Statistics Classes

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

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

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

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

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

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

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

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

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

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

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. May be repeated for credit. Offered occasionally. (2 credits) Prerequisite(s): STAT 155 and STAT/COMP 112

General Education Requirements:

Distribution Requirements:

Course Materials

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

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

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

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

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

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

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 and MATH 354.

General Education Requirements:

Distribution Requirements:

Course Materials

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 and MATH 354.

General Education Requirements:

Distribution Requirements:

Course Materials

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

"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

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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

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