Statistics for AI/ML

A continuation of the Causal Inference course from Columbia University, this advanced course delves into topics like mediation, principal stratification, and longitudinal causal inference.

This University of London course provides a comprehensive introduction to probability and statistics, focusing on understanding and interpreting p-values and confidence intervals.

Part of the Data Science Specialization from Johns Hopkins University, this course presents the fundamentals of statistical inference in a practical, hands-on manner for data analysis.

A guided project on Coursera that provides a hands-on introduction to essential causal inference techniques for data science.

This specialization from Duke University teaches you how to analyze and visualize data in R. It covers topics such as probability, inference, regression, and machine learning. The specialization is very hands-on and includes several projects.

This HarvardX course covers central concepts of statistical inference and modeling, including how to perform inference on high-dimensional data.

Part of Harvard's Data Science Professional Certificate, this course covers the fundamentals of probability theory needed for a data science career.

An MIT course that provides a foundational understanding of probability models, including random processes and the basic elements of statistical inference.

A Harvard University course that covers statistical concepts and models relevant for causal inference in the context of high-throughput experiments.

Another course in Harvard's Data Science Professional Certificate that introduces the basics of statistical inference using the R programming language.

This course from Columbia University introduces the fundamental concepts of statistical thinking for data science. It covers topics such as probability, sampling, estimation, and hypothesis testing. The course emphasizes the practical application of these concepts to real-world data problems.