College of Liberal Arts & Sciences

# Course Descriptions & Syllabi

## Undergraduate Duplication and Regression Policy

Data science majors may not earn a major or minor in computer science or statistics, a major in computer science and engineering, or the Certificate in Social Science Analytics. Likewise, statistics majors may not earn a major in data science.

Undergraduate students should be aware of the duplication and regression policies concerning the following courses.

Students may earn credit for only two of these:

STAT:1010 Statistics and Society

DATA:1015/STAT:1015 Introduction to Data Science

STAT:1020 Elementary Statistics and Inference (same as PSQF:1020)

STAT:1030 Statistics for Business

STAT:2010 Statistical Methods and Computing

Credit for STAT:1010 Statistics and Society may be earned only if the course is taken before any of these:

STAT:1015 Introduction to Data Science

STAT:1020 Elementary Statistics and Inference (same as PSQF:1020),

STAT:1030 Statistics for Business, or

STAT:2010 Statistical Methods and Computing.

Students may receive credit for only one course from each of these pairs:

STAT:2010 Statistical Methods and Computing and STAT:4200 Statistical Methods and Computing,

STAT:3100 Introduction to Mathematical Statistics I and STAT:3120 Probability and Statistics, and

STAT:3510 Biostatistics and STAT:4143 Introduction to Statistical Methods.

Students may not take STAT:3101 Introduction to Mathematical Statistics II and STAT:4101 Mathematical Statistics II at the same time and get credit for both (nor go back to STAT:3101 Introduction to Mathematical Statistics II after taking STAT:4101 Mathematical Statistics II).

- Course descriptions, meeting times, prerequisites, and registration instructions are also provided on MyUI.

## Actuarial Science Courses, Descriptions & Syllabi

ACTS:1000 FIRST-YEAR SEMINAR (1 s.h.)

Introduction to actuarial science; U.S. actuarial organizations and actuarial qualification process; program requirements and tips for academic success; career center, actuarial club, and internships; actuarial career; ethics; communication; introduction to actuarial computing. Students will investigate and report on different actuarial career paths. Students will use Excel worksheets to produce annuity and loan schedules and use these worksheets for sensitivity analysis. Students will gain valuable skills and resources for success in the Actuarial Science major.

Pre-requisites: none

Co-requisites: none

Requirements: first- or second-semester standing

Recommendations: none

Special Grading: none

Syllabus

ACTS:1001 INTRODUCTORY SEMINAR ON ACTUARIAL SCIENCE (1 s.h.)

Introduction to actuarial science, U.S. actuarial organizations and actuarial qualification process; program requirements and tips for academic success; career center, actuarial club, and internships; actuarial career; ethics; communication; introduction to actuarial computing.

Pre-requisites: none

Co-requisites: none

Requirements: actuarial science interest major and first-year standing

Recommendations: none

Special Grading: none

ACTS:3080 MATHEMATICS OF FINANCE I (3 s.h.)

Mathematics of compound interest, annuities certain, loan amortization schedules, bonds, yield rates, and introduction to interest rate risk management.

Pre-requisites: none

Co-requisites: none

Requirements: Calculus II or have graduate standing

Recommendations: none

Special Grading: none

Syllabus

ACTS:3110 ACTUARIAL EXAM P PREPARATION (1 s.h.)

Preparation for the Society of Actuaries exam P.

Pre-requisites: none

Co-requisites: STAT:3100 or STAT:4100 or STAT:5100

Requirements: none

Recommendations: none

Special Grading: Offered on S-F basis only for undergraduates; instructor has the option of using S-U grades for graduate level students

ACTS:3210 ACTUARIAL EXAM FM PREPARATION (1 s.h.)

Preparation for the Society of Actuaries exam FM.

Pre-requisites: none

Co-requisites: ACTS:3080, if not taken previously

Requirements: none

Recommendations: none

Special Grading: Offered on S-F basis only for undergraduates; instructor has the option of using S-U grades for graduate level students

ACTS:4130 QUANTITATIVE METHODS FOR ACTUARIES (3 s.h.)

Survival distributions, life tables, life insurance, life annuities, premiums. Offered fall semesters.

Pre-requisites: STAT:3100 with a minimum grade of B- and ACTS:3080 with a minimum grade of C+

Co-requisites: STAT:4100 or STAT:5100

Requirements: Multivariate calculus and linear algebra; meet the pre-requisites or have graduate standing

Recommendations: none

Special Grading: none

Syllabus

ACTS:4150 FUNDAMENTALS OF SHORT-TERM ACTUARIAL MATHEMATICS (3 s.h.)

Severity, frequency, aggregate loss, estimation, credibility theory, pricing and reserving for short-term insurance coverages, option pricing. Offered spring semesters.

Pre-requisites: STAT:4100 with a minimum grade of C+ or STAT:5100 with a minimum grade of C+

Co-requisites: STAT:4101 or STAT:5101

Requirements: none

Recommendations: none

Special Grading: none

ACTS:4160 TOPICS IN ACTUARIAL SCIENCE (arr. s.h.)

Selected topics in actuarial science, financial mathematics and quantitative risk management.

Pre-requisites: none

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: Offered on S-F basis only for undergraduates; instructor has the option of using S-U grades for graduate level students

Syllabus

ACTS:4180 LIFE CONTINGENCIES I (3 s.h.)

Reserves, multi-life models, multiple-decrement models, Markov chains Offered spring semesters.

Pre-requisites: ACTS:3080 with a minimum grade of C+ and ACTS:4130 with a minimum grade of C+ and (STAT:4100 or STAT:5100) with a minimum grade of C+

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

Syllabus

ACTS:4280 LIFE CONTINGENCIES II (3 s.h.)

Multi-state models, pension mathematics, emerging costs for traditional and equity-linked insurance, profit testing, profit measures, embedded options. Offered fall semesters.

Pre-requisites: ACTS:4180 with a minimum grade of C+

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

Syllabus

ACTS:4380 MATHEMATICS OF FINANCE II (3 s.h.)

Derivatives markets, forwards, options, pricing models, and actuarial applications. Offered spring semesters.

Pre-requisites: ACTS:3080 with a minimum grade of C+

Co-requisites: none

Requirements: mathematical statistics, multivariate calculus and linear algebra

Recommendations: none

Special Grading: none

Syllabus

ACTS:4990 READINGS IN ACTUARIAL SCIENCE (arr. s.h.)

Supervised reading and research in actuarial science, financial mathematics or quantitative risk management. If you have not received permission from the instructor to add this section, your enrollment may be administratively dropped.

Pre-requisites: none

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

ACTS:6160 TOPICS IN ACTUARIAL SCIENCE (3 s.h.)

Selected topics in actuarial science, financial mathematics and quantitative risk management.

Pre-requisites: none

Co-requisites: none

Requirements: consent of instructor

Recommendations: none

Special Grading: none

Syllabus

ACTS:6200 PREDICTIVE ANALYTICS (3 s.h.)

Cross-References: DATA:6200, STAT:6200

Linear mixed models; generalized linear mixed models; generalized additive models; applications of these models using associated R packages.

Pre-requisites: STAT:4560

Co-requisites: STAT:4561

Requirements: comfort working with the R software environment.

Recommendations: none

Special Grading: none

Syllabus

ACTS:6480 LOSS DISTRIBUTIONS (3 s.h.)

Severity, frequency, and aggregate models and their modifications; risk measures; construction of empirical models. Offered spring semesters.

Pre-requisites: STAT:4101 or STAT:5101

Co-requisites: ACTS:6580

Requirements: none

Recommendations: none

Special Grading: none

Syllabus

ACTS:6580 CREDIBILITY AND SURVIVAL ANALYSIS (3 s.h.)

Construction and selection of parametric models; credibility; simulation.

Offered spring semesters.

Pre-requisites: STAT:4101 or STAT:5101

Co-requisites: ACTS:6480

Requirements: none

Recommendations: none

Special Grading: none

Syllabus

ACTS:6990 READINGS IN ACTUARIAL SCIENCE (arr. s.h.)

Supervised reading and research in actuarial science, financial mathematics or quantitative risk management. If you have not received permission from the instructor to add this section, your enrollment may be administratively dropped.

Pre-requisites: none

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

ACTS:7730 ADVANCED TOPICS IN ACTUARIAL SCIENCE/FINANCIAL MATHEMATICS (3 s.h.)

Selected advanced topics in actuarial science, financial mathematics and quantitative risk management.

Pre-requisites: none

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: Instructor has the option of using S-U for graduate level students

## Data Science Courses, Descriptions & Syllabi

DATA:1015 INTRODUCTION TO DATA SCIENCE (3 s.h.)

Cross-reference: STAT:1015

In today's world, massive amounts of data are increasingly collected and leveraged for knowledge discovery, policy assessment, and decision-making across many fields, including business, natural sciences, social sciences, and humanities. The course will cover the following topics: data collection, visualization, and data wrangling; basics of probability and statistical inference; fundamentals of data learning, including regression, classification, prediction, and cross-validation; computing, learning, and reporting in the R environment; and literate programming and reproducible research.

GE: quantitative or formal reasoning

Pre-requisites: none Co-requisites: none

Requirements: one year of high school algebra or MATH:0100

Recommendations: none

Special Grading: none

Syllabus: see STAT:1015

DATA:3120 PROBABILITY AND STATISTICS (4 s.h.)

Cross-References: STAT:3120, IGPI:3120

Models, discrete and continuous random variables and their distributions, estimation of parameters, testing statistical hypotheses.

Pre-requisites: MATH:1560 or MATH:1860

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

Syllabus: see STAT:3120

DATA:3200 APPLIED LINEAR REGRESSION (3 s.h.)

Cross-references: STAT:3200, IE:3760

Regression analysis with focus on applications; model formulation, checking, selection; interpretation and presentation of analysis results; simple and multiple linear regression; logistic regression; ANOVA, polynomial regression; tree models; bootstrapping; hands-on data analysis with computer software.

Prerequisite: STAT:2010 or STAT:2020 or STAT:3120

Syllabus: see STAT:3200

DATA:4540 STATISTICAL LEARNING (3 s.h.)

Cross-References: STAT:4540, BAIS:4540, IGPI:4540

Introduction to supervised and unsupervised statistical learning, with a focus on regression, classification, and clustering. Methods will be applied to real data using appropriate software. Supervised learning topics include: linear and non-linear (e.g. logistic) regression; linear discriminant analysis; cross-validation, bootstrapping, model selection, and regularization methods (e.g. ridge and lasso); generalized additive and spline models; tree-based methods, random forests and boosting; and support-vector machines. Unsupervised learning topics include: principal components and clustering.

Requirements: An introductory statistics course and a regression course.

Recommendations: Prior exposure to programming and/or software, such as R, SAS, and Matlab is recommended, but not required.

Syllabus: see STAT:4540

DATA:4580 DATA VISUALIZATION AND DATA TECHNOLOGIES (3 s.h.)

Cross-References: STAT:4580, IGPI:4580

Introduces common techniques for visualizing univariate and multivariate data, data summaries, and modeling results. Students will learn how to create and interpret these visualizations, and to assess effectiveness of different visualizations based on an understanding of human perception and statistical thinking. Data technologies for obtaining and preparing data for visualization and further analysis will also be discussed. Students will also learn how to present their results in written reports and to use version control to manage their work.

Requirements: An introductory statistics course and a regression course.

Recommendations: Prior exposure to programming and/or software suchas R or SAS, as obtained from a regression course is highly recommended.

Syllabus: see STAT:4580

DATA:4600 CAUSAL INFERENCE FOR DATA SCIENCE (3 s.h.)

Cross-Reference: STAT:4600

Introduce methods for reasoning about causes, effects, and bias when analyzing experimental and observational data. Topics include the potential outcomes framework, counterfactuals, confounding, and missing data; the identification and estimation of causal effects via propensity score methods, marginal structural models, instrumental variables, and directed acyclic graphs; as well as applications of machine learning and Bayesian methods to causal inference.

Prerequisite: (DATA:3120 or STAT:3120) and (DATA:3200 or STAT:3200)

Syllabus

DATA:4750 PROBABILISTIC STATISTICAL LEARNING (3 s.h)

Cross-Reference: STAT:4750

This course focuses on essential machine learning and statistics ideas that are critical in analyzing modern complex and large data. Selected topics are covered in supervised learning: linear models, deep neural networks, and non-parametric models. Besides supervised learning, essential topics from non-linear dimension reduction, clustering, and recommender systems are part of the course.

Pre-requisites: (CS:1210 with a minimum grade of C- or ENGR:2730 with a minimum grade of C-) and (MATH:2700 or MATH:2550) and (STAT:2010 or STAT:2020 or STAT:4200) and STAT:4540

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

Syllabus

DATA:4880 DATA SCIENCE CREATIVE COMPONENT (1 s.h.)

Readings, group discussions, and short-term projects within the area of data science; emphasis on communication of ideas learned in student's data science. coursework, data ethics, and potential bias in algorithms.

Pre-requisites: none

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: Offered on S-U basis only for all students

Syllabus

DATA:4890 DATA SCIENCE PRACTICUM (3 s.h.)

On- or off-campus internship or group-based consulting project that provides experience in a real-world setting. Students apply knowledge and techniques learned in coursework and practice communicating results to others.

Pre-requisites: none

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: Offered on S-U basis only for all students

Syllabus

DATA:5400 COMPUTING IN STATISTICS (3 s.h.)

Cross-References: STAT:5400, IGPI:5400

R; database management; graphical techniques; importing graphics into word-processing documents (e.g., LaTeX); creating reports in LaTeX; SAS; simulation methods (Monte Carlos studies, bootstrap, etc.).

Prerequisites: STAT:3200 and (STAT:3120 or STAT:3101 or STAT:4101).

Corerequisites: STAT:5100 and STAT:5200 if not already taken.

Syllabus: see STAT:5400

DATA:5890 M.S. DATA SCIENCE PRACTICUM (2 s.h.)

On- or off-campus internship or group-based consulting project that provides experience in a real-world setting. Students apply knowledge and techniques learned in coursework and practice communicating results to others.

Pre-requisites: none

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: Instructor has the option of using S-U grades for graduate level students

Syllabus

DATA:6200 PREDICTIVE ANALYTICS (3 s.h.)

Cross-References: ACTS:6200, STAT:6200

Linear mixed models; generalized linear mixed models; generalized additive models; applications of these models using associated R packages.

Pre-requisites: STAT:4560

Co-requisites: STAT:4561

Requirements: comfort working with the R software environment.

Recommendations: none

Special Grading: none

Syllabus: see ACTS:6200

DATA:6220 CONSULTING AND COMMUNICATION WITH DATA (3 s.h.)

Cross-Reference: STAT:6220

Realistic supervised data analysis experiences, including statistical packages, statistical graphics, writing statistical reports, dealing with complex or messy data.

Prerequisites: (STAT:3200 and STAT:3210), or (STAT:5200 and STAT:5201)

Offered spring semesters.

Requirement: Undergraduate majors should have within major GPA of 3.0 or higher, and grades of B- or higher in STAT:3200 and STAT:3210

Syllabus: see STAT:6220

DATA:7350 HIGH-DIMENSIONAL PROBABILITY FOR DATA SCIENCE (3 s.h.)

Non-asymptotic probability with a view towards applications in data science; concentration inequalities for functions of independent variables; martingale inequalities; entropy method; random matrices; matrix inequalities; suprema of random processes; sparse recovery.

Pre-requisites: STAT:5101

Co-requisites: none

Requirements: a course in linear algebra, familiarity with R or Python

Recommendations: none

Special Grading: none

Syllabus

DATA:7400 COMPUTER INTENSIVE STATISTICS (3 s.h.)

Cross-References: STAT:7400, IGPI:7400

Computer arithmetic; random variate generation; numerical optimization; numerical linear algebra; smoothing techniques; bootstrap methods; cross-validation; MCMC; EM and related algorithms, other topics per student/instructor interests.

Prerequisites: STAT:3101 and (STAT:5200 or BIOS:5610) and STAT:5400.

Requirements: proficiency in Fortran or C or C++ or Java.

Offered spring semesters.

Syllabus: see STAT:7400

## Statistics Courses, Descriptions & Syllabi

STAT:1000 FIRST-YEAR SEMINAR (1 s.h.)

Small discussion class taught by a faculty member; topics chosen by instructor; may include outside activities (e.g. films, lectures, performances, readings, visits to research facilities).

Prerequisite: first- or second-semester standing.

Syllabus

STAT:1010 STATISTICS AND SOCIETY (3 s.h.)

Statistical ideas and their relevance to public policy, business, humanities, and the social, health, and physical sciences; focus on critical approach to statistical evidence.

GE: quantitative or formal reasoning.

Requirement: one year of high school algebra or MATH:0100

Offered fall and spring semesters.

Syllabus

STAT:1015 INTRODUCTION TO DATA SCIENCE (3 s.h.)

Cross-reference: DATA:1015

In today's world, massive amounts of data are increasingly collected and leveraged for knowledge discovery, policy assessment, and decision-making across many fields, including business, natural sciences, social sciences, and humanities. The course will cover the following topics: data collection, visualization, and data wrangling; basics of probability and statistical inference; fundamentals of data learning, including regression, classification, prediction, and cross-validation; computing, learning, and reporting in the R environment; and literate programming and reproducible research.

GE: quantitative or formal reasoning.

Pre-requisites: none Co-requisites: none

Requirements: one year of high school algebra or MATH:0100

Recommendations: none

Special Grading: none

Syllabus

STAT:1020 ELEMENTARY STATISTICS AND INFERENCE (3 s.h.)

Cross-reference: PSQF:1020

Graphing techniques for presenting data, descriptive statistics, correlation, regression, prediction, logic of statistical inference, elementary probability models, estimation and tests of significance.

GE: quantitative or formal reasoning.

Requirement: one year of high school algebra or MATH:0100

Offered fall and spring semesters.

Syllabus

STAT:1030 STATISTICS FOR BUSINESS (4 s.h.)

Descriptive statistics, graphical presentation, elementary probability, estimation and testing, regression, correlation; statistical computer packages.

GE: quantitative or formal reasoning.

Prerequisite: none

Offered fall and spring semesters.

Syllabus

STAT:2010 STATISTICAL METHODS AND COMPUTING (3 s.h.)

Methods of data description and analysis using SAS: descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power; emphasis on learning statistical methods and concepts through hands-on experience with real data. STAT:2010 is a beginning methods course for undergraduate students.

GE: quantitative or formal reasoning.

Offered spring semesters.

Syllabus

STAT:2020 PROBABILITY AND STATISTICS FOR THE ENGINEERING AND PHYSICAL SCIENCES (3 s.h.) Probability, random variables, important discrete and continuous distributions, joint distributions, transformations of random variables, descriptive statistics, point and interval estimation, tests of hypotheses, regression.

Prerequisite: MATH:1560 or MATH1860.

Offered fall and spring semesters.

Syllabus

STAT:3100 INTRODUCTION TO MATHEMATICAL STATISTICS I (3 s.h.)

Descriptive statistics, probability, conditional probability, discrete and continuous univariate and multivariate distributions, sampling distributions.

Prerequisite: MATH:1560 or MATH:1860

Offered fall semesters.

Syllabus

STAT:3101 (22S:131) INTRODUCTION TO MATHEMATICAL STATISTICS II (3 s.h.)

Estimation, testing statistical hypothesis, simple regression, nonparametric methods.

Prerequisite: STAT:3100

Offered spring semesters.

Syllabus

STAT:3120 PROBABILITY AND STATISTICS (4 s.h.)

Cross-References: DATA:3120, IGPI:3120

Models, discrete and continuous random variables and their distributions, estimation of parameters, testing statistical hypotheses.

Prerequisite: MATH:1560 or MATH:1860

Offered fall and spring semesters.

Syllabus

STAT:3200 APPLIED LINEAR REGRESSION (3 s.h.)

Cross-references: DATA:3200, IE:3760

Regression analysis with focus on applications; model formulation, checking, selection; interpretation and presentation of analysis results; simple and multiple linear regression; logistic regression; ANOVA, polynomial regression; tree models; bootstrapping; hands-on data analysis with computer software.

Prerequisite: STAT:2010 or STAT:2020 or STAT:3120

Syllabus

STAT:3210 EXPERIMENTAL DESIGN AND ANALYSIS (3 s.h.)

Single- and multifactor experiments; analysis of variance; multiple comparisons; contrasts; diagnostics, fixed, random, and mixed effects models; designs with blocking and/or nesting; two-level factorials and fractions thereof; use of statistical computing packages.

Prerequisites: STAT:3200

Offered spring semesters.

Syllabus

STAT:3510 BIOSTATISTICS (3 s.h.)

Statistical concepts and methods for the biological sciences: descriptive statistics, elementary probability, sampling distributions, confidence intervals, parametric and nonparametric methods, one-way ANOVA, correlation and regression, categorical data.

Requirements: MATH:0100 or MATH:1005 or ALEKS [30]

Offered fall and spring semesters.

Syllabus

STAT:3620 QUALITY CONTROL (3 s.h.)

Cross-reference: IE:3600 -adminstrative home.

STAT:4100 MATHEMATICAL STATISTICS I (3 s.h.)

Probability, conditional probability, random variables, distribution and density functions, joint and conditional distributions, various families of discrete and continuous distributions, mgf technique for sums, convergence in distribution, convergence in probability, central limit theorem.

Prerequisites: MATH:2700 and MATH:2850.

Offered fall and spring semesters.

Syllabus

STAT:4101 MATHEMATICAL STATISTICS II (3 s.h.)

Transformations, order statistics, point estimation, sufficient statistics, Rao-Blackwell Theorem, delta method, confidence intervals, likelihood ratio tests, applications.

Prerequisite: STAT:4100

Offered fall and spring semesters.

Syllabus

STAT:4143 INTRODUCTION TO STATISTICAL METHODS (3 s.h.)

Cross-reference: PSQF:4143 -administrative home.

All questions regarding this course should be directed to the Department of Psychological and Quantitative Foundations, 335-5577.

STAT:4200 STATISTICAL METHODS AND COMPUTING (3 s.h.)

Methods of data description and analysis using SAS: descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power; emphasis on learning statistical methods and concepts through hands-on experience with real data. STAT:4200 is a beginning methods course for graduate students in non-statistics, less quantitative majors.

Syllabus

STAT:4520 BAYESIAN STATISTICS (3 s.h.)

Cross-reference: PSQF:4520

Bayesian statistical analysis, with focus on applications; Bayesian and frequentist methods compared; Bayesian model specification, choice of priors, computational methods; hands-on Bayesian data analysis using appropriate software; interpretation and presentation of analysis results.

Prerequisite: STAT:3200 and (STAT:3101 or STAT4101 or STAT:3120)

Syllabus

STAT:4540 STATISTICAL LEARNING (3 s.h.)

Cross-References: DATA:4540, BAIS:4540, IGPI:4540

Introduction to supervised and unsupervised statistical learning, with a focus on regression, classification, and clustering. Methods will be applied to real data using appropriate software. Supervised learning topics include: linear and non-linear (e.g. logistic) regression; linear discriminant analysis; cross-validation, bootstrapping, model selection, and regularization methods (e.g. ridge and lasso); generalized additive and spline models; tree-based methods, random forests and boosting; and support-vector machines. Unsupervised learning topics include: principal components and clustering.

Requirements: An introductory statistics course and a regression course.

Recommendations: Prior exposure to programming and/or software, such as R, SAS, and Matlab is recommended, but not required.

Syllabus

STAT:4560 STATISTICS FOR RISK MODELING I (3 s.h.)

Simple linear regression, multiple linear regression, model diagnostics, linear models from a statistical learning perspective, generalized linear models, implementations of these models on real data.

Pre-requisites: STAT:4101 with a minimum grade of C+ or STAT:5101 with a minimum grade of C+.

Co-requisites: none

Requirements: none

Recommendations: none

Syllabus

STAT:4561 STATISTICS FOR RISK MODELING II (3 s.h.)

Regression-based time series models, decision trees, principal components analysis, cluster analysis, implementations of these analytic techniques on real data.

Pre-requisites: STAT:4560 with a minimum grade of C+

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

STAT:4580 DATA VISUALIZATION AND DATA TECHNOLOGIES (3 s.h.)

Cross-references: DATA:4580, IGPI:4580

Introduces common techniques for visualizing univariate and multivariate data, data summaries, and modeling results. Students will learn how to create and interpret these visualizations, and to assess effectiveness of different visualizations based on an understanding of human perception and statistical thinking. Data technologies for obtaining and preparing data for visualization and further analysis will also be discussed. Students will also learn how to present their results in written reports and to use version control to manage their work.

Requirements: An introductory statistics course and a regression course.

Recommendations: Prior exposure to programming and/or software suchas R or SAS, as obtained from a regression course is highly recommended.

Syllabus

STAT:4600 CAUSAL INFERENCE FOR DATA SCIENCE (3 s.h.)

Cross-Reference: DATA:4600

Introduce methods for reasoning about causes, effects, and bias when analyzing experimental and observational data. Topics include the potential outcomes framework, counterfactuals, confounding, and missing data; the identification and estimation of causal effects via propensity score methods, marginal structural models, instrumental variables, and directed acyclic graphs; as well as applications of machine learning and Bayesian methods to causal inference.

Prerequisite: (DATA:3120 or STAT:3120) and (DATA:3200 or STAT:3200)

Syllabus: see DATA:4600

STAT:4740 LARGE DATA ANALYSIS (3 s.h.)

Cross-reference: CS:4740 -administrative home. Current areas that deal with problem of Big Data; techniques from computer science, mathematics, statistics; high performance and parallel computing, matrix techniques, cluster analysis, visualization; variety of applications including Google PageRank, seismology, Netflix-type problems, weather forecasting; fusion of data with simulation; projects.

Prerequisites: (CS:1210 with a minimum grade of C- or ENGR:2730 with a minimum grade of C-) and (MATH:2700 or MATH:2550) and (STAT:2010 or STAT:2020 or STAT:4200).

Syllabus

STAT:4750 PROBABILISTIC STATISTICAL LEARNING (3 s.h)

Cross-Reference: DATA:4750

This course focuses on essential machine learning and statistics ideas that are critical in analyzing modern complex and large data. Selected topics are covered in supervised learning: linear models, deep neural networks, and non-parametric models. Besides supervised learning, essential topics from non-linear dimension reduction, clustering, and recommender systems are part of the course.

Pre-requisites: (CS:1210 with a minimum grade of C- or ENGR:2730 with a minimum grade of C-) and (MATH:2700 or MATH:2550) and (STAT:2010 or STAT:2020 or STAT:4200) and STAT:4540

Co-requisites: none

Requirements: none

Recommendations: none

Special Grading: none

Syllabus

STAT:5090 ALPHA SEMINAR (1 s.h.)

Resources available to students, program requirements, tips for academic success, professional statistical organizations, library and career center resources, statistical computing, scientific document preparation, history of statistics.

Corequisite: Graduate standing in the Statistics program.

Syllabus

STAT:5100 STATISTICAL INFERENCE I) (3 s.h.)

Review of probability, distribution theory (multiple random variables, moment-generating functions, transformations, conditional distributions), sampling distributions, order statistics, convergence concepts, generating random samples.

Corequisites: MATH:2850 and STAT:3101

Offered fall semesters.

Syllabus

STAT:5101 STATISTICAL INFERENCE II (3 s.h.)

Continuation of STAT:5100; principles of data reduction, point estimation theory (MLE, Bayes, UMVU), hypothesis testing, interval estimation, decision theory, asymptotic evaluations.

Prerequisite: STAT:5100

Offered spring semesters.

Syllabus

STAT:5120 MATHEMATICAL METHODS FOR STATISTICS (3 s.h.)

Real numbers, point set theory, limit points, limits, sequences and series, Taylor series (multivariate), uniform convergence, Riemann-Stieltjes integrals.

Prerequisite: graduate standing in Statistics.

Offered spring semesters.

Syllabus

STAT:5200 APPLIED STATISTICS I (4 s.h.)

Descriptive statistics, basic inferential methods (confidence intervals, chi-square tests); linear models (regression and ANOVA models -- specification and assumption, fitting, diagnostics, selection, testing, interpretation; nonlinear models, logistic regression.

Prerequisite: STAT:3101

Corequisite: STAT:5100 or STAT:4100

Requirements: facility with matrix algebra.

Offered fall semesters.

Syllabus

STAT:5201 APPLIED STATISTICS II (3 s.h.)

Design of experiments, analysis of designed experiments. Recommendation: Prior exposure to SAS software.

Prerequisite: STAT:5200

Recommendation: Prior exposure to SAS software

Offered spring semesters.

Syllabus

STAT:5400 COMPUTING IN STATISTICS (3 s.h.)

Cross-References: DATA:5400, IGPI:5400

R; database management; graphical techniques; importing graphics into word-processing documents (e.g., LaTeX); creating reports in LaTeX; SAS; simulation methods (Monte Carlos studies, bootstrap, etc.).

Prerequisites: STAT:3200 and (STAT:3120 or STAT:3101 or STAT:4101).

Corerequisites: STAT:5100 and STAT:5200 if not already taken.

Syllabus

STAT:5610 DESIGN AND ANALYSIS OF BIOMEDICAL STUDIES (3 s.h.)

Cross-reference: BIOS:5120 -administrative home

STAT:5810 RESEARCH DATA MANAGEMENT (3 s.h.)

Cross-Reference: BIOS:5310 (administrative home).

Overview of problems encountered in gathering and processing data from biomedical investigations; introduction to data management techniques useful in biomedical studies; introduction to Microsoft Access. Offered fall semesters of odd years. Requirements: Fortran, C or Python programming capability or equivalent programming experience.

STAT:6200 PREDICTIVE ANALYTICS (3 s.h.)

Cross-References: ACTS:6200, DATA:6200

Linear mixed models; generalized linear mixed models; generalized additive models; applications of these models using associated R packages.

Pre-requisites: STAT:4560

Co-requisites: STAT:4561

Requirements: comfort working with the R software environment.

Recommendations: none

Special Grading: none

Syllabus

STAT:6220 CONSULTING AND COMMUNICATION WITH DATA (3 s.h.)

Cross-Reference: DATA:6220

Realistic supervised data analysis experiences, including statistical packages, statistical graphics, writing statistical reports, dealing with complex or messy data.

Prerequisites: (STAT:3200 and STAT:3210), or (STAT:5200 and STAT:5201)

Offered spring semesters.

Requirement: Undergraduate majors should have within major GPA of 3.0 or higher, and grades of B- or higher in STAT:3200 and STAT:3210

Syllabus

STAT:6300 PROBABILITY AND STOCHASTIC PROCESSES I (3 s.h.)

Conditional expectations; Markov chains, including random walks and gambler's ruin; classification of states; stationary distributions; branching processes; Poisson processes; Brownian motion.

Prerequisites: STAT:4100

Syllabus

STAT:6301 PROBABILITY AND STOCHASTIC PROCESSES II (3 s.h.)

Markov chains with continuous state space, Martingales, random walks, Brownian motion and other continuous-time Markov chains, simulation methods.

Prerequisite: STAT:6300

Syllabus

STAT:6513 INTERMEDIATE STATISTICAL METHODS (3 s.h.)

Cross-reference: PSQF:6243 -administrative home.

STAT:6514 CORRELATION AND REGRESSION (4 s.h.)

Cross-reference: PSQF:6244 -administrative home.

STAT:6516 DESIGN OF EXPERIMENTS (4 s.h.)

Cross-reference: PSQF:6246 -administrative home.

STAT:6530 ENVIRONMENTAL AND SPATIAL STATISTICS (3 s.h.)

Geostatistics, kriging, variogram estimation, trend estimation, sampling design, extensions to river networks and the globe,lattice data analysis, analysis of spatial point patterns.

Prerequisites: STAT:3200 and STAT:4101

Syllabus

STAT:6547 NONPARAMETRIC STATISTICAL METHODS (3 s.h.)

Cross-reference: PSQF:6247 -administrative home.

STAT:6550 INTRODUCTORY LONGITUDINAL DATA ANALYSIS (3 s.h.)

Same as BIOS:6310 -administrative home.

STAT:6560 APPLIED TIME SERIES ANALYSIS (3 s.h.)

General stationary, nonstationary models, autocovariance autocorrelation functions; stationary, nonstationary autoregressive integrated moving average models; identification, estimation, forecasting in linear models; use of statistical computer packages.

Prerequisites: STAT:3101 and (STAT:3200 or STAT:5200)

Offered spring semesters.

Syllabus

STAT:6970 TOPICS IN STATISTICS (3 s.h.)

Selected advanced topics in Statistics.

Pre-requisite: none

Co-requisites: none

Requirements: none

Special Grading: Instructor has the option of using S-U grades for graduate level students

Syllabus

STAT:6990 READINGS IN STATISTICS

Supervised reading and research in statistics.

Pre-requisite: none

Co-requisites: none

Requirements: none

Special Grading: Instructor has the option of using S-U grades for graduate level students

STAT:7100 ADVANCED INFERENCE I (3 s.h.)

Concepts of convergence, asymptotic methods including the delta method, sufficiency, asymptotic efficiency, Fisher information and information bounds for estimation, maximum likelihood estimation, the EM-algorithm, Bayes estimation, decision theory.

Prerequisites: STAT:5101 and STAT:5120

Syllabus

STAT:7101 ADVANCED INFERENCE II (3 s.h.)

Hypothesis testing, asymptotics of the likelihood ratio test, asymptotic efficiency, statistical functionals, robustness, bootstrap and jackknife, estimation with dependent data.

Prerequisite: STAT:7100

Syllabus

STAT:7190 SEMINAR: MATHEMATICAL STATISTICS (arr.)

Selected advanced topics in mathematical statistics.

Pre-requisits: none

Co-requisites: none

Requirements: none

Special Grading: Offered on S-F basis only for undergraduates; instructor has the option of using S-U grades for graduate level students.

STAT:7200 LINEAR MODELS (4 s.h.)

Linear spaces and selected topics in matrix algebra, full-rank and non-full-rank linear models, estimability, least squares and best linear unbiased estimation, multivariate normal distribution and distributions of quadratic forms, interval estimation, hypothesis testing, random and mixed models, best linear unbiased prediction, variance component estimation.

Prerequisites: STAT:5101 and STAT:5200 and STAT:5201

Syllabus

STAT:7290 SEMINAR: APPLIED STATISTICS (arr.)

Selected advanced topis in applied statistics.

Pre-requisits: none

Co-requisites: none

Requirements: none

Special Grading: Offered on S-F basis only for undergraduates; instructor has the option of using S-U grades for graduate level students.

STAT:7300 ADVANCED PROBABILITY (3 s.h.)

Probability theory, with emphasis on constructing rigorous proofs; measure spaces, measurable functions, random variables and induced measures, distribution functions, Lebesque integral, product measure and independence, Borel Cantelli lemma, modes of convergence.

Prerequisite: STAT:5120

Syllabus

STAT:7390 SEMINAR: PROBABILITY (arr.)

Selected advanced topics in probability.

Pre-requisits: none

Co-requisites: none

Requirements: none

Special Grading: Offered on S-F basis only for undergraduates; instructor has the option of using S-U grades for graduate level students.

STAT:7400 COMPUTER INTENSIVE STATISTICS (3 s.h.)

Cross-References: DATA:7400, IGPI:7400

Computer arithmetic; random variate generation; numerical optimization; numerical linear algebra; smoothing techniques; bootstrap methods; cross-validation; MCMC; EM and related algorithms, other topics per student/instructor interests.

Prerequisites: STAT:3101 and (STAT:5200 or BIOS:5610) and STAT:5400.

Requirements: proficiency in Fortran or C or C++ or Java.

Offered spring semesters.

Syllabus

STAT:7500 STATISTICAL MACHINE LEARNING (3 s.h.)

Cross-Reference: BAIS:7500

Regularization methods for sparse models, computational algorithms for large scale problems, statistical inference in high-dimensional models, reproducing kernel Hilbert space, supervised learning, nonparametric density and conditional density estimation, neural networks and deep learning, optimal transport and generative learning, dimension reduction and representation learning.

Prerequisite: STAT:5100 or STAT:5200

Syllabus

STAT:7510 ANALYSIS OF CATEGORICAL DATA (3 s.h.)

Cross-Reference: BIOS:7410

Models for discrete data, distribution theory, maximum likelihood and weighted least squares estimation for categorical data, tests of fit, models selection.

Prerequisites: STAT:4101 or STAT:5101, and STAT:5200 or BIOS:5620

Offered spring semesters.

Syllabus

STAT:7520 BAYESIAN ANALYSIS (3 s.h.)

Decision theory, conjugate families, structure of Bayesian inference, hierarchical models, asymptotic approximations for posterior distributions, Markov chain Monte Carlo methods and convergence assessment, model adequacy and model choice.

Prerequisites: STAT:5101, STAT:5200, and STAT:5400

Syllabus

STAT:7560 TIME SERIES ANALYSIS (3 s.h.)

Stationary time series, ARIMA models, spectral representation, linear prediction inference for the spectrum, multivariate time series, state space models and processes, nonlinear time series.

Prerequisites: STAT:4101 and (STAT::3200 or STAT:6560)

Syllabus

STAT:7570 SURVIVAL DATA ANALYSIS (3 s.h.)

Cross-reference: BIOS:7210 -administrative home.

STAT:7990 READING RESEARCH (arr.)

Repeatable.

Instructor has the option of using S-U grades.