Contents

Think Data Science and Management has 15 chapters in five parts, followed by the appendices and bibliography. Chapter 1 introduces data science and programming; set up your tools with the appendices before the first lab. Chapter Topics summarizes what each chapter teaches.

Part I. Fundamentals

Data science as a field and Python as its working language.

• Introduction

• Python Basics

Part II. Working with Data

Arrays and tables: the two data structures the rest of the book builds on.

• NumPy Arrays

• Pandas

Part III. Data Visualization

Telling stories with data in pandas, Matplotlib, and Seaborn.

• Visualization

• Matplotlib

• Seaborn

Part IV. Inferential Statistics

Probability, simulation, hypothesis tests, estimation, and regression.

• Probability and Statistics

• Hypothesis Testing

• Two Samples

• Estimation

• Regression

Part V. Machine Learning

Predicting and grouping with models: regression, classification, and clustering.

• Multiple Regression

• Classification

• Clustering

Appendices

Tool setup for the course, then the bibliography.

• Course Tooling

• Jupyter Setup

• Bibliography