Course Offerings
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Fall 2024▲
See complete information about these courses in the course offerings database. For more information about a specific course, including course type, schedule and location, click on its title.
Data Science: Visualizing and Exploring Big Data
BIOL 185 - Whitworth, Gregg B.
We live in the era of big data. Major discoveries in science and medicine are being made by exploring large datasets in novel ways using computational tools. The challenge in the biomedical sciences is the same as in Silicon Valley: knowing what computational tools are right for a project and where to get started when exploring large data sets. In this course, students learn to use R, a popular open-source programming language and data analysis environment, to interactively explore data. Case studies are drawn from across the sciences and medicine. Topics include data visualization, machine learning, image analysis, geospatial analysis, and statistical inference on large data sets. We also emphasize best practices in coding, data handling, and adherence to the principles of reproducible research. No prior programming experience required. Fulfills the computer science requirement for biology and neuroscience majors.
Topics in Biology: Intro to Data Science in Python
BIOL 195C - Toporikova, Natalia
In this era of data science, major discoveries in science and medicine are being made by exploring datasets in novel ways using computational tools. The challenge in the biomedical sciences is the same as in Silicon Valley: knowing what computational tools are right for a project and where to get started when exploring large data sets. In this course, students learn to use Python, a popular open-source programming language and Jupyter Notebook data-analysis environment, to explore data interactively. Case studies are drawn from across the sciences and medicine. Topics include data visualization, physiological modeling, image analysis, and statistical inference on large data sets. We also emphasize best practices in coding, data handling, and adherence to the principles of reproducible research. No prior programming experience required.
Fundamentals of Business Analytics
BUS 202 - Frimpong, Bright
Business analytics allows for the conversion of raw data into actionable real-world insights. We'll build a foundation of knowledge in the fundamentals of statistics and data science using business data to formulate key metrics. We'll use a programming language to summarize and visualize data, interpret patterns, infer population parameters, explore relationships among variables, and make forecasts. No prior programming experience is expected.BUS 202 will count towards the statistics requirement of both the business administration and accounting majors (currently also satisfied by INTR 202, ECON 202, MATH 118, etc.). It will also count towards the statistics requirement of the Data Science minor. As is the case with INTR 202, etc., BUS 202 serves as a pre- or co-requisite for FIN 221. Due to contact overlap, students may take only one of the following courses for degree credit: BUS 202, INTR 202, ECON 202, MATH 118. Students who have already taken CBSC 250 should not take any of these other?courses.
Fundamentals of Business Analytics
BUS 202 - Hu, Lingshu
Business analytics allows for the conversion of raw data into actionable real-world insights. We'll build a foundation of knowledge in the fundamentals of statistics and data science using business data to formulate key metrics. We'll use a programming language to summarize and visualize data, interpret patterns, infer population parameters, explore relationships among variables, and make forecasts. No prior programming experience is expected.BUS 202 will count towards the statistics requirement of both the business administration and accounting majors (currently also satisfied by INTR 202, ECON 202, MATH 118, etc.). It will also count towards the statistics requirement of the Data Science minor. As is the case with INTR 202, etc., BUS 202 serves as a pre- or co-requisite for FIN 221. Due to contact overlap, students may take only one of the following courses for degree credit: BUS 202, INTR 202, ECON 202, MATH 118. Students who have already taken CBSC 250 should not take any of these other?courses.
Database Management for Business
BUS 315 - Larson, Keri M.
An introduction to the theories, concepts, features, and capabilities of database management systems in a business environment. This course provides a greater understanding of how to design, develop and access database-driven business applications and emphasizes the use of database-management systems in real-world business settings and how this technology can be applied effectively to solve business problems. In this project-oriented course, students acquire the skills to document, design, create, test, and access a fully functional Oracle business database application. No prior programming or application development experience is assumed. Preference to BSADM majors during first round of registration.
Introduction to Data Science: Trends Over Time
CBSC 185 - Shablack, Holly
How can we map our feelings, attitudes, and thoughts over the course of a year? can we effectively monitor our behavior and choices to identify how they impact our mental and physical health? Can we assess employee job satisfaction or student learning over periods of time? In this course students will have an introduction to common ways of examining psychological data over time. Students will learn to use R, a popular open-source programming language, to organize and manage data effectively, create eye-catching and informative data visualizations, and conduct a variety of statistical analyses to identify key trends in data. Students will learn best practices in coding, data handling and management, reproducibility, and data ethics. No prior programming experience is required.
Data Science: Mind Analytics
CBSC 309 - Johnson, Dan R.
Psychological tests promise to match you with your soul mate, reveal the hidden depths of your personality and attitudes, and predict your success in college. How would you determine if these promises are being kept? Students build data-science skills while teaming on how to assess a test's reliability and validity, including tests of abilities, personality, attitudes, and more. No programming experience is required while we use R, a popular open-source programming language, to learn data management, data visualization, model-comparison metrics, and statistical inference in a reproducible and ethically responsible manner.
Imaging Science in Art, Medicine, and Astronomy with Laboratory
CHEM 116 - Uffelman, Erich S.
Modern art analysis, medical imaging, and astrophysics/astrochemistry emphasize remote sensing -- i.e., the ability to obtain information (often chemical information) about something without making physical contact with it. This course will establish the rudiments of various chemical imaging modalities and conclude with projects involving the acquisition and processing of remote sensing datasets from art and astronomy. Some major medical imaging methodologies will be introduced. This non-calculus laboratory course assumes nothing other than basic high school math. No prior science courses are required. However, students will be expected to use, with assistance, the mathematical, statistical, and computational methods we introduce in the course.
Introduction to Computer Science
CSCI 111 - Watson, Cody A.
A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.
Introduction to Computer Science
CSCI 111 - Khan, Mohammad Taha (Taha)
A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.
Introduction to Computer Science
CSCI 111 - Lu, Kefu
A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.
Data Structures
CSCI 112 - Tolley, William J.
A continuation of CSCI 111. Emphasis is on the use and implementation of data structures, introductory algorithm analysis, and object-oriented design and programming with Python. Laboratory course.
Web Programming for Non-Programmers
DCI 110 - Barry, Jeffrey S.
Computer science and IT graduates are no longer the only people expected to have some knowledge of how to program. Humanities and social science majors can greatly increase their job prospects by understanding the fundamentals of writing computer code, not only through the ability itself but also being better able to communicate with programming professionals and comprehending the software development and design process as a whole. The most centralized and simple platform for learning is the Web. This course starts with a brief introduction to/review of HTML and CSS and then focuses on using JavaScript to write basic code and implement preexisting libraries to analyze and visualize data. Students become familiar with building a complete Web page that showcases all three languages. No prior programming experience is needed, but a desire to learn and to be challenged is a must.
Statistics for Economics
ECON 202 - Blunch, Niels-Hugo (Hugo)
Fundamentals of probability, statistics, estimation, and hypothesis testing and ending with an introduction to regression analysis. The topics are critical for success in upper-level economics electives and are important for careers that rely on empirical research in the social sciences. Students engage in a dialogue between theory and application and learn to think formally about data, uncertainty, and random processes, while learning hands-on methods to organize and analyze real data using modern statistical software. Not open to students with credit for BUS 202 or INTR 202.
Econometrics
ECON 203 - Anderson, Michael A.
Explorations of regression models that relate a response variable to one or more predictor variables. The course begins with a review of the simple bivariate model used in INTR 202, and moves on to multivariate models. Underlying model assumptions and consequences are discussed. Advanced topics include non-linear regression and forecasting. Examples in each class are drawn from a number of disciplines. The course emphasizes the use of data and student-directed research.
Introduction to Statistics
MATH 118 - Broda, James
Elementary probability and counting. Mean and variance of discrete and continuous random variables. Central Limit Theorem. Confidence intervals and hypothesis tests concerning parameters of one or two normal populations.
Linear Algebra
MATH 222 - McRae, Alan
Linear algebra is the backbone of much of mathematics. Students in this course learn to identify and explain the basic principles, terminology, and theories used in linear algebra, and apply quantitative and/or qualitative reasoning skills to solve problems posed in linear algebra, primarily through applications of to both mathematics and the sciences, and also by writing proofs In mathematics.
Probability
MATH 309 - Prince Nelson, Sybil
Probability, probability density and distribution functions, mathematical expectation, discrete and continuous random variables, and moment generating functions.
Applied Statistics
POL 202 - Ponce de Leon Seijas, Zoila
Not open to students with credit for BUS 202, ECON 202, or INTR 202. An examination of the principal applications of statistics to allow students to develop a working knowledge and understanding of applied statistics in the social sciences (politics, sociology, and economics), and accounting and business. Topics include descriptive statistics, probability, estimation, hypothesis testing, and regression analysis
Personal Networks and Social Capital
SOAN 244 - Eastwood, Jonathan R. (Jon)
This course will be a hybrid seminar/research lab that covers some of the most important findings and methods in the study of personal networks, with an emphasis on the application of network methods to the study of social capital. In the lab portion of the class, we will learn how to do personal network analysis in R, covering topics like (a) sampling and gathering personal network data; (b) descriptive statistics that allow us to measure and study structural features of people's local social environments; and (c) models for linking those measures of local structure to individual-level predictors and outcomes of interest.
Spring 2024▲
We do not offer any courses this term.
Winter 2024▲
See complete information about these courses in the course offerings database. For more information about a specific course, including course type, schedule and location, click on its title.
Introduction to Data Science in Python
BIOL 187 - Toporikova, Natalia
In this era of data science, major discoveries in science and medicine are being made by exploring datasets in novel ways using computational tools. The challenge in the biomedical sciences is the same as in Silicon Valley: knowing what computational tools are right for a project and where to get started when exploring large data sets. In this course, students learn to use Python, a popular open-source programming language and Jupyter Notebook data-analysis environment, to explore data interactively. Case studies are drawn from across the sciences and medicine. Topics include data visualization, physiological modeling, image analysis, and statistical inference on large data sets. We also emphasize best practices in coding, data handling, and adherence to the principles of reproducible research. No prior programming experience required.
Statistics for Biology and Medicine
BIOL 201 - Toporikova, Natalia
This course examines the principles of statistics and experimental design for biological and medical research. The focus is on the practical and conceptual aspects of statistics, rather than mathematical derivations. Students completing this class will be able to read and understand research papers, to design realistic experiments, and to carry out their own statistical analyses using computer packages.
Fundamentals of Business Analytics
BUS 202 - Davis, Justin
Business analytics allows for the conversion of raw data into actionable real-world insights. We'll build a foundation of knowledge in the fundamentals of statistics and data science using business data to formulate key metrics. We'll use a programming language to summarize and visualize data, interpret patterns, infer population parameters, explore relationships among variables, and make forecasts. No prior programming experience is expected.BUS 202 will count towards the statistics requirement of both the business administration and accounting majors (currently also satisfied by INTR 202, ECON 202, MATH 118, etc.). It will also count towards the statistics requirement of the Data Science minor. As is the case with INTR 202, etc., BUS 202 serves as a pre- or co-requisite for FIN 221. Due to contact overlap, students may take only one of the following courses for degree credit: BUS 202, INTR 202, ECON 202, MATH 118. Students who have already taken CBSC 250 should not take any of these other?courses.
Fundamentals of Business Analytics
BUS 202 - Hu, Lingshu
Business analytics allows for the conversion of raw data into actionable real-world insights. We'll build a foundation of knowledge in the fundamentals of statistics and data science using business data to formulate key metrics. We'll use a programming language to summarize and visualize data, interpret patterns, infer population parameters, explore relationships among variables, and make forecasts. No prior programming experience is expected.BUS 202 will count towards the statistics requirement of both the business administration and accounting majors (currently also satisfied by INTR 202, ECON 202, MATH 118, etc.). It will also count towards the statistics requirement of the Data Science minor. As is the case with INTR 202, etc., BUS 202 serves as a pre- or co-requisite for FIN 221. Due to contact overlap, students may take only one of the following courses for degree credit: BUS 202, INTR 202, ECON 202, MATH 118. Students who have already taken CBSC 250 should not take any of these other?courses.
Fundamentals of Business Analytics
BUS 202 - Frimpong, Bright
Business analytics allows for the conversion of raw data into actionable real-world insights. We'll build a foundation of knowledge in the fundamentals of statistics and data science using business data to formulate key metrics. We'll use a programming language to summarize and visualize data, interpret patterns, infer population parameters, explore relationships among variables, and make forecasts. No prior programming experience is expected.BUS 202 will count towards the statistics requirement of both the business administration and accounting majors (currently also satisfied by INTR 202, ECON 202, MATH 118, etc.). It will also count towards the statistics requirement of the Data Science minor. As is the case with INTR 202, etc., BUS 202 serves as a pre- or co-requisite for FIN 221. Due to contact overlap, students may take only one of the following courses for degree credit: BUS 202, INTR 202, ECON 202, MATH 118. Students who have already taken CBSC 250 should not take any of these other?courses.
Introduction to Data Science for Business
BUS 314 - Larson, Keri M.
This course covers organizational concerns related to data science such as artificial intelligence, machine learning, predictive algorithms, Big Data, cloud computing, security and privacy, and the digitization of products and processes. Through readings, students develop a strong conceptual understanding of concepts prior to developing technical proficiency in some of them. Students learn SQL and the Exploratory UI (user interface) for R to quickly access capabilities including data wrangling and machine learning without programming. Assignments focus on how organizations can improve decision making and create new business opportunities using Data Science. Not open to students with credit for BUS 316. Students looking for a more advanced business course in data analytics should register for BUS 316. No prerequisite or prior coursework assumed in statistics or programming.
Data Mining for Business Analytics
BUS 317 - Ballenger, Robert M. (Bob)
Data mining is the science of discovering structure and making predictions in large, complex data sets. In the era of e-commerce and information economy, enormous amounts of data are generated daily from business transactions, networked sensors, social networking activities, website traffic, GPS systems, etc. Data-driven decision-making has become essential across a wide variety of functional areas in businesses such as targeted advertising, market segmentation, personalized recommendation, supplier/customer relationship management, product design, credit scoring, fraud detection and workforce management. This course serves as an introduction to Data Mining for students interested in Business Analytics. Students will learn about many commonly-used methods for predictive and descriptive analytics tasks. They will also learn to assess the methods' predictive and practical utility. A prerequisite for this course is the successful completion of an R tidyverse centric data analytics course. Preference to BSADM majors or DS, DSBA, ENTR minors during initial registration.
Introduction to Data Science: Trends Over Time
CBSC 185 - Shablack, Holly
How can we map our feelings, attitudes, and thoughts over the course of a year? can we effectively monitor our behavior and choices to identify how they impact our mental and physical health? Can we assess employee job satisfaction or student learning over periods of time? In this course students will have an introduction to common ways of examining psychological data over time. Students will learn to use R, a popular open-source programming language, to organize and manage data effectively, create eye-catching and informative data visualizations, and conduct a variety of statistical analyses to identify key trends in data. Students will learn best practices in coding, data handling and management, reproducibility, and data ethics. No prior programming experience is required.
Statistics and Research Design
CBSC 250 - Shablack, Holly / Brindle, Ryan C.
Students learn about the design and analysis of psychological research, with particular emphasis on experimentation. Students learn statistical inference appropriate for hypothesis testing, and they use standard statistical packages to analyze data. Laboratory course.
Statistics and Research Design
CBSC 250 - Johnson, Dan R.
Students learn about the design and analysis of psychological research, with particular emphasis on experimentation. Students learn statistical inference appropriate for hypothesis testing, and they use standard statistical packages to analyze data. Laboratory course.
Statistics and Research Design
CBSC 250 - Whiting, Wythe L.
Students learn about the design and analysis of psychological research, with particular emphasis on experimentation. Students learn statistical inference appropriate for hypothesis testing, and they use standard statistical packages to analyze data. Laboratory course.
Fundamentals of Programming I
CSCI 111 - Sprenkle, Sara E.
A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.
Fundamentals of Programming I
CSCI 111 - Matthews, Elizabeth A. (Liz)
A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.
Fundamentals of Programming II
CSCI 112 - Tolley, William J.
A continuation of CSCI 111. Emphasis is on the use and implementation of data structures, introductory algorithm analysis, and object-oriented design and programming with Python. Laboratory course.
Fundamentals of Programming II
CSCI 112 - Levy, Simon D.
A continuation of CSCI 111. Emphasis is on the use and implementation of data structures, introductory algorithm analysis, and object-oriented design and programming with Python. Laboratory course.
Special Topics in Data Science: Statistics and Medicine
DS 395B - Prince Nelson, Sybil
This course explores into the world of probability, probability density, and distribution functions, and their pivotal role in the development and assessment of vaccines, with a particular focus on the ongoing global COVID-19 pandemic. The course equips students with essential statistical and data science tools to analyze the effectiveness, ethics, and quality of vaccines, as well as their equitable distribution.
Directed Individual Study
DS 401 - Barry, Jeffrey S. / Eastwood, Jonathan R. (Jon)
Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio. To be taken during the fall or winter term of the senior year.
Directed Individual Study
DS 401 - Johnson, Dan R.
Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio. To be taken during the fall or winter term of the senior year.
Directed Individual Study
DS 401 - Ballenger, Robert M. (Bob)
Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio. To be taken during the fall or winter term of the senior year.
Directed Individual Study
DS 401 - Hu, Lingshu
Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio. To be taken during the fall or winter term of the senior year.
Directed Individual Study
DS 401 - Shablack, Holly
Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio. To be taken during the fall or winter term of the senior year.
Directed Individual Study
DS 401 - Prince Nelson, Sybil
Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio. To be taken during the fall or winter term of the senior year.
Directed Individual Study
DS 401 - Watson, Cody A.
Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio. To be taken during the fall or winter term of the senior year.
Statistics for Economics
ECON 202 - Anderson, Michael A.
Fundamentals of probability, statistics, estimation, and hypothesis testing and ending with an introduction to regression analysis. The topics are critical for success in upper-level economics electives and are important for careers that rely on empirical research in the social sciences. Students engage in a dialogue between theory and application and learn to think formally about data, uncertainty, and random processes, while learning hands-on methods to organize and analyze real data using modern statistical software. Not open to students with credit for BUS 202 or INTR 202.
Econometrics
ECON 203 - Blunch, Niels-Hugo (Hugo)
Explorations of regression models that relate a response variable to one or more predictor variables. The course begins with a review of the simple bivariate model used in INTR 202, and moves on to multivariate models. Underlying model assumptions and consequences are discussed. Advanced topics include non-linear regression and forecasting. Examples in each class are drawn from a number of disciplines. The course emphasizes the use of data and student-directed research.
GIS and Remote Sensing
EEG 260 - Harbor, David J.
A laboratory course introducing the use of a Geographic Information System (GIS) and remote sensing in geological/environmental analyses and decision making. Students use state-of-the-art software with a wide variety of spatial geologic, environmental, economic and topographic data derived from satellites; remote databases and published maps to evaluate geologic conditions; local landscape processes; environmental conditions; and hypothetical land-use cases.
Applied Statistics
INTR 202 - Ponce de Leon Seijas, Zoila
An examination of the principal applications of statistics in accounting, business, economics, and politics. Topics include descriptive statistics, probability, estimation, hypothesis testing, and regression analysis.
Linear Algebra
MATH 222 - Bush, Michael R.
Linear algebra is the backbone of much of mathematics. Students in this course learn to identify and explain the basic principles, terminology, and theories used in linear algebra, and apply quantitative and/or qualitative reasoning skills to solve problems posed in linear algebra, primarily through applications of to both mathematics and the sciences, and also by writing proofs In mathematics.
Probability
MATH 309 - Prince Nelson, Sybil
Probability, probability density and distribution functions, mathematical expectation, discrete and continuous random variables, and moment generating functions.
Mathematical Statistics
MATH 310 - Broda, James
Sampling distributions, point and interval estimation, testing hypotheses, regression and correlation, and analysis of variance.
Art & Science of Survey Research
SOAN 276 - Jasiewicz, Krzysztof
This course is designed as a group research project in questionnaire construction and survey data analysis. Students prepare a list of hypotheses, select indicators, construct a questionnaire, collect and analyze data, and write research reports. When appropriate, the course may include service-learning components (community-based research projects).
Senior Seminar in Quantitative Analysis
SOAN 395 - Eastwood, Jonathan R. (Jon)
In this course students will carry out independent research on anthropological or sociological topics that they identify and develop in consultation with their professor and while working alongside their peers. Projects completed under the auspices of this course will use (or mostly use) quantitative methods, generally the statistical analysis of experimental or observational data. Students will develop a question, select appropriate methods, ground their approach in an appropriate theoretical perspective from their discipline of concentration (anthropology or sociology), carry out, write up, and present their research.