# No Bullshit Stats Notebooks#

Use the binder button to run the notebooks interactively:

## Why learn stats?#

Understanding statistics is becoming increasingly important in modern days. We’re surrounded by data, including survey data, analytics, personal data, location data, sales data, data from scientific experiments, etc. Scientists, researchers, and even business folks are starting to realize they need to learn the tools of statistics to make sense of all the data that surrounds them. Indeed, statistical thinking and procedures are at the core of modern research methodology and data-driven decision making in business.

## Book pitch#

The No Bullshit Guide to Statistics presents both theoretical and practical aspects of statistics as part of a connected whole. The book covers hands-on matters of data manipulation, and also digs into probability theory prerequisites so readers will become fluent with the math tools used to model random phenomena. With both theory and practical prerequisites in place, readers can tackle stats topics and truly understand the subject instead of just skimming the surface by applying stats formulas blindly. This book introduces statistics in a rigorous, yet accessible manner.

The good news is that I’m going to teach you everything I know about statistics. This means I’ll expose you to all the equations, 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 available in the statistical toolbox.

Don’t worry if you have no prior experience with Python coding. I have prepared for you a step-by-step Python tutorial that will bring you up to speed on all the basic Python concepts that you need to know to use Python as a scientific calculator.

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 in this book, and you’ll need to concentrate to understand fancy math expression like subscripts $$x_i$$, summations $$\Sigma_{i=1}^n x_i$$, integrals $$\int f_X(x) dx$$, expected values $$\mathbb{E}_X[g(X)]$$, and all kinds of other weird-ass math notation. Yes, the math complexity will escalate quickly, but you have to trust me on this: it’s all necessary complexity. I want you to understand the math so you can understand statistics better.

You’ll also have to endure lots of code examples that illustrate statistical procedures expressed as Python commands. These code examples will help to 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. You can play with the code examples by changing the parameters to explore what happens interactively: generate observations from random variables, plot distributions, run simulations, etc. 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: