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What will you learn?
The mathematical foundations for machine learning
Statistics literacy: understand the meaning of statements such as "at a 99% confidence level"
About the 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.
Program
Probability and Statistics in Data Science using Python
Using Python, learn statistical and probabilistic approaches to understand and gain insights from data.
Probability and Statistics in Data Science using Python
Using Python, learn statistical and probabilistic approaches to understand and gain insights from data.
Probability and Statistics in Data Science using Python
Using Python, learn statistical and probabilistic approaches to understand and gain insights from data.
Lecturers
Alon Orlitsky
Professor, Electrical and Computer Engineering UC San Diego
Yoav Freund
Professor of Computer Science and Engineering UC San Diego
Platform
All the courses on this platform are free of charge. The authors are top universities and corporations that seek to maintain high quality standards. If you do not meet a deadline for assignments, you lose points. Like on other platforms, the videos in which the theory is explained are followed by practical assignments. Courses are available in English, Chinese, Spanish, French and Hindi.