Statistics and Data Science, M.S.

Program Overview

This Master's program in Data Science combines statistical theory, methods, and communication skills to train students to effectively use data for decision-making. It includes required courses in statistical methods, professional skills, and electives in areas such as machine learning, data visualization, and statistical computing. Graduates are prepared for careers as data scientists, statistical analysts, and other data-related roles in various industries.

Program Outline

Degree Overview:

This program combines a background in statistical theory, methods and practice related to data science with communication skills to educate a new generation of leaders who can use data effectively for planning and decision-making. The objective is to train students to translate vague questions about complex data into pragmatic analysis using statistical thinking. The program is grounded at key points through projects involving real data and/or realistic simulated data.

Outline:

Required Courses (11 credits):

• Statistical Methods I (4 credits)
• Introduction to Statistical Inference (4 credits)
• Statistical Learning (3 credits)

Professional Skills Courses (6 credits):

• Data Science Computing Project (3 credits)
• Data Science Practicum (3 credits)
or
• Introduction to Statistical Consulting (3 credits)

Elective Courses (13 credits):

Students may count up to 3 credits of Statistics undergraduate electives, including:

• R for Statistics I
• R for Statistics II
• R for Statistics III
• Introduction to Time Series
• Introductory Nonparametric Statistics
• Data Science Computing Project
• An Introduction to Sample Survey Theory and Methods
• Applied Categorical Data Analysis
• Data Science with R
• Statistical Data Visualization
• Classification and Regression Trees
• Introduction to Machine Learning and Statistical Pattern Classification
• Introduction to Deep Learning and Generative Models
• Applied Multivariate Analysis
• Financial Statistics
• Introduction to Computational Statistics
• Special Topics in Statistics
Students may count up to 1 elective course (up to 4 credits) of coursework numbered 500 or above taught outside of Statistics with advisor approval, including:
• Engineering, Industrial and Systems Engineering, and Operations and Information Management: Probabilistic Modeling for Engineering and Operations Research
• Computer Science: Introduction to Statistical Modeling, Machine Learning, Database Management Systems, Computational Data Analysis, Natural Language Processing, Deep Learning for Natural Language Processing, Probabilistic Graphical Models
• Business: Data Analytics for Business
Students must have at least 3 credits of coursework numbered 600 or above taught within Statistics, including:
• Computing in Data Science and Statistics
• Mathematical Statistics I
• Statistical Methods for Clinical Trials
• Statistical Methods for Epidemiology
• Special Topics in Statistics (may be repeated with different topic titles)
• Applied Time Series Analysis, Forecasting and Control I
• Mathematical Statistics II
• Mathematical Statistics
• Large Sample Theory of Statistical Inference
• Survival Analysis Theory and Methods
• Multivariate Analysis I
• Decision Trees for Multivariate Analysis
• Statistical Methods for Medical Image Analysis
• Statistical Computing
• Linear Randomized Algorithms for Data Science
• Introduction to Bayesian Decision and Control I
• Experimental Design I
• Non-Parametric Statistics
• Sample Survey Theory and Method
• Empirical Processes and Semiparametric Inference
• Statistical Model Building and Learning
• Nonparametric Statistics and Machine Learning Methods
• Theory and Application of Regression and Analysis of Variance I
• Theory and Application of Regression and Analysis of Variance II
• Estimation of Functions from Data
• Statistical Methods for Molecular Biology

Teaching:

The program is face-to-face and meets during weekdays on the UW-Madison Campus. Students will be taught by a faculty of experts in the field of statistics and data science, including:

• Professor Cecile Ane
• Assistant Professor Joshua Cape
• Professor Richard Chappell
• Professor Peter Chien
• Assistant Professor Jessi Cisewski-Kehe
• Assistant Professor Deshpande, Sameer
• Assistant Professor Nicolas Garcia Trillos
• Assistant Professor Yinqiu He
• Associate Professor Hyunseung Kang
• Professor Sunduz Keles
• Professor Bret Larget
• Assistant Professor Keith Levin
• Professor Wei-Yin Loh
• Professor Michael Newton
• Assistant Professor Vivak Patel
• Assistant Professor Alejandra Quintos
• Associate Professor Garvesh Raskutti
• Professor Karl Rohe
• Assistant Professor Kris Sankaran
• Professor Jun Shao
• Assistant Professor Miaoyan Wang
• Professor Yahzen Wang (chair)
• Professor Brian Yandell
• Professor Chunming Zhang
• Assistant Professor Yiqiao Zhong
• Professor Jun Zhu

Careers:

Students who graduate from this program can pursue careers as data scientists, statistical analysts, and other roles in industries such as technology, healthcare, finance, and government.