{"id":668,"date":"2019-04-01T17:17:46","date_gmt":"2019-04-01T17:17:46","guid":{"rendered":"https:\/\/www.macalester.edu\/160-mscs\/?page_id=668"},"modified":"2024-07-08T15:22:40","modified_gmt":"2024-07-08T15:22:40","slug":"statistics","status":"publish","type":"page","link":"https:\/\/www.macalester.edu\/mscs\/schedules\/statistics\/","title":{"rendered":"Statistics Classes"},"content":{"rendered":"
\n <\/a>\n

\n \n Spring 2026<\/a>\n \n \n Fall 2026<\/a>\n \n \n\t\n\t\n\t\n <\/p>\n \n\t

Spring 2026<\/h2>\n

ÈÕº«¾«Æ· the Registrar's Class Schedule for live registration information<\/a><\/p>\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Num. \/ Sec. \/ CRN<\/th>\n Name<\/th>\n Days<\/th>\n Time<\/th>\n Room<\/th>\n Instructor<\/th>\n <\/th>\n \n <\/tr>\n <\/thead>\n
STAT 112-01 32422<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>OLRI 254\n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-01 (32421); Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-02 32424<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>03:00 pm-04:30 pm\n <\/td>\n \n Room: <\/span>OLRI 254\n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-02 (32423); Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-03 32426<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>OLRI 254\n <\/td>\n \n Instructor: <\/span>Dan Drake\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-03 (32425);Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-01 32475<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span>THEATR 200\n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-02 32476<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>02:20 pm-03:20 pm\n <\/td>\n \n Room: <\/span>THEATR 200\n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-03 32477<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>THEATR 202\n <\/td>\n \n Instructor: <\/span>Jedidiah Carlson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-04 32478<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>THEATR 202\n <\/td>\n \n Instructor: <\/span>Jedidiah Carlson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-05 32479<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span>THEATR 202\n <\/td>\n \n Instructor: <\/span>Alicia Johnson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-06 32480<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>THEATR 203\n <\/td>\n \n Instructor: <\/span>Md Mutasim Billah\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-07 32481<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span>THEATR 203\n <\/td>\n \n Instructor: <\/span>Md Mutasim Billah\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for First Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 202-01 32482<\/span><\/td>\n Data and Society<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>03:30 pm-04:30 pm\n <\/td>\n \n Room: <\/span>THEATR 200\n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required; 2 credits*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course is designed to encourage and facilitate students\u2019 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. May be repeated for credit. Offered occasionally. (2 credits) Prerequisite(s): STAT 155 and STAT\/COMP 112\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 212-01 32437<\/span><\/td>\n Intermediate Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 212-01 (32436)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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\u00a0and\u00a0COMP 123\u00a0and\u00a0STAT 155;\u00a0STAT 253\u00a0recommended but not required.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 253-01 32483<\/span><\/td>\n Statistical Machine Learning<\/td>\n \n Days: <\/span>M W \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span>OLRI 254\n <\/td>\n \n Instructor: <\/span>Kelsey Grinde\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n The linear and logistic modeling techniques from\u00a0STAT 155\u00a0are augmented with the three foundational machine learning tasks: regression, classification, and clustering. \u00a0The 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. \u00a0As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 253-02 32484<\/span><\/td>\n Statistical Machine Learning<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>OLRI 245\n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n The linear and logistic modeling techniques from\u00a0STAT 155\u00a0are augmented with the three foundational machine learning tasks: regression, classification, and clustering. \u00a0The 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. \u00a0As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 253-03 32485<\/span><\/td>\n Statistical Machine Learning<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>02:20 pm-03:20 pm\n <\/td>\n \n Room: <\/span>THEATR 213\n <\/td>\n \n Instructor: <\/span>Md Mutasim Billah\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n The linear and logistic modeling techniques from\u00a0STAT 155\u00a0are augmented with the three foundational machine learning tasks: regression, classification, and clustering. \u00a0The 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. \u00a0As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 354-01 32469<\/span><\/td>\n Probability<\/td>\n \n Days: <\/span>M W \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Taylor Okonek\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required; cross-listed with MATH 354-01 (32468)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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\u00a0or MATH 237\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 355-01 32471<\/span><\/td>\n Statistical Theory<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>OLRI 254\n <\/td>\n \n Instructor: <\/span>Kelsey Grinde\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required; first day attendance required; cross-listed with MATH 355-01 (32470)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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 non\u00adparametric methods. Prerequisite(s): STAT 155, MATH 236,\u00a0MATH 354\/STAT 354.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 454-01 32486<\/span><\/td>\n Bayesian Statistics<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Taylor Okonek\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Bayesian statistics, an alternative to the traditional frequentist approach taken in our other statistics courses, is playing an increasingly integral role in modern statistics. The Bayesian philosophy is natural, allowing us to formally balance data with our prior knowledge, and updating this knowledge as more data come in. It answers natural questions. It can shine in settings where frequentist "likelihood" methods break down. And it is becoming increasingly popular with the availability of computing tools necessary to its implementation. This course explores the Bayesian approach to statistical analysis, Bayesian computing, and both sides of the frequentist versus Bayesian debate. Topics include Bayes' Theorem, prior and posterior probability distributions, Bayesian regression, Bayesian hierarchical models, and an introduction to Markov chain Monte Carlo computing techniques. Prerequisite(s): STAT 155\u00a0and MATH 354.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 454-02 32487<\/span><\/td>\n Bayesian Statistics<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Taylor Okonek\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Bayesian statistics, an alternative to the traditional frequentist approach taken in our other statistics courses, is playing an increasingly integral role in modern statistics. The Bayesian philosophy is natural, allowing us to formally balance data with our prior knowledge, and updating this knowledge as more data come in. It answers natural questions. It can shine in settings where frequentist "likelihood" methods break down. And it is becoming increasingly popular with the availability of computing tools necessary to its implementation. This course explores the Bayesian approach to statistical analysis, Bayesian computing, and both sides of the frequentist versus Bayesian debate. Topics include Bayes' Theorem, prior and posterior probability distributions, Bayesian regression, Bayesian hierarchical models, and an introduction to Markov chain Monte Carlo computing techniques. Prerequisite(s): STAT 155\u00a0and MATH 354.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n <\/tbody>\n <\/table>\n<\/div>\n\n \n\n \n\t

Fall 2026<\/h2>\n

ÈÕº«¾«Æ· the Registrar's Class Schedule for live registration information<\/a><\/p>\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Num. \/ Sec. \/ CRN<\/th>\n Name<\/th>\n Days<\/th>\n Time<\/th>\n Room<\/th>\n Instructor<\/th>\n <\/th>\n \n <\/tr>\n <\/thead>\n
STAT 112-01 10184<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-01 (10183); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-02 10186<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-02 (10185); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-03 10188<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Getiria Onsongo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-03 (10187); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-04 10190<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>03:30 pm-04:30 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Getiria Onsongo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-04 (10189); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-01 10706<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-02 10707<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lori Ziegelmeier\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-03 10708<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>STAFF\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-04 10709<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>02:20 pm-03:20 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>STAFF\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-05 10710<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lori Ziegelmeier\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-06 10711<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lori Ziegelmeier\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 212-01 10201<\/span><\/td>\n Intermediate Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 212-01 (10200)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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\u00a0and\u00a0COMP 123\u00a0and\u00a0STAT 155;\u00a0STAT 253\u00a0recommended but not required.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 253-01 10712<\/span><\/td>\n Statistical Machine Learning<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Alicia Johnson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n The linear and logistic modeling techniques from\u00a0STAT 155\u00a0are augmented with the three foundational machine learning tasks: regression, classification, and clustering. \u00a0The 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. \u00a0As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 253-02 10713<\/span><\/td>\n Statistical Machine Learning<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Alicia Johnson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n The linear and logistic modeling techniques from\u00a0STAT 155\u00a0are augmented with the three foundational machine learning tasks: regression, classification, and clustering. \u00a0The 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. \u00a0As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. Prerequisite(s): STAT 155.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 354-01 10714<\/span><\/td>\n Probability<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Alexander Hanhart\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required; cross-listed with MATH 354-01 (10715)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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\u00a0or MATH 237\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 354-02 10716<\/span><\/td>\n Probability<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Alexander Hanhart\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required; cross-listed with MATH 354-02 (10717)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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\u00a0or MATH 237\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 451-01 10718<\/span><\/td>\n Causal Inference<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n "Correlation does not imply causation." We've all heard this mantra, warding us away from reading too much into the association between murder rates and ice cream sales, between shoe size and reading ability, and the like. But this mantra leaves us wanting: how do we study causation? Questions of causation are essential when we try to understand the effects of new medical treatments, economic policies, or government programs. In this course, we'll examine frameworks of thinking, statistical tools, and study designs that enable us to learn about the causal effects of interventions. Some specific topics include causal graphs, experimental studies, and quasi-experimental study designs. This course is a capstone course with a significant project component. Prerequisite(s): STAT 155\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 452-01 10719<\/span><\/td>\n Correlated Data<\/td>\n \n Days: <\/span>M W \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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\u00a0and MATH 354\/STAT 354\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 456-01 10210<\/span><\/td>\n Projects in Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Bret Jackson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required; cross-listed with COMP 456-01 (10209)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n 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.\u00a0Counts as a capstone course for the Computer Science major and the Data Science major. Prerequisite(s): STAT 212\u00a0\u00a0and\u00a0STAT 253\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n <\/tbody>\n <\/table>\n<\/div>\n\n \n\n \n \n \n\n \n\n \n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":238,"featured_media":0,"parent":258,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-668","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/pages\/668","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/users\/238"}],"replies":[{"embeddable":true,"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/comments?post=668"}],"version-history":[{"count":2,"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/pages\/668\/revisions"}],"predecessor-version":[{"id":1587,"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/pages\/668\/revisions\/1587"}],"up":[{"embeddable":true,"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/pages\/258"}],"wp:attachment":[{"href":"https:\/\/www.macalester.edu\/mscs\/wp-json\/wp\/v2\/media?parent=668"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}