# 3 Data Science

## 3.1 Elements of Data Science

An introduction to data science designed for people with no programming experience, this book presents a small, powerful subset of Python that allows you to do real work in data science as quickly as possible. It includes Jupyter notebooks where you can read the text, run the code, and work on exercises to practice what you learn.

https://allendowney.github.io/ElementsOfDataScience/README.html

## 3.2 GEOG 30323: Data Analysis & Visualization

Dr. Kyle Walker at Texas Christian University

This is a course in exploratory data analysis with an emphasis on the Python programming language and PyData ecosystem. The course targets students from all disciplines with an interest in data analytics, but does not assume any prior experience with computer programming.

Course assignments can be viewed from the sidebar menu. To “launch” a assignment in Binder or Google Colab, click the rocket icon in the top left of the screen and choose your platform.

## 3.3 Python Data Science Handbook

The Python Data Science Handbook by Jake VanderPlas (O’Reilly Media, 2016). This is a comprehensive introduction to the most important data science tools in the Python world. Several examples used in the book are drawn from posts on this blog. The full text can be read online, and the content is also available as Jupyter notebooks on GitHub.

## 3.4 Think Bayes 2e: Bayesian Statistics in Python

An introduction to Bayesian statistics using simple Python programs instead of complicated math.

## 3.5 Think Stats, 2e: Exploratory Data Analysis

An introduction to exploratory data analysis. Like the first edition, this book emphasizes simple computational tools for exploring real data. It includes several new topics, including regression, time series analysis, and survival analysis. It presents basic use of NumPy, SciPy, Pandas, and StatsModels.