Summary  
This chapter explains how to implement code that computes the mean, variance, and standard deviation of a data set by accumulating sums of squared deviations and applying the appropriate formulas.

General domain of usage  
Statistical data analysis

This course gives you a basic knowledge of statistics. It is intended for students with basic knowledge of Python syntax as they will be working with NumPy and pandas modules. During the course, you will become familiar with the basic concepts and then gradually move on to more complex concepts and tasks.

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.

Calculating Variance and Standard Deviation

Solution

Awesome!

Awesome!

Calculating Variance and Standard Deviation

Solution

Awesome!