities based on their postal codes and then extrapolated the common venues present in each of the neighborhoods finally concluding with clustering similar neighborhoods together.
We could see that each of the neighborhoods in both the cities has a wide variety of experiences to offer which is unique in its own way. The cultural diversity is quite evident which also gives the feeling of a sense of inclusion.
Both Paris and London seem to offer a vacation stay or a romantic getaway with a lot of places to explore, beautiful landscapes, amazing food, and a wide variety of cultures. Overall, it’s upto the stakeholders to decide which experience they would prefer more and which would more to their liking.
Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of Applied Data Science With Capstone Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also and follow our Techno-RJ Blog for more updates.
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Google Data Analytics Certification vs IBM Data Analyst- Which is Better?
Do you want to know which is better Google Data Analytics or IBM Data Analyst? If yes, then this comparison of Google Data Analytics Certification vs IBM Data Analyst Certification will clear your doubts.
I have compared both programs on these criteria- Projects, Topics, Content Quality, Rating, and support provided. And at the end of this article, you will get my final recommendation on whether you should go with the Google Data Analyst Certificate or the IBM Data Analyst Certificate.
So read this comparison, Google Data Analytics Certification vs IBM Data Analyst , and then decide which one is better for you.
Google Data Analytics Certification vs IBM Data Analyst
Comparison between google data analytics and ibm data analyst, topics covered in google data analytics certification program.
- Topics covered in IBM Data Analyst Professional certificate
Pros and Cons of Google Data Analytics Certification
Pros and cons of ibm data analyst.
- My Recommendation: Google Data Analytics Certification vs IBM Data Analyst- Which One is Better?
Before we dive into projects and topics covered in both programs , let’s have a quick comparison between both certification programs-
So, this is a quick comparison of Google Data Analytics Certification vs IBM Data Analyst . Now let’s see the topics covered in both Certification programs-
In Google Data Analytics Certification, there are 8 courses . So let’s see the details of each course.
Course 1- Foundations: Data, Data, Everywhere
This is the first course of the Google Data Analytics Certification . In this course, data analysts will introduce to the course and then they will discuss data analytics roles and responsibilities. You will also learn six phases of data analysis. At the end of this course, you will listen to data analysts about their experiences and understand the interview best practices.
Pros of this Course-
- The course is combined with various practice exercises.
- Along with video tutorials, you will get extra reading resources.
- A good course for complete beginners.
Cons of this Course-
- Very basic course.
- At beginning of the course, the instructor doesn’t cover any technical concepts.
Course 2- Ask Questions to Make Data-Driven Decisions
In the second course, you will learn how to solve problems by asking effective and correct questions. Spreadsheet basics are also covered in this course. You will learn Formulas in spreadsheets and how to use a spreadsheet. At the end of this course, you will learn about appropriate communication with stakeholders. The instructor will provide tips for effective communication.
- The course will help you to learn about Spreadsheets and Data Communication.
- Throughout the course, there are various practical exercises.
- This course might be boring for you if you already have some previous experience in data analytics.
Course 3- Prepare Data for Exploration
The third course is about Data Exploration. In this course, you will learn everything about data such as what is structured data and unstructured data, how to ensure data integrity, what is bias and unbiased data, etc. The instructor also explains databases and how to import data from spreadsheets and databases.
- This is a good course to revise your SQL concepts and understand data.
- You will work on ungraded projects in this course.
- The course is more theoretical.
Course 4- Process Data from Dirty to Clean
This course will teach about data cleaning . Data Cleaning is an essential step in data analytics and this course will cover data cleaning tools and techniques. You will learn how to clean data using spreadsheets and SQL. The instructor also explains the differences between spreadsheets and SQL.
- There are hands-on practices in this course for data cleaning.
- The instructor explains the topics very well.
- This course should have more hands-on exercises. Because Data Cleaning can’t be taught by theory. It requires practice.
Course 5- Analyze Data to Answer Questions
In this course, you will learn data analysis . But before teaching data analysis, the instructor explains data organization and data formatting. You will learn about the SORT function . After that, you will learn how to perform data calculations and understand common calculation formulas. In this course, the instructor explains how to work on a pivot table. And there is one hands-on practice on exploring movie data with pivot tables.
- This course is good for understanding how to perform data analysis using SQL and spreadsheets.
- This course focuses on the practical part, which is good.
- Some of the topics are very advanced and some topics are very basic. Which might confuse the students.
Course 6- Share Data Through the Art of Visualization
After analyzing your data, you must know how to graphically represent your data . And this course is all about data visualization. In this course, you will learn all about Tableau and how to use Tableau to create dashboards and dashboard filters. You will also learn how to craft a story with data and how to prepare a presentation to showcase your findings.
- The course covers Tableau for Data Visualization, which is helpful.
- There are some hands-on exercises on Tableau.
- Covers Presentation skills.
- The course doesn’t cover advanced concepts of Tableau.
Course 7- Data Analysis with R Programming
In this course, you will learn R programming , RStudio , and fundamental concepts associated with R such as functions, variables, and various R packages. Then you will learn data organization and data cleaning using R Programming. At the end of this course, you will learn the basics of data visualization using R and tidyverse . ggplot() is also explained in this course. ggplot() is a data visualization library in R programming.
- This course has more hands-on practice and exercises.
- Good course to learn R basics for beginners.
- The instructor’s explanation is perfect.
- If you already know R Programming, you might feel that exercises are easy.
Course 8- Google Data Analytics Capstone: Complete a Case Study
This is the last course of Google data Analytics Certification. In this course, there is a case study that you have to complete. In this capstone project, you have to use all the concepts learned throughout the course. You can select the dataset of your choice. You will also get interview tips and guidance such as how to discuss your portfolio and highlight specific skills in interview scenarios.
- This Capstone Case Study will help you in your interview.
- You will learn how to build your portfolio.
- If you are stuck in this capstone, you have to figure it out by yourself.
So this is all about Google Data Analytics Certification .
Check Google Data Analytics Certification
Now let’s see the topics covered by IBM Data Analyst .
Topics covered in IBM Data Analyst Professional certificate
Course 1- introduction to data analytics.
The first course provides an introduction to data analytics by using real-world examples. You will understand the responsibilities of a Data Analyst. This course will also cover Data ecosystems and Data Repositories such as RDBMS, NoSQL, etc. Data Wrangling and Data Cleaning are covered in the first course, which is good. The instructor of this course provides a basic introduction to data mining and data visualization too.
- If I compare it to the Google Data Analytics first course, this course is more informative.
- This course covered more technical terms in this first course than Google Data Analytics’ first course.
- The course covers the theoretical part only.
Course 2- Excel Basics for Data Analysis
This course is focused on teaching Excel Basics. You can use Excel for performing Data Analysis tasks. You will learn Spreahseets basics in this course. After that, you will understand how to perform data wrangling and cleaning using Excel. At the end of this course, you will understand how to use VLOOKUP and HLOOKUP Functions .
- This course has graded and practice quizzes.
- There is one project in this course cleaning and analyzing the data using Excel.
- If you already know Excel, this course might bore you.
Course 3- Data Visualization and Dashboards with Excel and Cognos
This course is short and more focused on hands-on practice . In this course, you will gain a basic understanding of Data Visualization. The instructor covers how to create Treemaps, Scatter Charts, and Histograms . After that, you will learn Cognos Analytics . There is one project in this course where you have to perform data visualization using Excel and Cognos.
- The structure of this course is easy to follow.
- Good course to learn Cognos.
- The course doesn’t cover in-depth concepts. It is a basic introduction-type course.
Course 4- Python for Data Science and AI
This is the same course available in IBM Data Science Professional Certificate . In this course, you will learn Python basics, Data Structure , and the two popular Python libraries Pandas and Numpy. At the end of this course, the instructor explains APIs such as Simple APIs , REST APIs & HTTP Requests, etc.
- Along with video tutorials, there are various reading materials available.
- The final project on IBM Watson is not clear.
Course 5- Python Project for Data Science
There is one project course in between the certificate program. In this project, you have to test your understanding of previously learned Python concepts. This project force you to explore and experiment with your knowledge. This is not an easy project for beginners.
- Help you to understand how to work on real-world problems by yourself.
- Very few details are given by IBM for the project. That’s why you have to figure it out by yourself.
Course 6- Databases and SQL for Data Science with Python
SQL knowledge is essential for data analysts and data scientists. And this course will teach you SQL basics and different types of SQL statements. This course also covers some advanced concepts of SQL such as how to work with multiple tables , Sub-Queries and Nested Selects, etc. After that, the instructor explains how to connect a database using Python. At the end of this course, you will work on the 1-course assignment.
- The quizzes of this course help you to practice more.
- There is a bonus section that covers advanced SQL.
- The course has some technical issues for eg working on IBM Db2 Cloud is time-consuming.
Course 7- Data Analysis with Python
In this course, you will learn Python libraries available for data analysis. You will also learn how to import and export the data using Python. In short, this course will teach you the complete data analysis process starting from data processing to model evaluation using Python. At the end of this course, there is one project , where the dataset will be provided and you have to perform data analysis from scratch.
- You will learn statistical concepts in this course.
- This course has practice and graded quizzes.
- The course material lacks in quality and needs improvement.
Course 8- Data Visualization with Python
Matplotlib is a data visualization library in Python. And in this course, you will learn how to create data visualization using Matplotlib. This course also covers Area Plots , Histograms , Bar Charts , Pie Charts , Box Plots , and Scatter Plots . Some advanced techniques of data visualization are also explained in this course. You will learn about Plotly and Dash.
- The course has one project to test the understanding.
- There are practical exercises and quizzes in this course.
- The course quizzes have errors.
Course 9- IBM Data Analyst Capstone Project
This is the last course of this certification program. This is the Capstone project. In this capstone project, you have to perform all the tasks related to data analysis starting from Data Collection . For data collection, you have to scrape the data using web APIs.
After data collection, you have to perform data wrangling and clean the data. Next, you have to analyze the data. And after analyzing your data, you have to showcase your results by using data visualization skills.
- This Capstone project will help you in your portfolio.
- You can showcase this project at the time of the interview.
- And this project help you to revise all the concepts learned in this program.
- You have to complete this project by yourself. No support will be provided.
Check IBM Data Science Professional Certificate
You know what topics are covered in both certification programs. Now let’s see the Pros and Cons of both programs-
- You will learn from real data analysts and understand real-world problems with them.
- After completing the course, you will get access to Google’s network of over 130 different employers that are looking for people with this certificate.
- You will gain an immersive understanding of the practices and processes used by data analysts in their day-to-day job.
- You will learn the most popular tool, Tableau , for data visualization.
- Google Data Analyst certification spends a lot of time setting you up for the job after the certificate.
- The first course is very introductory and basic.
- In the data visualization part, there should be more Tableau practice exercises.
- The content of this program is well structured and provides a foundational base in data analytics.
- Perfect program for novices to gain hands-on skills and practice by working with a variety of data sources, project scenarios, and data analysis tools like Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics .
- The final assignment of the course Data Visualization with Python is hard to complete because the instructor didn’t explain too much about the assignment concepts.
- Python for Data Science and AI is not a well-sequenced and complete course to learn Python. You need some python textbooks to supplement your learning.
Now it’s time to know which one is better for you.
My Recommendation: Google Data Analytics Certification vs IBM Data Analyst – Which One is Better?
I recommend Google Data Analytics Certification .
Because it has a network established of employers that are already going to accept this certificate in place of a degree equivalent, another reason is that you will learn from data analysts and see what problems they are facing as data analysts daily and get to know about their experiences while learning.
A capstone project is the next reason. Because in Google Data Analytics Certification, you can choose your own dataset to build the capstone project. Along with that, you will also get interview tips and guidance, which is not provided by IBM Data Analyst Certification.
But, it doesn’t mean that IBM data analyst is not good. Especially if you are looking for a course in your native language or you want to learn Data Analytics using Python . But if we compare it with Google Data Analytics Certification , then Google Data Analytics Certification is much better than IBM Data Analyst.
Start Learning with Google Data Analytics Certification
Now you got your answer.
I hope this Google Data Analytics Certification vs IBM Data Analyst comparison has cleared your doubts and you can easily choose the one which suits you. If you have any questions, feel free to ask me in the comment section. I am here to help you. And If you found this article helpful, share it with others to help them too.
All the Best!
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Contribute to rauljrz/IBM-Data-Analyst-Capstone-Project development by creating an account on GitHub. As a Data Analyst, you will be assisting with this initiative and have been tasked with collecting data from
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In this course you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate
The earner has successfully completed 8 courses in Data Analytics and gained hands-on experience with data analysis tools, including Excel, SQL, Databases, Python, JupyterLab and Cognos
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If yes, then this comparison of Google Data Analytics Certification vs IBM Data Analyst Certification will clear your doubts. And at the end of this article, you will get my final recommendation on whether you