Probability and Statistical Data Analysis: Discrete Distributions and Categorical Data
Master the probabilistic reasoning and statistical inference that machine learning is built on. Part two in a five-part series, this online course teaches discrete modeling skills that form the foundation of classification, prediction, and data analysis.
Course Information
Certificate Track
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About this Course
Learn how to model count data and categorical outcomes using probability distributions, contingency tables, and discrete statistical models. Quantify uncertainty with probability mass functions, and Binomial and Multinomial models. Gain the discrete modeling skills used in classification, machine learning, and statistical inference.
This course is part two 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
Devavrat Shah
Andrew (1956) and Erna Viterbi Professor, Electrical Engineering and Computer Science; Director, MicroMasters Program in Statistics and Data Science
Devavrat Shah is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, a member of IDSS and LIDS at MIT, and an IEEE Fellow. He received his PhD in computer science from Stanford University in 2004 and joined the MIT faculty in 2005. His research spans statistical inference, stochastic networks, and social data processing, driven by the challenge of designing simple, high-performance algorithms under severe resource constraints. Dr. Shah is co-founder of the machine learning startup Ikigai Labs.
