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Learn Probability Theory, earn certificates with free online courses from Harvard, Stanford, MIT, University of Pennsylvania and other top universities around the world. Read reviews to decide if a class is right for you.
If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place.
Master essential math concepts for data science: set theory, real numbers, functions, graphs, rates of change, and probability theory. Build a strong foundation for advanced analysis.
Explores probability theory for random groups in number theory, topology, and combinatorics. Covers moment problems, universality theorems, and distributions of class groups and Selmer groups.
Explore fundamental set theory concepts and their application in probability, including sample spaces, events, and key operations like unions and intersections through clear examples.
Explore the fundamentals of probability theory, including random phenomena, sample spaces, events, and conditional probability concepts.
Explores constructive approaches to probability theory, comparing frequentist and pointfree metric methods with classical Kolmogorov axioms and other paradigms in mathematics.
Explore the mathematical concept of Chebyshev's Inequality, a fundamental principle in probability theory and statistics.
This course provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact.
Learn sampling, data exploration, and basic probability using R. Gain practical skills in statistical analysis through hands-on exercises and a final project.
Explore statistical concepts, from descriptive methods to inferential techniques. Learn to calculate, interpret, and apply statistics using real-world examples and free software.
Explore the fundamentals of probability theory and random variables in this concise introduction to signals and systems.
Comprehensive introduction to probability concepts, covering sample spaces, events, observed and classical probabilities, and subjective probability, with practical examples and applications.
Explore the medical test paradox and a redesigned Bayes' rule, focusing on likelihood ratios and Bayes factors to enhance understanding of conditional probability in diagnostic testing.
Explore quality control principles through multinomial distribution, learning to assess batch reliability using non-destructive inspection methods and statistical sampling techniques.
Learn probability theory -- essential for a data scientist -- using a case study on the financial crisis of 2007-2008.
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