Statistical Thinking for Data Science

Data speaks the language of probability and statistics. It is humanely not possible to perform all calculations and predictions on millions of real data points. This is when probabilistic thinking comes into play. The idea of being able to answer for uncertainity by calculating (mathematically) what could happen when an event is repeated again and again and again.

This repository is aimed to be a guide and a tutorial of any one learning Python implementation of general statistical analysis and focuses mainly on data visualization for running through probability and statistics.

Univariate Data Analysis

We start in the order of a typical Data Analysis framework and move forward. The first notebook deals with some basic data visualizations that would help in getting a grasp of the data we are dealing with (Univariate)

Multivariate Data Analysis

Statistical Thinking Part - 1

1. ECDF
2. Variance, Covariance and Standard Deviations
3. Pearson Coefficient of Correlation

Statistical Thinking Part - 2