No Bullshit Stats Notebooks#
Use the binder button to run the notebooks interactively:
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Contents#
notebooks/: the notebooks that accompany each section of the book.tutorials/: tutorials that introduce technical prerequisites like Python coding, data management with Pandas, and data visualization with Seaborn.
This book introduces statistics in a rigorous, yet accessible manner.
Good news; bad news#
The good news is that I’m going to teach you everything I know about statistics. This means I’ll show you all the formulas and computations required to understand statistics deeply. We’ll learn multiple alternative approaches for doing statistical calculations, including visualizations, math models, and Python code. By the end of this book, you’ll have developed practical skills for data management, analytical stills for doing probability calculations, and gained valuable experience with the various procedures in the statistics toolbox.
The bad news is that I’m going to teach you everything I know about statistics. This means there will be a lot of equations, and you’ll need to concentrate to understand fancy math expression like summations \(\Sigma_{i=1}^n x_i\) and integrals \(\int f_X(x) dx\). You have to trust me that the math complexity is necessary complexity, because knowing the math details will allow you to understand statistics better.
You’ll also have to become comfortable with code examples that illustrate statistical procedures expressed as Python commands. These code examples will keep us honest: if the answers we obtain using math formulas are the same as the answers we obtain by running the Python code, then we can be sure we’re doing things right. This is why these notebooks exist—so you can try things for yourself.
Book details#
These notebooks are part of the support materials for the upcoming book titled No Bullshit Guide to Statistics by Ivan Savov (Minireference Publishing). Check on the links below to learn more about the book:
Concept map from the book.
Detailed book outline (continuously updated)
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