Create Explanatory  Statistics

Math Statistics Lab is the innovator in the undergraduate and graduate teaching of statistics. From the exploratory data analysis to the most difficult concepts  of statistical estimation and hypothesis  testing theory, the classes taught by the lab members are build upon project based learning and are oriented around questions and answers, student/professor/TA interactions, hands on examples & projects and most of all critical thinking.  

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Math 189
Exploratory Data Analysis and Inference for Data Science

This course provides a balance between statistical theory and application; offering valuable skills in an area of growing importance across multiple disciplines (Big Data).  It contains an important experiential component - where students will participate in a data analysis competition at the end of the class.

Winter 2019,2018, 2017, 2016, 2015

Math 181b
Introduction to Mathematical Statistics: II

This course provides a  thorough  statistical theory of two sample hypothesis testing and estimation in regression setting; offering fundamental mathematical skills  to all interested in deepening their understanding of statistical concepts.  An important  component  of the class is active and critical participations of  the  students,  in each lecture,  where  in collaborations with other students or the professor they will provide missing details to the mathematical computations presented to them. 

Spring 2018,2017, 2016, 2015, 2013, 2014, 2012
​Fall 2017, 2018

Math 183
​Statistical Methods for Engineers

This course provides a balance between probability theory, statistical theory and application; offering valuable skills in an area of immense  importance  today (Data Analysis).  It contains both theoretical and applied components - students will  complete missing theoretical computations and provide statistical analysis of real-life datasets.

Winter 2015, Fall 2013, Winter 2013

Math 281 A
Mathematical Statistics I

This course was based on the E. Lehman book on "Theory of Point Estimation" with major focus on finite sample theory and shrinkage and admissibility of point estimators. Starting Fall 2018 this course will have endured significant changes including 3-4 textbook changes as well as topic changes. Majority of the effort will be on a modern tools and techniques for asymptotic statistics.

This course is a prerequisite for the Statistics PhD program. Lectures are at a PhD level and prepare for the qualifying exam in Mathematical Statistics.

Fall 2018, 2013

Math 281B
​Mathematical Statistics II

This course is based on two books: "Asymptotic Statistics" by S. Van de.Geer and "Theory of Point Estimation" by E. Lehman. Major focus is on large tail bounds and asymptotic theory of M-estimators and their Asymptotic Efficiency study.  This course is a prerequisite for the Statistics PhD program.

Winter 2014

Math 287
Theory of Statistical Learning

This course illustrates state-of-the art methods in statistical learning, bridging the gap between theory and methodology in high dimensional estimation, inference and signal detection.   It contains an important reading component - where students  read new journal articles and present or implement methods in form of a report or lecture to the whole class. Some of the topics in the Spring 2015 class were: penalized regression, signal detection, false discovery rate, random forests, deep learning, graphical models, etc.

Spring 2017, 2015, 2013

Math 289a
Statistical Learning & Data Science

Topics course in modern applications of statistics for machine learning, deep learning and data science. The course is centered on statistics applications through python. Topics covered include classification/regression, trees, dimensionality reduction, neural networks, regularizations, convolutional neural networks, transfer learning, etc.

Fall 2018

MGTF 413
Computational Finance

This course illustrates state-of-the art methods in computational finance, bridging the gap between methodology and applications of option pricing.  An important component of the class is independent reading and final projects presentations - where students   present and implement methods in form of a  presentation to the whole class.  

Winter 2019, 2018, 2017, 2016, 2015

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