As businesses are increasingly moving toward data-driven decision-making, the ability to derive meaningful insights from raw data is becoming all the more 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. You will be working on a project simulating a real-life scenario with actual datasets. The project would involve carrying 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.

Throughout the project, you will demonstrate your proficiency in tools such as Jupyter Notebooks, SQL, and Relational Databases Management System (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.

Your final deliverable will be a comprehensive data analysis report that includes an executive summary, detailed insights, and a conclusion for organizational stakeholders.

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.

Course Learning Objectives

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.

Course Syllabus

Welcome to the Course

Video: Course Introduction
Reading: General Information: About the course
Reading: Course Learning Objectives and Course Syllabus
Video: Project Overview
Reading: Project Scenario
Reading: Grading Scheme

Module 1: Data Collection
Collecting Data Using APIs

Reading: Module 1 Introduction and Learning Objectives
(Optional) Lab 1: Review Of Accessing APIs
Reading: API Access Details
Lab 2: Collecting Data Using APIs
Checklist: Collecting Data Using APIs

Collecting Data Using Webscraping

Lab 3: Review Of Web Scraping
Lab 4: Collecting Data Using Web Scraping
Checklist: Collecting Data Using Webscraping

Exploring Data

Reading: About the Dataset
Lab 5: Exploring the Dataset
Checklist: Exploring Data
Graded Quiz: Data Collection

Module 2: Data Wrangling

Finding Duplicates

Reading: Module 2 Introduction and Learning Objectives
Reading: Assignment Overview
Lab 6: Finding Duplicates
Checklist: Finding Duplicates

Removing Duplicates

Lab 7: Removing Duplicates
Checklist: Removing Duplicates

Finding Missing Values

Lab 8: Finding Missing Values
Checklist: Finding Missing Values

Imputing Missing Values

Lab 9: Impute Missing Values
Checklist: Imputing Missing Values

Normalizing Data

Lab 10: Normalizing Data
Checklist: Normalizing Data

Assignment Overview

Lab 11: Data Wrangling
Checklist: Data Wrangling
Graded Quiz: Data Wrangling

Module 3: Exploratory Data Analysis

Assignment Overview

Reading: Module 2 Introduction and Learning Objectives
Reading: Assignment Overview
Lab 12: Exploratory Data Analysis
Checklist: Exploratory Data Analysis

Analyzing the data distribution

Lab 13: Finding How The Data Is Distributed
Checklist: Analyzing the data distribution

Handling Outliers

Lab 14: Finding Outliers
Checklist: Handling Outliers

Correlation

Lab 15: Finding Correlation
Checklist: Correlation
Graded Quiz: Exploratory Data Analysis

Module 4: Data Visualization

Assignment Overview

Reading: Module 4 Introduction and Learning Objectives
ReadIng: Assignment Overview
Lab 16: Data Visualization
Checklist: Data Visualization

Visualizing Distribution Of Data

Lab 17: Histograms
Lab 18: Box Plots
Checklist: Visualizing Distribution Of Data

Visualizing Relationship

Lab 19: Scatter Plot
Lab 20: Bubble Plots
Checklist: Visualizing Relationship

Visualizing Composition of Data

Lab 21: Pie Charts
Lab 22: Stacked Charts
Checklist: Visualizing Composition of Data

Visualizing Comparison of Data

Lab 23: Line Charts
Lab 24: Bar Charts
Checklist: Visualizing Comparison of Data
Graded Quiz: Data Visualization

Module 5: Building A Dashboard

Assignment Overview

Reading: Module 5 Introduction and Learning Objectives
Reading: Assignment Overview

Dashboards

Lab 25: Option A - Building A Dashboard With IBM Cognos Analytics
Lab 26: Option B - Building A Dashboard With Google Looker Studio
Checklist: Dashboards
Graded Quiz: Building a Dashboard

Module 6: Final Assignment: Present Your Findings

How To Present Your Findings

Reading: Module 6 Introduction and Learning Objectives
Video: Elements Of A Successful Data Findings Report
Lab: Structure Of A Report
Video: Best Practices For Presenting Your Findings
(Optional) Lab 27: Getting Started With PowerPoint For The Web
(Optional) Lab 28: Basics of PowerPoint
(Optional) Lab 29: Save your PowerPoint Presentation as PDF

Final Presentation

Reading: Assignment Overview
Exercise: Preparing Your Presentation
Peer-graded Assignment

Course Wrap Up

Congratulations and Next Steps
Thanks from the Course Team
Copyrights and Trademark

Recommended Skills Prior to Taking this Course

This course requires you to be proficient in data analysis and have experience using SQL, Relational Databases, performing data collection, data wrangling, data analysis, & data visualization with Python libraries, and using a BI tool like IBM Cognos Analytics or Google Looker. I

IBM Data Analytics and Visualization Capstone Project

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