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April 20, 2026, 4:52 p.m.
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(Last updated: April 20, 2026, 4:52 p.m.)
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SOURCE: Downey A. Think Stats. Exploratory Data Analysis 3ed 2025
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MEDIAINFO
Textbook in PDF format If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis—from wrangling data and generating statistics to identifying patterns and testing hypotheses. Whether you're a data scientist, software engineer, or data enthusiast, you'll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You'll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place. There are two ways you can run the notebooks. By far the easier one is to use Colab, which is a service provided by Google where you can run the notebooks in a web browser without installing anything on your computer. If you start from the Think Stats home page, you will find links to the notebooks, including one that introduces Colab and Jupyter notebooks. If you don’t want to use Colab, you can download the notebooks and run them on your computer, but in that case you will have to install Python, Jupyter, and the libraries the book uses, including NumPy, SciPy, and StatsModels. If you have experience installing software, setting up an environment where you can run the notebooks is not difficult. But if you don’t have that experience, your first attempt can be challenging, and sometimes frustrating. In that case, it can be a barrier to getting the most out of this book. If you want to learn about exploratory data analysis in Python, you don’t want to spend your time and cognitive capacity on installing software! So I strongly recommend that you run at least the first few chapters on Colab. Analyze data distributions and visualize patterns using Python libraries Improve predictions and insights with regression models Dive into specialized topics like time series analysis and survival analysis Integrate statistical techniques and tools for validation, inference, and more Communicate findings with effective data visualization Troubleshoot common data analysis challenges Boost reproducibility and collaboration in data analysis projects with interactive notebooks
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