Fundamentals of Statistics
Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science.
Fundamentals of Statistics
Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science.
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.
What you'll learn
- 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)
Prerequisites
- Undergraduate probability theory
- Undergraduate level single and multi-variable calculus
- Vectors and matrices
Meet your instructors
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Associate Professor -
Teaching Assistant -
MITx Digital Learning Scientist, Statistics & Data Science MicroMasters Program
Philippe Rigollet
Philippe Rigollet
Associate Professor
Philippe Rigollet is an associate professor in the Department of Mathematics at MIT. He works at the intersection of statistics, machine learning, and optimization, focusing primarily on the design and analysis of statistical methods for high-dimensional problems. He received a PhD in mathematical statistics from the University of Paris VI. He has held positions as a visiting assistant professor at the Georgia Institute of Technology and then as an assistant professor at Princeton University. He has received an NSF CAREER award, a Howard B. Wentz Jr. Junior Faculty Award at Princeton and the best paper award at the conference on learning theory COLT 2013.
Jan-Christian Hütter
Jan-Christian Hütter
Teaching Assistant
Jan-Christian Hütter is a graduate student in the Mathematics department at MIT. His research in Mathematical Statistics is about shape constrained estimation and causal discovery. He was a teaching assistant for High-Dimensional Statistics (18.657) in 2017.
Karene Chu
Karene Chu
MITx Digital Learning Scientist, Statistics & Data Science MicroMasters Program
Karene Chu received her Ph.D. in mathematics from the University of Toronto in 2012. Since then she has been a postdoctoral fellow first at the University of Toronto/Fields Institute, and then at MIT, with research focus on knot theory. She has taught single and multi-variable calculus, and linear algebra at the University of Toronto where she received a teaching award.