Probability and Statistical Data Analysis: Foundations and Exploratory Data Analysis
Master the probabilistic reasoning and statistical inference that machine learning is built on. Part one in a five-part series, this online course explores the foundations of data analysis.
Course Information
Certificate Track
Learn for Free
About this Course
Build foundational knowledge in probability and explore data with histograms, boxplots, and empirical proportions. Then formalize what you see with sample spaces, events, and conditional thinking. Learn how to translate scientific questions into probabilistic language, the essential first step in statistical modeling for machine learning.
This course is part one of five in the Probability and Statistical Data Analysis Program, which includes the following:
What you'll learn
- Speak the language of sample spaces and events
- Reason conditionally about how outcomes relate
- Turn a scientific question into a probability question
- Summarize data with histograms, boxplots, and proportions
Meet your instructors
Yury Polyanskiy
Professor of Electrical Engineering and Computer Science
Yury Polyanskiy is the Cutten Professor of Electrical Engineering and Computer Science, a member of IDSS and LIDS at MIT, and an IEEE Fellow. Yury received Ph.D. degree in electrical engineering from Princeton University, Princeton, NJ in 2010. His research interests span information theory, machine learning and statistics. Dr. Polyanskiy won the 2020 IEEE Information Theory Society James Massey Award, 2013 NSF CAREER award and 2011 IEEE Information Theory Society Paper Award. Over the years, he worked on applied AI projects at Amazon and Anthropic.
