Title: Applications invited for MicroMasters program in Statistics and Data Science.
Massachusetts Institute of Technology — a coeducational, privately endowed research university founded in 1861 — is dedicated to advancing knowledge and educating students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century. Through MITx, the Institute furthers its commitment to improving education worldwide
About this course
The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.
Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.
The course covers all of the basic probability concepts, including:
The contents of this courseare heavily based upon the corresponding MIT class -- Introduction to Probability -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class but will enable you to apply the tools of probability theory to real-world applications or to your research.
What you'll learn
Unit 1: Probability models and axioms
Unit 2: Conditioning and independence
Unit 3: Counting
Unit 4: Discrete random variables
Unit 5: Continuous random variables
Unit 6: Further topics on random variables
Unit 7: Bayesian inference
Unit 8: Limit theorems and classical statistics
Unit 9: Bernoulli and Poisson processes
Unit 10 (Optional): Markov chains
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