STATISTICS (STAT)
College of Arts and Sciences, Interdisciplinary Program: Department of Computer and Information Sciences (OSS 402) and Department of Mathematics (OSS 201), (651) 962-5520
Shemyakin (MATH) committee chair;
Advisory committee: Berg (CISC), Curran (CISC), Dwyer (CISC), McNamara (CISC), Werness (CISC)
Statistics is an interdisciplinary major that draws upon faculty and courses in the departments of Computer and Information Sciences and Mathematics. The major is administered by a committee of representatives from both departments. This joint major allows students to pursue an interest in mathematical statistics, applied statistics, and related areas including biostatistics, operations research, and data mining.
Program Faculty
Sergey Berg berg2294@stthomas.edu
Associate Professor (651) 962-5382
Erin Curran ecurran@stthomas.edu
Associate Professor (651) 962-5397
Anna Dwyer dwye9852@stthomas.edu
Clinical Faculty (651) 962-5393
Amelia McNamara amelia.mcnamara@stthomas.edu
Associate Professor (651) 962-5391
Arkady Shemyakin a9shemyakin@stthomas.edu
Professor (651) 962-5522
Mark Werness mewerness@stthomas.edu
Associate Professor (651) 962-5471
Major in Statistics (B.S.)
- MATH 113 Calculus I (or MATH 108 and MATH 109) (4 credits)
- MATH 114 Calculus II (4 credits)
- MATH 128 Intro to Discrete Mathematics or MATH 240 Linear Algebra (4 credits)
- CISC 131 (or CISC 130) Intro Programming and Problem Solving (4 credits)
- STAT 360 Computational Methods in Statistics (4 credits)
- STAT 400 Data Mining and Machine Learning (4 credits)
- STAT 460 Statistical Research/Practicum - a capstone experience (4 credits)
Plus:
- Concentration in Mathematical Statistics or Applied Statistics
Concentration in Mathematical Statistics
- MATH 200 Multi-variable Calculus (4 credits)
- MATH 313 Probability (4 credits)
- STAT 314 Mathematical Statistics (4 credits)
- STAT 333 Predictive Modeling: Regression, GLM, Forecasting (4 credits)
- Plus eight credits from the list of electives below.
Concentration in Applied Statistics
- STAT 220 Statistics I (4 credits)
- STAT 320 Statistics II (4 credits)
- Plus sixteen credits from the list of electives below.
Electives
- ACSC 364 Mathematical Finance (4 credits)
- STAT 310 Biostatistics (4 credits)
- STAT 336 Data Communication and Visualization (4 credits)
- STAT 370 Bayesian Statistical Models and Credibility Theory (4 credits)
- STAT 380 Spatial Statistics (4 credits)
- STAT 413 Generalized Linear Mixed Models (4 credits)
- STAT 414 Network Models and Simulations (4 credits)
Minor in Statistics
This joint minor allows students to pursue an interest in mathematical statistics, applied statistics, and related areas including biostatistics, operations research, and data mining.
Required courses (each of two tracks includes 6 courses with MATH or STAT designation numbered in the brackets):
Required courses for Mathematical and Applied Staistics tracks:
- MATH 113 Calculus (1) (or MATH 108 and MATH 109) (4 credits)
Plus one of the two tracks below-
Mathematical Statistics track:
- MATH 114 Calculus II (4 credits)
- MATH 200 Multivariable Calculus (4 credits)
- MATH 240 Linear Algebra (4 credits)
- MATH 313 Probability (4 credits)
- STAT 314 Mathematical Statistics (4 credits)
Applied Statistics track:
- CISC 131 (or CISC 130) Intro Programming and Problem Solving (4 credits)
- STAT 220 Introduction to Statistics (4 credits)
- STAT 320 Applied Regression Analysis (4 credits)
- STAT 360 Computational Methods in Statistics (4 credits)
Plus four credits from the following electives:
- STAT 310 Biostatistics (4 credits)
- STAT 336 Data Communication and Visualization (4 credits)
- STAT 370 Bayesian Statistical Models and Credibility Theory (4 credits)
- STAT 380 Spatial Statistics (4 credits)
- STAT 400 Data Mining and Machine Learning (4 credits)
- STAT 413 Generalized Linear Mixed Models (4 credits)
- STAT 414 Network Models and Simulations (4 credits)
Statistics Undergraduate Courses
| Course Number | Title | Credits | |
|---|---|---|---|
| STAT 120 | Introduction to Data Science | 4 | |
| Description of course Introduction to Data Science : | This course provides students with an introduction to the field of data science. Students learn foundational skills, including basic data visualization, data wrangling, descriptive modeling techniques, and simulation-based inference. All material is grounded in contextual data examples, and consideration of data context and ethical issues is paramount. | ||
| STAT 201 | Introductory Statistics II | 2 | |
| Description of course Introductory Statistics II : | This course provides students who already have a solid conceptual understanding of statistics the opportunity to apply their knowledge to analyzing data using modern statistical software. Topics include data visualization, inference for one and two samples, analysis of variance, chi-square tests for goodness of fit and association, and simple and multiple linear regression. Prerequisites: STAT 206 or AP Statistics Credit. Note, students who receive credit for STAT 201 may not receive credit for STAT 220. | ||
| STAT 206 | Introductory Statistics I | 2 TO 4 | |
| Description of course Introductory Statistics I : | For transfer articulation purposes only. Used when the transferred course does not include extensive data analysis using modern statistical software that is an essential component of STAT 220. | ||
| STAT 220 | Introductory Statistics | 4 | |
| Description of course Introductory Statistics : | This course is composed of an in-depth study of the processes through which statistics can be used to learn about environments and events. There will be an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in a variety of real-world contexts. Topics include data collection, research design, data visualization, bootstrap confidence intervals, inference for one and two samples, randomized hypothesis testing, analysis of variance, chi-square tests for goodness of fit and association, and simple and multiple linear regression. Extensive data analysis using modern statistical software is an essential component of this course. Prerequisites: Math placement at level of MATH 108 or above; or MATH 006, 100, 101, 103, 104, 105, 108, 109, 111, or 113. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201 or STAT 206. | ||
| STAT 298 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| STAT 303 | Statistics/Applied Sciences | 4 | |
| Description of course Statistics/Applied Sciences : | Probability, Estimation, Hypothesis Testing, Analysis of Variance, Regression Analysis, Topics selected from Experimental Design, Statistical Process Control, Non-Parametric Methods, Factor Analysis as time permits. Prerequisite: A grade of C- or above in MATH 114. NOTE: Students who receive credit for STAT 303 may not receive credit for STAT 313 or STAT 314. | ||
| STAT 310 | Biostatistics | 4 | |
| Description of course Biostatistics : | In this course, students acquire the knowledge and skill required to effectively apply intermediate statistical methods in biology, medicine, public health, and other health-related fields. There is an emphasis on the following inferential statistical techniques: one-way and factorial ANOVA, interactions, repeated measures, and general linear models; logistic regression for cohort and case-control studies; nonparametric and distribution-free statistics; loglinear models and contingency table analyses; survival data, Kaplan-Meier methods, and proportional hazards models. Prerequisites: STAT 201 or STAT 220 or STAT 314 or MATH 303 | ||
| STAT 313 | Probability | 4 | |
| Description of course Probability : | Probability theory in discrete and continuous sample spaces; random variables and distribution functions; moments; the moment-generating function; functions of random variables; law of large numbers; central limit theorem. Prerequisites: A grade of C- or above in MATH 200 (may be taken concurrently with consent of instructor). NOTE: Students who receive credit for STAT 313 may not receive credit for STAT 303. | ||
| STAT 314 | Mathematical Statistics | 4 | |
| Description of course Mathematical Statistics : | Students will learn the theory and applications of point estimation, interval estimation, and hypothesis testing. Students will construct intervals and tests using a variety of statistical tools including frequentist statistical theory, Bayesian statistical theory, and resampling-based simulation. Prerequisites: Grades C- or higher in MATH 240 and STAT 313. NOTE: Students who receive credit for STAT 314 may not receive credit for STAT 303. | ||
| STAT 320 | Applied Regression Analysis | 4 | |
| Description of course Applied Regression Analysis : | This course provides students with the knowledge to effectively use various forms of regression models to address problems in a variety of fields. Students learn both simple and multiple forms of linear, ordinal, nominal, and beta regression models. There is an emphasis on simultaneous inference, model selection and validation, detecting collinearity and autocorrelation, and remedial measures for model violations. Students are also introduced to the use of time series and forecasting methods. Prerequisites: Grades C- or higher in STAT 201 or STAT 220 or STAT 314 or MATH 303. | ||
| STAT 333 | Predictive Modeling | 4 | |
| Description of course Predictive Modeling : | The course introduces the theory and applications of simple and multiple regression methods, including model construction and selection, transformation of variables and residual analysis; introduction to GLM (generalized linear models) for categorical and count response variables; time series analysis with ARIMA (autoregressive integrated moving average models). Students are introduced to principles of data collection and analysis, learn to work with statistical literature. Students present a writing intensive small group course project. Prerequisites: Grades C- or higher in MATH 240 and in STAT 303 or STAT 314. | ||
| STAT 336 | Data Comm and Visualization | 4 | |
| Description of course Data Comm and Visualization : | This course will prepare students to effectively communicate the insights from data analysis. The course will cover the three main methods of communicating information about data—visually, orally, and in writing. Students will learn to tailor their communication to their audience and create publication-ready and boardroom-ready presentations of their results. Prerequisites: CISC 130 or 131; AND STAT 201 or STAT 220 or STAT 314 or MATH 303. | ||
| STAT 360 | Comp STAT & Data Analysis | 4 | |
| Description of course Comp STAT & Data Analysis : | This course introduces students to advanced computational methods in statistics and data analysis that require a thorough knowledge of a programming language such as Python or R. There will be an intensive focus on investigating the correlation and covariance structure of data, including data extraction and modification, dimensionality reduction, and structural equation modeling. Prerequisites: Grades C- or higher in CISC 130 or 131; AND MATH 109, 112 or 113; AND STAT 320 or 333 or ECON 315. | ||
| STAT 370 | Bayesian Statistical Models | 4 | |
| Description of course Bayesian Statistical Models : | The course covers a range of statistical models used in applications including Actuarial Science, Finance, Health, and Social Sciences. It is oriented towards practical model construction and problem solving. The theory of Monte Carlo and Markov Chain Monte Carlo simulation is considered as well as its practical implementation. Credibility theory serves as one of the major applications. Prerequisites: MATH 109, 112 or 113; AND STAT 314 or DASC 240. | ||
| STAT 380 | Spatial Statistics | 4 | |
| Description of course Spatial Statistics : | This course provides students with the background necessary to investigate spatially-referenced data and processes. There is an emphasis on specifying and fitting hierarchical models to represent geostatistical or point-referenced data, lattice or aerial data, and point process data. Students will also be introduced to the use of formal spatial data structures, point pattern analysis and cluster detection, spatial interpolation and kriging, spatial autocorrelation and variogram analysis, and spatial autoregressive models. Prerequisites: STAT 320 or STAT 333 | ||
| STAT 400 | Data Mining & Machine Learning | 4 | |
| Description of course Data Mining & Machine Learning : | In this course students will learn methods for working with massive and complex data. They will explore these topics from both statistical and computational perspectives. Topics include data preparation, defining and exploring data sources, pattern discovery, cluster analysis, decision trees, regression, neural networks, memory-based reasoning, survival analysis, and genetic algorithms. Software used in the course includes, but is not limited to, JMP, Excel, Java, R, Python, and Minitab. Prerequisites: Grades C- or higher in CISC 130 or 131 AND MATH 109, 112 or 113; AND STAT 320 or 333 or ECON 315. | ||
| STAT 413 | Generalized Linear Mixed Model | 4 | |
| Description of course Generalized Linear Mixed Model : | This course provides students we a review of methods of inference in the context of the linear model and generalized linear model. Students will then learn about correlation structures and linear models, and finally will create and conduct inference on generalized linear models. The course will emphasize analyzing real world data using modern statistical software. Additionally, students will understand the statistical theory underlying inference for generalized linear mixed models. Prerequisites: STAT DASC 360 | ||
| STAT 414 | Network Models and Simulations | 4 | |
| Description of course Network Models and Simulations : | This course provides a systematic approach to the use of network modeling in the understanding and prediction of complex social, technological, and biological systems such as the emergence of fake news, the exchange of information across network routers, and the spread of infectious diseases. There will be an emphasis on efficient numerical methods for describing, visualizing, constructing, and simulating processes across both directed and undirected networks that may be static or dynamic in nature. Prerequisites: STAT 320 or 333 | ||
| STAT 460 | STAT & Data Science Practicum | 4 | |
| Description of course STAT & Data Science Practicum : | This course provides students the opportunity to develop and pursue an advanced statistical analysis with real world relevance and application. In addition to working with a faculty instructor, students are also given the opportunity to collaborate with professional mentors from various industries and to participate in national competitions. Previous sponsors include the Minnesota Department of Natural Resources, the Travelers Companies, U.S. Bancorp, SCOR Reinsurance, Drake Bank, and numerous professors from other departments at St. Thomas. Grade of C- or higher in STAT 360 and senior standing. | ||
| STAT 476 | Experiential Learning | 1 TO 4 | |
| Description of course Experiential Learning : | No description is available. | ||
| STAT 490 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| STAT 495 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||