Introduction

Let’s get started! As mentioned before, the goal of these notes is to be very thorough, yet accessible. I want to teach you as much as possible but also not go on tangents and make things unnecessarily complicated.  With that said, this guide is designed for someone who really wants to get a solid background in statistics and is not for the faint of heart.

Prerequisites

As of now, the website does not cover calculus, linear algebra, or programming languages like R or Python.  However, I do recommend that you have a basic understanding of those three topics. Some statistical concepts do use a fair amount of derivatives and integrals.  Derivatives are often used when optimizing a model as this requires finding a maximum or minimum. Integrals are important for understanding how probability distributions work and the relationship between cumulative density functions and probability density functions.  While you don’t really need to know calculus to understand much of statistics, it will enhance your understanding when studying the theory behind it.

It is also important to understand a programming language so you can do your own statistical analysis. We recommend learning R, which is a specialized statistical programming language and Python, which is the one of the most common programming languages used by businesses today. Both are free to download and use and include many tools for statistical inference and machine learning.

Finally, linear/matrix algebra is important if you want to get deeper into statistics.  Many literature will explain concepts in linear/matrix algebra terms, and you will have to understand linear/matrix algebra if you want to create your own machine learning algorithms. You can implement many machine learning algorithms using pre-built packages, but for very specific problems, you may have to build your own algorithm.

Recommended Material

No book is necessary to use the website but the following resources can act as another explanation for the topics taught here.

  • Probability And Statistics (4th) – DeGroot, Schervish
  • Foundations And Applications Of Statistics – Pruim
  • A linear regression book (TBD)
  • An Introduction to Statistical Learning (7th) – James
  • The Elements of Statistical Learning (2nd) – Hastie

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