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

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

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

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

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

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

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

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

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

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

"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, policies, or 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 graphical causal models, randomized experiments, propensity score methods, instrumental variables, and interrupted time series designs. This course should be useful to those interested in biology, economics, medicine, public policy and any area in which interventions are routinely evaluated. Prerequisite(s): STAT 155 and 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

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

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

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

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

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

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

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

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