IBM Data Analyst Capstone Project
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IBM Data Analyst Capstone Project
This course is part of IBM Data Analyst Professional Certificate
Instructors: Rav Ahuja
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What you'll learn
Apply techniques to gather and wrangle data from multiple sources.
Analyze data to identify patterns, trends, and insights through exploratory techniques.
Create visual representations of data using Python libraries to communicate findings effectively.
Construct interactive dashboards with BI tools to present and explore data dynamically.
Skills you'll gain
- Data Manipulation
- Business Intelligence Software
- Looker (Software)
- Data Collection
- Interactive Data Visualization
- Data Analysis
- Business Intelligence
- Data Presentation
- IBM Cognos Analytics
- Data Wrangling
- Presentations
- Statistical Visualization
- Data Visualization
- Data Cleansing
- Web Scraping
- Data Visualization Software
- Exploratory Data Analysis
- Dashboard Creation
Tools you'll learn
Details to know
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Build your Data Analysis expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from IBM
There are 6 modules in this course
In an increasingly data-centric world, the ability to derive meaningful insights from raw data is essential. The IBM Data Analyst Capstone Project gives you the opportunity to apply the skills and techniques learned throughout the IBM Data Analyst Professional Certificate. Working with actual datasets, you will carry out tasks commonly performed by professional data analysts, such as data collection from multiple sources, data wrangling, exploratory analysis, statistical analysis, data visualization, and creating interactive dashboards. Your final deliverable will include a comprehensive data analysis report, complete with an executive summary, detailed insights, and a conclusion for organizational stakeholders.
Throughout the project, you will demonstrate your proficiency in tools such as Jupyter Notebooks, SQL, Relational Databases (RDBMS), and Business Intelligence (BI) tools like IBM Cognos Analytics. You will also apply Python libraries, including Pandas, Numpy, Scikit-learn, Scipy, Matplotlib, and Seaborn. We recommend completing the previous courses in the Professional Certificate before starting this capstone project, as it integrates all key concepts and techniques into a single, real-world scenario.
In this module, you’ll apply key data collection and analysis techniques using APIs and web scraping. You’ll start by exploring HTTP requests and using APIs to retrieve and paginate job postings across different technologies. Then, you’ll work with a JSON endpoint to collect job data through API requests. Next, you’ll use web scraping techniques to download webpages, extract links and images, and gather data from HTML tables into a CSV file. By the end of this module, you’ll have hands-on experience with real-world data collection methods. You’ll also complete a graded quiz to check your understanding.
What's included
2 videos4 readings4 assignments5 app items
2 videos•Total 7 minutes
- Course Introduction•2 minutes
- Project Overview•5 minutes
4 readings•Total 40 minutes
- Prerequisites and Course Syllabus•5 minutes
- Emerging Trends in Data Analytics•10 minutes
- Project Scenario•10 minutes
- About the Dataset•15 minutes
4 assignments•Total 62 minutes
- Graded Quiz: Data Collection•30 minutes
- Checklist: Collecting Data Using APIs•10 minutes
- Checklist: Collecting Data Using Webscraping•8 minutes
- Checklist: Exploring Data•14 minutes
5 app items•Total 180 minutes
- (Optional) Lab 1: Review Of Accessing APIs•30 minutes
- Lab 2: Collecting Data Using APIs•30 minutes
- Lab 3: Review Of Web Scraping•30 minutes
- Lab 4: Collecting Data Using Web Scraping•60 minutes
- Lab 5: Exploring the Dataset•30 minutes
In this module, you will perform essential data-wrangling techniques necessary for cleaning and preparing datasets for analysis. Throughout the module, you will engage in hands-on activities to identify and handle common data issues, including duplicate entries and missing values. You will strategically remove duplicate records, apply suitable imputation strategies for missing data, and normalize datasets to ensure consistency and accuracy. Additionally, you will have a graded quizz to assess your understanding and reinforce the concepts covered.
What's included
1 reading7 assignments6 app items
1 reading•Total 5 minutes
- Assignment Overview •5 minutes
7 assignments•Total 104 minutes
- Graded Quiz: Data Wrangling•30 minutes
- Checklist: Finding Duplicates•14 minutes
- Checklist: Removing Duplicates•10 minutes
- Checklist: Finding Missing Values•16 minutes
- Checklist: Imputing Missing Values•12 minutes
- Checklist: Normalizing Data•10 minutes
- Checklist: Data Wrangling•12 minutes
6 app items•Total 240 minutes
- Lab 6: Finding Duplicates•30 minutes
- Lab 7: Removing Duplicates•30 minutes
- Lab 8: Finding Missing Values•30 minutes
- Lab 9: Impute Missing Values•60 minutes
- Lab 10: Normalizing Data•60 minutes
- Lab 11: Data Wrangling•30 minutes
In this module, you will engage in essential exploratory data analysis (EDA) techniques to uncover meaningful insights from your data set. You will start by identifying the distribution of the data through plotting distribution curves and histograms, which are crucial for understanding how values are spread across different features. Next, you will detect outliers that may skew your analysis and learn how to effectively remove them to ensure data integrity. Additionally, you will explore correlations between various features in the data set, revealing relationships that can inform your overall analysis. Finally, you will create a new DataFrame to organize and present your findings. The module includes a graded quiz to test your knowledge.
What's included
1 reading5 assignments4 app items
1 reading•Total 2 minutes
- Assignment Overview•2 minutes
5 assignments•Total 92 minutes
- Graded Quiz: Exploratory Data Analysis•30 minutes
- Checklist: Exploratory Data Analysis•22 minutes
- Checklist: Analyzing the Data Distribution•14 minutes
- Checklist: Handling Outliers•14 minutes
- Checklist: Correlation•12 minutes
4 app items•Total 120 minutes
- Lab 12: Exploratory Data Analysis•30 minutes
- Lab 13: Finding How The Data Is Distributed•30 minutes
- Lab 14: Finding Outliers•30 minutes
- Lab 15: Finding Correlation•30 minutes
In this lab, you will perform essential data visualization techniques to extract meaningful insights from the Stack Overflow survey data set. You will start by visualizing the distribution of data using histograms and box plots to understand the spread of compensation and age. Next, you will explore relationships between features through scatterplots and bubble plots, followed by examining the composition of data with pie charts and stacked charts. Additionally, you will compare data across categories using line and bar charts. The module includes a graded quizz that will assess your knowledge of these concepts, ensuring you are well prepared for further analysis in your final project.
What's included
1 reading6 assignments9 app items
1 reading•Total 2 minutes
- Assignment Overview•2 minutes
6 assignments•Total 78 minutes
- Graded Quiz: Data Visualization•30 minutes
- Checklist: Data Visualization•16 minutes
- Checklist: Visualizing Distribution of Data•8 minutes
- Checklist: Visualizing Relationship•8 minutes
- Checklist: Visualizing Composition of Data•8 minutes
- Checklist: Visualizing Comparison of Data•8 minutes
9 app items•Total 330 minutes
- Lab 16: Data Visualization•60 minutes
- Lab 17: Histograms•30 minutes
- Lab 18: Box Plots•30 minutes
- Lab 19: Scatter Plot•30 minutes
- Lab 20: Bubble Plots•30 minutes
- Lab 21: Pie Charts•30 minutes
- Lab 22: Stacked Charts•30 minutes
- Lab 23: Line Charts•60 minutes
- Lab 24: Bar Charts•30 minutes
In this module, you will create dashboards using Stack Overflow survey data using either IBM Cognos Analytics or Google Looker Studio. The assignment is divided into Part A: Building a Dashboard with IBM Cognos Analytics and Part B: Building a Dashboard with Google Looker Studio. You will design a dashboard with sections on Current Technology Usage, Future Technology Trends, and Demographics. After completing the assignment, you will be required to submit the link of the Cognos or Looker Studio dashboard you complete. The module also includes a checklist that helps you ensure you have completed all necessary tasks before moving on.
What's included
1 reading2 assignments2 plugins
1 reading•Total 10 minutes
- Assignment Overview•10 minutes
2 assignments•Total 40 minutes
- Graded Quiz: Building a Dashboard•30 minutes
- Checklist: Dashboards•10 minutes
2 plugins•Total 60 minutes
- Lab 25: Option A - Building A Dashboard With IBM Cognos Analytics•45 minutes
- Lab 26: Option B - Building A Dashboard With Google Looker Studio•15 minutes
In the final module, you will focus on presenting your data findings effectively. You will begin by exploring key elements contributing to a successful data findings report, including structuring your report, using best practices for data visualization, and presenting complex information in an engaging, accessible format. The module also includes labs covering basics in PowerPoint, foundational presentation techniques, and saving your presentation as a PDF to ensure a polished, professional final product. Finally, you will complete and submit a final presentation highlighting insights derived from the Stack Overflow Developer Survey data for evaluation through AI Grading or Peer Review.
What's included
2 videos4 readings1 peer review1 app item3 plugins
2 videos•Total 8 minutes
- Elements Of A Successful Data Findings Report•4 minutes
- Best Practices For Presenting Your Findings•3 minutes
4 readings•Total 30 minutes
- Structure Of A Report•20 minutes
- Final Project Submission Guidelines and Deliverables•5 minutes
- Congratulations and Next Steps•3 minutes
- Thanks from the Course Team•2 minutes
1 peer review•Total 60 minutes
- Option 2: Peer Graded - Final Project Submission and Evaluation•60 minutes
1 app item•Total 60 minutes
- Option 1: AI Graded - Final Project: Submission and Evaluation•60 minutes
3 plugins•Total 40 minutes
- (Optional) Lab 27: Getting Started With PowerPoint For The Web•20 minutes
- (Optional) Lab 28: Basics of PowerPoint•15 minutes
- (Optional) Lab 29: Save your PowerPoint Presentation as PDF•5 minutes
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Reviewed on Sep 26, 2021
Great. I practiced data visualization on IBM's Cognos Analytics software. It's a great piece of software. I then learned how to make a presentation to present the results of the analysis.
Reviewed on Aug 8, 2021
Course was excellent! Enjoyed the final project and being able to work with authentic data that helps understand IT career trends.
Reviewed on Jul 17, 2022
A good beginner friendly course in data analysis. Using the jupyter notebook was easier than going over to some websites to open the same jupyter notebook.
Frequently asked questions
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and support business strategies. It involves techniques such as statistical analysis, visualization, and reporting to identify trends, patterns, and insights from datasets.
When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings, and assignments anytime and anywhere through the web or your mobile device.
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