Python for Data Analysis: Solve Real-World Challenges

Get a practical, project-based look at using Python for data analysis.

Introduction

• Introduction
• Prerequisites
• CoderPad tour

1. Case Study Introduction

• The Who, What, Where & Why
• Who
• What
• Where
• Why
• Case Study Introduction

2. Breaking down the Problem Statement

• Define the problem (the three Ds)
• Get Familiar: Domain
• Domain: Applied
• Get Familiar: Data
• Get Familiar: Deliverable
• Set the Right Expectations

3. Data Collection, Cleaning and Transformation

• Getting Setup in Codespaces
• Read from CSV
• General Cleaning Techniques
• General Cleaning Techniques: High Level Checks
• General Cleaning Techniques: Missing Values
• Data Transformations: Binning
• Solution: Prepare data for analysis

4. EDA

• Intro to EDA
• Summary Statistics
• Distributions: Histograms
• Data Transformations: Normalization and Log
• Other Distribution Types
• Data Visualizations: Comparing Categories
• Data Visualization: Data Tables
• Data Visualization: Relationships
• Create a ridgeplot
• Solution: Building summary table using pandas

5. Data Visualization & Storytelling

• Visual Best Practices: Part 1
• Visual Best Practices: Part 2
• Leverage Exploratory and Explanatory Visualizations
• Choose a medium
• Storyboarding
• Whats in a story?
• Putting it all together
• Solution: Visualizing with Matplotlib

Conclusion

• Wrap-up and next steps