Probability - The Science of Uncertainty and Data

MOOC
Language
English
Duration
3 months
Certificate
Certification paid
Course by EdX
What will you 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
About the 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:


  • 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/.

Program
Probability - The Science of Uncertainty and Data
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.
Probability - The Science of Uncertainty and Data
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.
Lecturers
John Tsitsiklis
John Tsitsiklis
Professor, Department of Electrical Engineering and Computer Science MIT
Dimitri Bertsekas
Dimitri Bertsekas
Professor, Electrical Engineering and Computer Science MIT
Patrick Jaillet
Patrick Jaillet
Professor, Electrical Engineering and Computer Science MIT
Karene Chu
Karene Chu
Lecturer and Research Scientist Massachusetts Institute of Technology
Qing He
Qing He
Teaching Assistant MIT
Jimmy Li
Jimmy Li
Teaching Assistant MIT
Jagdish Ramakrishnan
Jagdish Ramakrishnan
Teaching Assistant MIT
Katie Szeto
Katie Szeto
Teaching Assistant MIT
Kuang Xu
Kuang Xu
Teaching Assistant MIT
Platform
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All the courses on this platform are free of charge. The authors are top universities and corporations that seek to maintain high quality standards. If you do not meet a deadline for assignments, you lose points. Like on other platforms, the videos in which the theory is explained are followed by practical assignments. Courses are available in English, Chinese, Spanish, French and Hindi.
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