Taylor Brown

Assistant Professor
Unit: College of Arts and Sciences
Department: Department of Statistics
Office location and address
148 Amphitheater Way
Charlottesville, Virginia 22904
STAT 1602: Introduction to Data Science with Python
Credits: 3
This course provides an introduction to various topics in data science using the Python programming language. The course will start with the basics of Python, and apply them to data cleaning, merging, transformation, and analytic methods drawn from data science analysis and statistics, with an emphasis on applications. No prior experience with programming, data science, or statistics is required.
STAT 3120: Introduction to Mathematical Statistics
Credits: 3
This course provides a calculus-based introduction to mathematical statistics with some applications. Topics include: sampling theory, point estimation, interval estimation, testing hypotheses, linear regression, correlation, analysis of variance, and categorical data. Prerequisite: MATH 3100 or APMA 3100.
STAT 3250: Data Analysis with Python
Credits: 3
This course provides an introduction to data analysis using the Python programming language. Topics include using an intergrated development environment; data analysis packages numpy, pandas and scipy; data loading, storage, cleaning, merging, transformation, and aggregation; data plotting and visualization.
STAT 4993: Independent Study
Credits: 1–4
Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and direct the program.
STAT 5170: Applied Time Series
Credits: 3
Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 3120
STAT 5430: Statistical Computing with SAS and R
Credits: 3
Topics include importing data from various sources into R/SAS, manipulating and combining datasets, transform variables, "clean" data so that it is ready for further analysis, manipulating character strings, export datasets, and produce basic graphical and tabular summaries of data. More advanced topics will include how to write, de-bug, and tune functions and macros. Approximately equal time will be spent using SAS and R. Prerequisites: Introductory statistics course.
STAT 6021: Linear Models for Data Science
Credits: 3
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components. The primary software is R. Prerequisite: A previous statistics course, a previous linear algebra course, and permission of instructor.
STAT 7200: Introduction to Advanced Probability
Credits: 3
This course introduces fundamental concepts in probability from a measure-theoretic perspective. Topics include sigma fields, general measures, integration and expectation, the Radon-Nikodym derivative, product measure and conditioning, convergence concepts, and important limit theorems. The student is prepared for advanced study of statistical theory and stochastic processes. Prerequisite: STAT 6190 and graduate standing in Statistics
STAT 7510: Advanced Inference
Credits: 3
This course covers advanced theory and methodology in statistical inference. It includes, but is not limited to, substantial, in-depth coverage of topics in asymptotic inference. Context and additional topics vary by instructor.
STAT 8120: Topics in Statistics
Credits: 3
Study of topics in statistics that are currently the subject of active research.