STAT 1100 Chance: An Introduction to Statistics

This course studies introductory statistics and probability, visual methods for summarizing quantitative information, basic experimental design and sampling methods, ethics and experimentation, causation, and interpretation of statistical analyzes. Applications use data drawn from various current sources, including journals and news. No prior knowledge of statistics is required. Students will not receive credit for both STAT 1100 and STAT 1120.

This course is typically offered every semester.

STAT 1120 Introduction to Statistics

This course includes graphical displays of data, relationships in data, design of experiments, causation, random sampling, probability distributions, inference, confidence intervals, tests of hypotheses, and regression and correlation. No prior knowledge of statistics is required. Students will not receive credit for both STAT 1100 and STAT 1120.

This course is typically not offered. Credit for this course is given for a score of 4 on the AP Exam.

STAT 1400 Forensic Science and Statistics

This course provides an introduction to statistical analysis in the context of forensic science. Statistical topics covered include probability distributions, hypothesis testing, confidence intervals, measures of association, and regression. Applications drawn from forensics include analysis of fingerprints, DNA, and particle evidence. No prior knowledge of statistics or forensic science is required.

This course is typically not offered on a regular schedule.

STAT 1601 Introduction to Data Science with R

This course provides an introduction to the process of collecting, manipulating, exploring, analyzing, and displaying data using the statistical software R. The collection of elementary statistical analysis techniques introduced will be driven by questions derived from the data. The data used in this course will generally follow a common theme. No prior knowledge of statistics, data science, or programming is required.

This course is typically offered every semester.

STAT 1602 Introduction to Data Science with Python

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 knowledge of statistics, data science, or programming is required.

This course is typically offered every semester.

STAT 1800 Introduction to Sports Analytics

This course provides an introduction to sports analytics, including the collection, analysis, and visualization of sports data using the statistical programming language R. Elementary statistical analysis techniques will be introduced through questions arising in sports. No prior knowledge of statistics is required.

This course is typically offered every other fall semester.

STAT 2020 Introduction to Biostatistics

This course includes a basic treatment of probability, and covers inference for one and two populations, including both hypothesis testing and confidence intervals. Analysis of variance and linear regression are also covered. Applications are drawn from biology and medicine. No prior knowledge of statistics is required. Co-requisite: Concurrent enrollment in a lab section of STAT 2020.

This course is typically offered every semester.

STAT 2120 Introduction to Statistical Analysis

This course provides an introduction to the probability & statistical theory underlying the estimation of parameters & testing of statistical hypotheses, including those in the context of simple & multiple regression Applications are drawn from economics, business, & other fields. No prior knowledge of statistics is required. Highly Recommended: Prior experience with calculus I; Co-requisite: Concurrent enrollment in a lab section of STAT 2120.

This course is typically offered every semester.

STAT 3080 From Data to Knowledge

This course introduces methods to approach uncertainty and variation inherent in elementary statistical techniques from multiple angles. Simulation techniques such as the bootstrap will also be used. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R. Prerequisite: A prior course in distribution-based statistics and a prior course in programming.

SIS enrollment requirements: Students must have completed ONE of STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 1601, STAT 1602, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically offered every semester.

STAT 3110 Foundations of Statistics

This course provides an overview of basic probability and matrix algebra required for statistics. Topics include sample spaces and events, properties of probability, conditional probability, discrete and continuous random variables, expected values, joint distributions, matrix arithmetic, matrix inverses, systems of linear equations, eigenspaces, and covariance and correlation matrices. Prerequisite: A prior course in calculus II.

SIS enrollment requirements: Students must have completed ONE of MATH 1220, MATH 1320, APMA 1110

This course is typically offered every semester.

STAT 3120 Introduction to Mathematical Statistics

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: A prior course in probability.

SIS enrollment requirements: Students must have completed ONE of STAT 3110, MATH 3100, APMA 3100

This course is typically offered every semester.

STAT 3130 Design and Analysis of Sample Surveys

This course introduces main designs & estimation techniques used in sample surveys; including simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation; non-response problems, measurement errors. Properties of sample surveys are developed through simulation procedures. Prerequisite: A prior course in statistics.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120

This course is typically offered every fall semester.

STAT 3220 Introduction to Regression Analysis

This course provides a survey of regression analysis techniques, covering topics from simple regression, multiple regression, logistic regression, and analysis of variance. The primary focus is on model development and applications. Prerequisite: A prior course in statistics.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120

This course is typically offered every semester.

STAT 3250 Data Analysis with Python

This course provides an introduction to data analysis using the Python programming language. Topics include using an integrated development environment; data analysis packages numpy, pandas and scipy; data loading, storage, cleaning, merging, transformation, and aggregation; data plotting and visualization. Prerequisite: A prior course in statistics and a prior course in programming.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 1601, STAT 1602, STAT 3080, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically offered every semester.

STAT 3280 Data Visualization and Management

This course introduces methods for presenting data graphically and in tabular form, including the use of software to create visualizations. Also introduced are databases, with topics including traditional relational databases and SQL (Structured Query Language) for retrieving information. Prerequisite: A prior course in statistics and a prior course in programming.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically offered every semester.

STAT 3480 Nonparametric and Rank-Based Statistics

This course includes an overview of parametric vs. non-parametric methods including one-sample, two-sample, and k-sample methods; pair comparison and block designs; tests for trends and association; multivariate tests; analysis of censored data; bootstrap methods; multi-factor experiments; and smoothing methods. Prerequisite: A prior course in statistics.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120

This course is typically offered every spring semester.

STAT 4120 Applied Linear Models

This course includes linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, and other topics. Conceptual discussion is supplemented with hands-on practice in applied data-analysis tasks. Highly recommended: A prior course in applied regression such as STAT 3220. Prerequisite: A prior course in statistics and a prior course in linear algebra.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 3110, MATH 3350, MATH 3351, APMA 3080

This course is typically offered every spring semester.

STAT 4130 Multivariate Statistics

This course develops fundamental methodology to the analysis of multivariate data using computational tools. Topics include multivariate normal distribution, multivariate linear model, principal components and factor analysis, discriminant analysis, clustering, and classification. Prerequisite: A prior course in mathematical statistics, a prior course in linear algebra, and a prior course in programming.

SIS enrollment requirements: Students must have completed STAT 3120 & ONE of STAT 3110, MATH 3350, MATH 3351, APMA 3080 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically not offered on a regular schedule.

STAT 4160 Experimental Design

This course introduces various topics in experimental design, including simple comparative experiments, single factor analysis of variance, randomized blocks, Latin squares, factorial designs, blocking and confounding, and two-level factorial designs. The statistical software R is used throughout this course. Prerequisite: A prior course in regression.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021

This course is typically offered every fall semester.

STAT 4170 Financial Time Series and Forecasting

This course introduces topics in time series analysis as they relate to financial data. Topics include properties of financial data, moving average and ARMA models, exponential smoothing, ARCH and GARCH models, volatility models, case studies in linear time series, high frequency financial data, and value at risk. Prerequisite: A prior course in probability, a prior course in regression, and a prior course in programming.

SIS enrollment requirements: Students must have completed ONE of STAT 3110, MATH 3100, APMA 3100 & ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically offered every spring semester.

STAT 4220 Applied Analytics for Business

This course focuses on applying data analytic techniques to business, including customer analytics, business analytics, and web analytics through mining of social media and other online data. Several projects are incorporated into the course. Prerequisite: A prior course in regression and a prior course in programming.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically offered every spring semester.

STAT 4630 Statistical Machine Learning

This course introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated throughout. Prerequisite: A prior course in regression and a prior course in programming.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically offered every fall semester.

STAT 4800 Advanced Sports Analytics I

This course provides a platform for exploring advanced statistical modeling and analysis techniques through the lens of state-of-the-art sports analytics. Prerequisite: A prior course in mathematical statistics, a prior course in regression, and a prior course in programming.

SIS enrollment requirements: Students must have completed STAT 3120 & ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is typically offered every fall semester.

STAT 4993 Independent Study

This course is for students interested in topics that are not covered in other courses and involves reading and study in areas of interest to the individual student. Students must obtain a faculty advisor to approve and direct the program.  

STAT 4996 Capstone

Students will work in teams on a capstone project. The project will involve significant data preparation and analysis of data, preparation of a comprehensive project report, and presentation of results. Many projects will come from external clients who have data analysis challenges. Prerequisite: A prior course in regression and a prior course in programming. This course is restricted to Statistics majors in their final year.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113 & 4th Year Statistics major

This course is typically offered every semester.

STAT 5140  Survival Analysis and Reliability Theory

Topics include lifetime distributions, hazard functions, competing-risks, proportional hazards, censored data, accelerated-life models, Kaplan-Meier estimator, stochastic models, renewal processes, and Bayesian methods for lifetime and reliability data analysis. Prerequisite: MATH 3120 or5100, or instructor permission; corequisite: STAT5980.

This course is typically not offered on a regular schedule.

STAT 5170  Applied Time Series

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

This course is typically offered every schedule.

STAT 5180  Design and Analysis of Sample Surveys

This course covers the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisites: STAT 3120.

This course is typically not offered on a regular schedule.

STAT 5330  Data Mining

This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisites: Previous or concurrent enrollment in STAT5120 or STAT 6120.

This course is typically not offered on a regular schedule.

STAT 5350  Applied Causal Inference

Introduces statistical methods used for causal inference, particularly for quasi-experimental data. Focus is on the potential outcomes framework as an organizing principle and examining the estimation of treatment effects under various assumptions. Topics include matching, instrumental variables, difference-in-difference, regression discontinuity, synthetic control, and sensitivity analysis. Examples come from various fields.

This course is typically not offered on a regular schedule.

STAT 5390  Exploratory Data Analysis

Introduces philosophy and methods of exploratory (vs confirmatory) data analysis: QQ plots; letter values; re-expression; median polish; robust regression/anova; smoothers; fitting discrete, skewed, long-tailed distributions; diagnostic plots; standardization. Emphasis on real data, computation (R), reports, presentations. Prerequisite: A previous statistics course; previous exposure to calculus and linear algebra recommended.

This course is typically not offered on a regular schedule.

STAT 5630  Statistical Machine Learning

Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated throughout. Prerequisite: STAT5120, STAT 6120, or ECON 3720, and previous experience with R Prerequisite: STAT5120, STAT 6120, or ECON 3720, and previous experience with R

This course is typically not offered on a regular schedule.