Statistics Classes

Spring 2025 Fall 2025

Spring 2025

Visit the Registrar's Class Schedule for live registration information

Num. / Sec. / CRN	Name	Days	Time	Room	Instructor
STAT 112-01 30416	Introduction to Data Science	Days: M W F	Time: 09:40 am-10:40 am	Room: THEATR 205	Instructor: Amin Alhashim
Cross-listed with COMP 112-01 (30415) Details This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background. General Education Requirements: Quantitative Thinking Q2 Distribution Requirements: Natural science and mathematics Course Materials
STAT 112-02 30418	Introduction to Data Science	Days: M W F	Time: 10:50 am-11:50 am	Room: THEATR 205	Instructor: Amin Alhashim
Cross-listed with COMP 112-02 (30417) Details This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background. General Education Requirements: Quantitative Thinking Q2 Distribution Requirements: Natural science and mathematics Course Materials
STAT 112-03 30420	Introduction to Data Science	Days: M W F	Time: 12:00 pm-01:00 pm	Room: THEATR 205	Instructor: Amin Alhashim
Cross-listed with COMP 112-03 (30419) Details This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background. General Education Requirements: Quantitative Thinking Q2 Distribution Requirements: Natural science and mathematics Course Materials
STAT 125-01 30470	Epidemiology	Days: M W F	Time: 09:40 am-10:40 am	Room: THEATR 001	Instructor: Kelsey McDonald
Details Epidemiology is the study of the distribution and determinants of disease and health in human populations and the application of this understanding to the solution of public health problems. Topics include measurement of disease and health, the outbreak and spread of disease, reasoning about cause and effect, analysis of risk, detection and classification, and the evaluation of trade-offs. The course is designed to fulfill and extend the professional community's consensus definition of undergraduate epidemiology. In addition to the techniques of modern epidemiology, the course emphasizes the historical evolution of ideas of causation, treatment, and prevention of disease. The course is a required component of the concentration in Community and Global Health. General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-01 30471	Introduction to Statistical Modeling	Days: M W F	Time: 03:30 pm-04:30 pm	Room: OLRI 241	Instructor: Taylor Okonek
Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, experiment and study design, probability, hypothesis testing, multivariate regression, single and multi-way analysis of variance, logistic regression. General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-02 30472	Introduction to Statistical Modeling	Days: T R	Time: 09:40 am-11:10 am	Room: THEATR 202	Instructor: Jedidiah Carlson
Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, experiment and study design, probability, hypothesis testing, multivariate regression, single and multi-way analysis of variance, logistic regression. General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-03 30473	Introduction to Statistical Modeling	Days: T R	Time: 01:20 pm-02:50 pm	Room: THEATR 202	Instructor: Jedidiah Carlson
Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, experiment and study design, probability, hypothesis testing, multivariate regression, single and multi-way analysis of variance, logistic regression. General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-04 30474	Introduction to Statistical Modeling	Days: T R	Time: 03:00 pm-04:30 pm	Room: THEATR 202	Instructor: Jedidiah Carlson
Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, experiment and study design, probability, hypothesis testing, multivariate regression, single and multi-way analysis of variance, logistic regression. General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 212-01 30431	Intermediate Data Science	Days: M W	Time: 08:00 am-09:30 am	Room: THEATR 203	Instructor: Brianna Heggeseth
Cross-listed with COMP 212-01 (30430) Details This second course in the data science curriculum emphasizes advanced data wrangling and manipulation, interactive visualization, writing functions, working with data in databases, version control, and data ethics. Through open-ended and interdisciplinary projects, students practice the constant feedback loop of asking questions of the data, manipulating the data to help answer the question, and then returning to more questions. Prerequisite(s): COMP 112 and COMP 123 and STAT 155; STAT 253 recommended but not required. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 253-01 30475	Statistical Machine Learning	Days: T R	Time: 03:00 pm-04:30 pm	Room: OLRI 241	Instructor: Kelsey Grinde
Details The linear and logistic modeling techniques from STAT 155 are augmented with the three foundational machine learning tasks: regression, classification, and clustering. The course explores techniques central to these tasks, including methods of data exploration, supervised and unsupervised learning, parametric and nonparametric modeling, and model training and evaluation. As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 253-02 30476	Statistical Machine Learning	Days: T R	Time: 09:40 am-11:10 am	Room: THEATR 200	Instructor: Brianna Heggeseth
Details The linear and logistic modeling techniques from STAT 155 are augmented with the three foundational machine learning tasks: regression, classification, and clustering. The course explores techniques central to these tasks, including methods of data exploration, supervised and unsupervised learning, parametric and nonparametric modeling, and model training and evaluation. As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 253-03 30477	Statistical Machine Learning	Days: T R	Time: 01:20 pm-02:50 pm	Room: THEATR 200	Instructor: Brianna Heggeseth
Details The linear and logistic modeling techniques from STAT 155 are augmented with the three foundational machine learning tasks: regression, classification, and clustering. The course explores techniques central to these tasks, including methods of data exploration, supervised and unsupervised learning, parametric and nonparametric modeling, and model training and evaluation. As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 354-01 30464	Probability	Days: T R	Time: 01:20 pm-02:50 pm	Room: OLRI 241	Instructor: Alicia Johnson
Permission of instructor required; cross-listed with MATH 354-01 (30463) Details An introduction to probability theory and application. Fundamental probability concepts include: sample spaces, combinatorics, conditional probability, independence, random variables, probability distributions, expectation, variance, moment-generating functions, and limit theorems. Special course topics vary and may include: computer simulation, stochastic processes, and statistical inference. Prerequisite(s): MATH 137 or MATH 237 General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 355-01 30770	Statistical Theory	Days: M W F	Time: 02:20 pm-03:20 pm	Room: OLRI 241	Instructor: Taylor Okonek
Permission of instructor required; cross-listed with MATH 355-01 (30771) Details An important course for students considering graduate work in statistics or biostatistics, this course explores the mathematical theory underlying modern statistical techniques. Topics include the theory behind: parameter estimation, evaluation of estimator properties, hypothesis testing, confidence intervals, and linear regression. Special topics vary and may include: tests of independence, resampling techniques, introductory Bayesian concepts, and nonparametric methods. Prerequisite(s): STAT 155, MATH 236, MATH 354/STAT 354. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 494-01 30478	Statistical Genetics	Days: T R	Time: 09:40 am-11:10 am	Room: OLRI 241	Instructor: Kelsey Grinde
Permission of instructor required Details Statistical methods for analyzing genetic data and understanding the genetic basis of human diseases and traits are at the heart of government-sponsored precision medicine initiatives, genetic testing routinely conducted at health clinics, and direct-to-consumer genetic and ancestry testing offered by companies like 23andMe and AncestryDNA. Statistical geneticists work to answer these important scientific questions while navigating the unique statistical challenges posed by genetic data such as their large size (p >> n) and complicated correlation structure. In this course, we will explore advanced statistical methods that can address these challenges, applying ideas from other statistics courses (e.g., linear models, principal component analysis) in the context of genetic data. Throughout, we will place a particular emphasis on scientific communication and computing. Prior knowledge of (or at least interest in) genetics will be helpful, but is not formally required. This course will conclude with a final project that could be presented at the MSCS Capstone Days. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials

Fall 2025

Visit the Registrar's Class Schedule for live registration information

Num. / Sec. / CRN	Name	Days	Time	Room	Instructor
STAT 112-01 12524	Introduction to Data Science	Days: T R	Time: 08:00 am-09:30 am	Room:	Instructor: Andrew Beveridge
Cross-listed with COMP 112-01 (12523); registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background. General Education Requirements: Quantitative Thinking Q2 Distribution Requirements: Natural science and mathematics Course Materials
STAT 112-02 12526	Introduction to Data Science	Days: T R	Time: 09:40 am-11:10 am	Room:	Instructor: Andrew Beveridge
Cross-listed with COMP 112-02 (12525); registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background. General Education Requirements: Quantitative Thinking Q2 Distribution Requirements: Natural science and mathematics Course Materials
STAT 112-03 12528	Introduction to Data Science	Days: T R	Time: 01:20 pm-02:50 pm	Room:	Instructor: Jedidiah Carlson
Cross-listed with COMP 112-03 (12527); registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background. General Education Requirements: Quantitative Thinking Q2 Distribution Requirements: Natural science and mathematics Course Materials
STAT 112-04 12530	Introduction to Data Science	Days: T R	Time: 03:00 pm-04:30 pm	Room:	Instructor: Jedidiah Carlson
Cross-listed with COMP 112-04 (12529); registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background. General Education Requirements: Quantitative Thinking Q2 Distribution Requirements: Natural science and mathematics Course Materials
STAT 125-01 12575	Epidemiology	Days: M W F	Time: 12:00 pm-01:00 pm	Room:	Instructor: Kelsey McDonald
Permission of instructor required; first day attendance required Details Epidemiology is the study of the distribution and determinants of disease and health in human populations and the application of this understanding to the solution of public health problems. Topics include measurement of disease and health, the outbreak and spread of disease, reasoning about cause and effect, analysis of risk, detection and classification, and the evaluation of trade-offs. The course is designed to fulfill and extend the professional community's consensus definition of undergraduate epidemiology. In addition to the techniques of modern epidemiology, the course emphasizes the historical evolution of ideas of causation, treatment, and prevention of disease. The course is a required component of the concentration in Community and Global Health. General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-01 12576	Introduction to Statistical Modeling	Days: T R	Time: 01:20 pm-02:50 pm	Room:	Instructor: Alicia Johnson
First day attendance required; registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing). General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-02 12577	Introduction to Statistical Modeling	Days: T R	Time: 03:00 pm-04:30 pm	Room:	Instructor: Alicia Johnson
First day attendance required; registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing). General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-03 12578	Introduction to Statistical Modeling	Days: M W F	Time: 09:40 am-10:40 am	Room:	Instructor: STAFF
First day attendance required; registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing). General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-04 12579	Introduction to Statistical Modeling	Days: M W F	Time: 12:00 pm-01:00 pm	Room:	Instructor: STAFF
First day attendance required; registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing). General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 155-05 12580	Introduction to Statistical Modeling	Days: T R	Time: 09:40 am-11:10 am	Room:	Instructor: Jedidiah Carlson
First day attendance required; registration limit adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Year students Details An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing). General Education Requirements: Quantitative Thinking Q3 Distribution Requirements: Natural science and mathematics Course Materials
STAT 202-01 12831	Data and Society	Days: M W F	Time: 03:30 pm-04:30 pm	Room:	Instructor: Brianna Heggeseth
Permission of instructor required; 2 credits Details This course is designed to encourage and facilitate students’ collaboration with real data in society and the various stakeholders in a supportive environment. Students in this course will work in small groups to engage in data-focused work in partnership with a local office or organization. Through independent and collaborative learning, students will learn new data science and statistical methods and skills needed for the data project, will develop communication and project management skills, and will regularly reflect individually and as a cohort, learning from each other as well as their organizational partners. This course does not fulfill any major, minor, or concentration requirements. Prerequisite(s): COMP/STAT 112 and STAT 155 General Education Requirements: Distribution Requirements: Course Materials
STAT 212-01 12541	Intermediate Data Science	Days: M W F	Time: 12:00 pm-01:00 pm	Room:	Instructor: Amin Alhashim
Cross-listed with COMP 212-01 (12540) Details This second course in the data science curriculum emphasizes advanced data wrangling and manipulation, interactive visualization, writing functions, working with data in databases, version control, and data ethics. Through open-ended and interdisciplinary projects, students practice the constant feedback loop of asking questions of the data, manipulating the data to help answer the question, and then returning to more questions. Prerequisite(s): COMP 112 and COMP 123 and STAT 155; STAT 253 recommended but not required. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 253-01 12581	Statistical Machine Learning	Days: T R	Time: 08:00 am-09:30 am	Room:	Instructor: Kelsey Grinde
First day attendance required Details The linear and logistic modeling techniques from STAT 155 are augmented with the three foundational machine learning tasks: regression, classification, and clustering. The course explores techniques central to these tasks, including methods of data exploration, supervised and unsupervised learning, parametric and nonparametric modeling, and model training and evaluation. As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 253-02 12582	Statistical Machine Learning	Days: T R	Time: 09:40 am-11:10 am	Room:	Instructor: Kelsey Grinde
First day attendance required Details The linear and logistic modeling techniques from STAT 155 are augmented with the three foundational machine learning tasks: regression, classification, and clustering. The course explores techniques central to these tasks, including methods of data exploration, supervised and unsupervised learning, parametric and nonparametric modeling, and model training and evaluation. As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 253-03 12583	Statistical Machine Learning	Days: T R	Time: 03:00 pm-04:30 pm	Room:	Instructor: Kelsey Grinde
First day attendance required Details The linear and logistic modeling techniques from STAT 155 are augmented with the three foundational machine learning tasks: regression, classification, and clustering. The course explores techniques central to these tasks, including methods of data exploration, supervised and unsupervised learning, parametric and nonparametric modeling, and model training and evaluation. As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155. General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 354-01 12567	Probability	Days: T R	Time: 08:00 am-09:30 am	Room:	Instructor: Taylor Okonek
Permission of instructor required; cross-listed with MATH 354-01 (12566) Details An introduction to probability theory and application. Fundamental probability concepts include: sample spaces, combinatorics, conditional probability, independence, random variables, probability distributions, expectation, variance, moment-generating functions, and limit theorems. Special course topics vary and may include: computer simulation, stochastic processes, and statistical inference. Prerequisite(s): MATH 137 or MATH 237 General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 354-02 12569	Probability	Days: T R	Time: 09:40 am-11:10 am	Room:	Instructor: Taylor Okonek
Permission of instructor required; cross-listed with MATH 354-02 (12568) Details An introduction to probability theory and application. Fundamental probability concepts include: sample spaces, combinatorics, conditional probability, independence, random variables, probability distributions, expectation, variance, moment-generating functions, and limit theorems. Special course topics vary and may include: computer simulation, stochastic processes, and statistical inference. Prerequisite(s): MATH 137 or MATH 237 General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 452-01 12585	Correlated Data	Days: T R	Time: 09:40 am-11:10 am	Room:	Instructor: Brianna Heggeseth
Permission of instructor required; first day attendance required Details One of the most common assumptions made in Statistics is that observations are independent; however, there are many situations in which the data violate this assumption by design. In this class, we discuss advanced visualization and modeling approaches for when the data are correlated. Topics will include time series analysis, longitudinal data analysis, and spatial data analysis. Applications are drawn from across the disciplines. Prerequisite(s): STAT 155 and MATH 354/STAT 354 General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 452-02 12586	Correlated Data	Days: T R	Time: 01:20 pm-02:50 pm	Room:	Instructor: Brianna Heggeseth
Permission of instructor required; first day attendance required Details One of the most common assumptions made in Statistics is that observations are independent; however, there are many situations in which the data violate this assumption by design. In this class, we discuss advanced visualization and modeling approaches for when the data are correlated. Topics will include time series analysis, longitudinal data analysis, and spatial data analysis. Applications are drawn from across the disciplines. Prerequisite(s): STAT 155 and MATH 354/STAT 354 General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials
STAT 456-01 12549	Projects in Data Science	Days: T R	Time: 01:20 pm-02:50 pm	Room:	Instructor: Shilad Sen
Cross-listed with COMP 456-01 (12548) Details This third course in the data science curriculum is a capstone course that emphasizes team-based learning through open-ended data science projects. Working with a team throughout the course of the semester you will take on an interdisciplinary in-depth data science project and gain experience in developing and refining research questions, identifying and wrangling datasets, and clearly presenting results and conclusions. Mini-lectures by the instructor, guest speakers, and students will present advanced topics that supplement and support team-based learning. Counts as a capstone course for the Computer Science major and the Data Science major. Prerequisite(s): STAT 212 and STAT 253 General Education Requirements: Distribution Requirements: Natural science and mathematics Course Materials

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Spring 2025

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