---
title: 'Data Analysis'
date: '2026-09-04'
excerpt: 'Learn Data Analysis with Python.'
tags: ['data-analysis', 'python', 'machine-learning']
---

## Course overview

Data Analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Learn to explore data, fit regressions, predict with machine learning, and reason about cause and effect — all in Python.

## Chapters

Each chapter provides summaries, outline, slides, and case study links.

| Part | Ch. | Title |
| --- | --- | --- |
| **I: Data Exploration** | 01 | Origins of Data |
| | 02 | Preparing Data for Analysis |
| | 03 | Exploratory Data Analysis |
| | 04 | Comparison and Correlation |
| | 05 | Generalizing from Data |
| | 06 | Testing Hypotheses |
| **II: Regression Analysis** | 07 | Simple Regression |
| | 08 | Complicated Patterns and Messy Data |
| | 09 | Generalizing Results of a Regression |
| | 10 | Multiple Linear Regression |
| | 11 | Modeling Probabilities |
| | 12 | Regression with Time Series Data |
| **III: Prediction** | 13 | A Framework for Prediction |
| | 14 | Model Building for Prediction |
| | 15 | Regression Trees |
| | 16 | Random Forest and Boosting |
| | 17 | Probability Prediction and Classification |
| | 18 | Forecasting from Time Series Data |
| **IV: Causal Analysis** | 19 | A Framework for Causal Analysis |
| | 20 | Designing and Analyzing Experiments |
| | 21 | Regression and Matching with Observational Data |
| | 22 | Difference-in-Differences |
| | 23 | Methods for Panel Data |
| | 24 | Appropriate Control Groups for Panel Data |