Introduction

Introduction

This lesson summarizes the topics we'll be covering in section 09 and why they'll be important to you as a data scientist.

Objectives

You will be able to:

• Understand and explain what is covered in this section
• Understand and explain why the section will help you to become a data scientist

It's all Stats!

You've already seen the value of descriptive statistics when doing exploratory data analysis. In this section we're going to dive deeper into a range of statistical concepts. We're going to start by looking at discrete and continuous distributions and how you can use stem and leaf plots for visualizing distributions.

We're then going to look at a range of techniques for representing distributions - the Probability Mass Function, the Cumulative Distrubution Function and the Probability Density Function.

We're then going to dig a little deeper into the Normal/Gaussian distribution and the Standard Normal DIstribution, before introducing the ideas of skewness and kurtosis that can be used to quantify how "un-normal" a given distribution is.

We'll then introduce the use cases for z-tables and p-values for describing statistical significance, and we'll finish off the section by looking at the "one-sample z test" - the most basic type of hypothesis test.

Summary

In this section, we're going to take a deeper dive into a range of foundational statistical concepts that we'll need as we start to dig into machine learning later in the course.