Probability and Statistics in Data Science using Python

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters® program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL.

Prerequisites

1. The previous course in the MicroMasters program (Python for Data Science)
2. Undergraduate level education in:
   • Multivariate calculus
   • Linear algebra

Course Format

This course is self-paced, containing assignments without due dates. You can progress through the course at your own speed.

Learn more and enroll on edX.