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date_range Starts on May 11, 2021
event_note Ends on September 14, 2021
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assignment Level : Advanced
chat_bubble_outline Language : English
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credit_card Free access
verified_user Fee-based Certificate
timer 180 hours in total

About the content

Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an analysis of their asymptotic performance.

After developing basic tools to handle parametric models, we will explore how to answer more advanced questions, such as the following:

  • How suitable is a given model for a particular dataset?
  • How to select variables in linear regression?
  • How to model nonlinear phenomena?
  • How to visualize high-dimensional data?

Taking this class will allow you to expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link them together, equipping you with the tools you need to develop new ones.

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

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  • 6.431x or equivalent probability theory course
  • College-level single and multi-variable calculus
  • Vectors and matrices



  • Construct estimators using method of moments and maximum likelihood, and decide how to choose between them
  • Quantify uncertainty using confidence intervals and hypothesis testing
  • Choose between different models using goodness of fit test
  • Make prediction using linear, nonlinear and generalized linear models
  • Perform dimension reduction using principal component analysis (PCA)


Philippe Rigollet
Associate Professor
Massachusetts Institute of Technology

Jan-Christian Hütter
Teaching Assistant
Massachusetts Institute of Technology

Karene Chu
Digital Learning Scientist and Research Scientist
Massachusetts Institute of Technology


Content Designer


MIT is a world-class educational institution where teaching and research — with relevance to the practical world as a guiding principle — continue to be its primary purpose.

MIT is independent, coeducational, and privately endowed. Its five schools and one college encompass numerous academic departments, divisions and degree-granting programs, as well as interdisciplinary centers, laboratories and programs whose work cuts across traditional departmental boundaries.




Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with EdX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

4.6 /5 Average
Best Review

Every aspect of this course is very good.

Published on November 16, 2019
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November 16, 2019

Every aspect of this course is very good.

October 22, 2019

At ~1/3 of the course I can surely say it is a very "fully packed" course. I am using 20-30 h/week of self study. Most of the course goes smooth, with well tailored exercises for students with just enough calculus and linear algebra backgrounds like me (but you need to have studied probabilities before). If I have to find a minus, while the material is presented in a rigorous, analytical way, the related concepts are not much given, letting you a bit wondering what all this is for. Still, I prefer the MITx "Introduction to probabilities" course, but this is not much far.