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Probability - The Science of Uncertainty and Data

This course is a part of Statistics and Data Science MicroMasters® Program, a 5-course MicroMasters series from edX.

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 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.

This course is part of theMITx 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. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

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Rating 5.0 based on 1 ratings
Length 16 weeks
Effort 10 - 14 hours per week
Starts Sep 1 (in 3 weeks)
Cost $300
From MITx via edX
Instructors John Tsitsiklis, Patrick Jaillet, Qing He, Jimmy Li, Jagdish Ramakrishnan, Katie Szeto, Kuang Xu, Dimitri Bertsekas, Eren Can Kizildag, Karene Chu
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science Mathematics
Tags Computer Science Data Analysis & Statistics Math

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Rating 5.0 based on 1 ratings
Length 16 weeks
Effort 10 - 14 hours per week
Starts Sep 1 (in 3 weeks)
Cost $300
From MITx via edX
Instructors John Tsitsiklis, Patrick Jaillet, Qing He, Jimmy Li, Jagdish Ramakrishnan, Katie Szeto, Kuang Xu, Dimitri Bertsekas, Eren Can Kizildag, Karene Chu
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science Mathematics
Tags Computer Science Data Analysis & Statistics Math

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