Part 1: Essential Concepts

In this part, we will introduce the math concepts that you will encounter again and again as a data scientist. These concepts are vital to gain a good understanding of. After a recap of basic math notation, we look at the concepts related to how data is produced and then move through to concepts related to how to transform data, finally building up to our end goal of how to model data. These concepts are essential because you will use and combine them simultaneously in your work. By the end of Part 1, you will be comfortable with the math concepts that underpin almost all data science models and algorithms.

This section contains the following chapters:

• Chapter 1, Recap of Mathematical Notation and Terminology
• Chapter 2, Random Variables and Probability Distributions
• Chapter 3, Matrices and Linear Algebra
• Chapter 4, Loss Functions and Optimization
• Anchor 5, Probabilistic Modeling