Key Information
About the content
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.
Prerequisite
- The previous coursein the MicroMasters program: DSE200x
- Undergraduate level education in:
- Multivariate calculus
- Linear algebra
Syllabus
- The mathematical foundations for machine learning
- Statistics literacy: understand the meaning of statements such as "at a 99% confidence level"
Instructors
Alon Orlitsky
Professor, Electrical and Computer Engineering
UC San Diego
Yoav Freund
Professor of Computer Science and Engineering
UC San Diego
Content Designer
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