edX
edXProbability and Statistics in Data Science using Python
323 studentsADVANCEDEnglish
Korshub's take
Data science lives at the intersection of mathematics and messy reality. This MicroMasters course builds your mathematical foundation—probability, statistics, and the reasoning about uncertainty they enable—then immediately grounds you in hands-on Jupyter notebooks. You'll work with random variables, correlation, regression, dimensionality reduction (PCA), and information theory. The dual focus on theory and application makes this course rigorous yet practical: you'll understand not just how to code an analysis, but why it works and when it fails.
About this course
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.
Price
$350
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