Python has become the most popular programming language for data science and competency in Python is a critical skill for students interested in this area. This tutorial course introduces Python within the context of the closely related areas of statistics and data science.

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L87 SDS 5532

Advanced Statistical Computing II

This is the second course on advanced methods and tools for Statistical Computing. This course will introduce classical methods, including the EM algorithm and its variants. It also will cover basic convex optimization theory and advanced computing tools and techniques for big data and learning algorithms. Prereq: Math 233; a course in linear algebra at level of Math 309 or Math 429; multivariable-calculus-based probability and mathematical statistics (Math/SDS 493-494 or Math/SDS 3211/4211); Experience with a high-level programming language like R, Python, C++, etc.

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2:30P-3:50P

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L87 SDS 5801

Advanced Topics in Statistics: Time Series and High-dimensional Data Analysis

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This is an advanced topics course on time series analysis and high-dimensional statistics. It will provide a systematic introduction to two research topics: selfnormalization (SN) for time series inference and nonlinear dependence metrics and their statistical applications. For self-normalization, we plan to cover its use for both confidence interval construction and hypothesis testing in the setting of stationary multivariate time series, functional time series, and high-dimensional time series. Change-point testing and estimation based on self-normalization will be introduced in detail for both low and high-dimensional data. Some recent work which combines sample splitting and self-normalization will also be presented. The course assumes that the student has the basic background of time series analysis and some research experience in time series analysis is desired but not a prerequisite. For nonlinear dependence metrics, the emphasis will be placed on distance covariance, energy distance and their variants, including Hilbert-Schmidt Independence Criterion, maximum mean discrepancy, and martingale di?erence divergence, among others. The usefulness of these metrics will be demonstrated in some contemporary problems in statistics, such as dependence testing and variable screening/selection for high-dimensional data, as well as dimension reduction and diagnostic checking for multivariate time series. Some recent work on their applications to the inference of non-Euclidean data will also be discussed. The presentations are based on the research results my collaborators and I have obtained in the past and will cover methodology, theory and practical data examples.

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4:00P-5:20P

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Shao

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L87 SDS 5802

Advanced Topics in Statistics: Five Statistical Insights You Need to Know

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This advanced topic course delves into five critical ideas and insights that have significantly impacted the fields of statistics and data science. From foundational concepts to influential advancements, each topic is selected for its historical significance and enduring impact. Students will develop a deep appreciation for these key ideas, associated insights, and their role in shaping contemporary statistical theory and practice. For each topic, we will discuss motivations, innovations, and impacts through presentations and discussions. Students will be required to read important papers and share their perspectives. Although many topics fit this criterion, the list includes, but is not limited to, influence functions, resampling, adaptive designs, dimension reduction, data augmentation, regularization, identifiability, and propensity scores. Depending partly on the interests of the students, we will select and focus on five topics over the course of one semester. Prerequisites: SDS 5020 and 5071.

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4:00P-5:20P

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C	=	Credit (letter grade)
P	=	Pass/Fail
A	=	Audit
U	=	Satisfactory/Unsatisfactory
S	=	Special Audit
Q	=	ME Q (Medical School)
Please note: not all grade options assigned to a course are available to all students, based on prime school and/or division. Please contact the student support services area in your school or program with questions.