Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference — Course 1 of 4 in the MITx MicroMasters program in Statistics and Data Science.
- 16 weeks long
- 10-14 hours per week
- Learn for FREE, Ugpradable
- Taught by: John Tsitsiklis, Dimitri Bertsekas, Patrick Jaillet, Eren Can Kizildag, Qing He, Jimmy Li, Jagdish Ramakrishnan, Katie Szeto, Kuang Xu
- View Course Syllabus
Online Course Details:
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:
- multiple discrete or continuous random variables, expectations, and conditional distributions
- laws of large numbers
- the main tools of Bayesian inference methods
- an introduction to random processes (Poisson processes and Markov chains)
The contents of this course are 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.
This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master’s at other universities.
What you’ll learn
- The basic structure and elements of probabilistic models
- Random variables, their distributions, means, and variances
- Probabilistic calculations
- Inference methods
- Laws of large numbers and their applications
- Random processes