Data Science Minor Requirements
2024 - 2025 Catalog
We have the following degrees:
Data Science - Business Analytics minor
A minor in data science-business analytics requires completion of 22 credits, as follows. In meeting the requirements of this interdisciplinary minor, a student may not use more than nine credits that are also used to meet the requirements of other majors or minors. Business Administration majors may not minor in Data Science- Business Analytics but may minor in Data Science.
- Business Foundations: ACCT 100
- Data Science Foundations: At least three credits chosen from among the following courses: BUS 310, 314
- Statistics: At least three credits chosen from among the following courses: BIOL 201; BUS 202; CBSC 250; DS 395, 399, 421, 422, 423; DCI 202; ECON 202; POL 202; MATH 118, 310; SOAN 218, SOAN 222
- Computing/Programming: At least three credits chosen from among the following: BUS 315, 316; CSCI 111
- Electives: At least nine additional credits chosen from among the following: BUS 314, 315, 316, 359; CSCI 111, 112, 315; DS 395, 399, 421, 422, 423; ECON 203; MATH 222, 309, SOAN 222
- Portfolio: DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor, and also including at least two reflections on data science competencies.
No more than three credits of 400-level work can be counted towards the minor. Additional prerequisites may be required depending on course choices above.Additional prerequisites may be required depending on course choices above.
- Business Foundations:
- ACCT 100 - Introduction to Accounting
Credits 3 An introduction to accounting for both internal and external purposes. Students cover the fundamental principles of financial accounting (external) and an introduction to how companies process financial information in order to disclose it to the public. The course also investigates how managers prepare information for internal purposes (managerial accounting). Financial accounting is guided by external requirements, while managerial accounting generally is not.
- Data Science Foundations:
- BUS 314 - Introduction to Data Science for Business
Credits 3 Prerequisite ECON 100, 180, or 180A, and ACCT 100, and at least sophomore class standing 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.
- Statistics:
- BIOL 201 - Statistics for Biology and Medicine
Credits 3 Prerequisite BIOL 111, 113, and either a Biology major, Neuroscience major, or Data Science minor 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.
- BUS 202 - Fundamentals of Business Analytics
Credits 3 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 POL/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 POL/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, POL/INTR 202, ECON 202, MATH 118. Students who have already taken CBSC 250 should not take any of these other courses.
- CBSC 250 - Statistics and Research Design
Credits 4 Prerequisite any CBSC course and at least sophomore class standing 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.
- ECON 202 - Data Analytics for Economics
Credits 3 Prerequisite ECON 100, 180, 180A, or both ECON 101 and ECON 102 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 POL/INTR 202.
- POL 202 - Applied Statistics
Credits 3 Not open to students with credit for BUS 202, ECON 202, INTR 202, CBSC 250, or MATH 118. 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.
- MATH 118 - Introduction to Statistics
FDR FM Math and Computer Science Foundation Credits 3 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.
- MATH 310 - Mathematical Statistics
Credits 3 Prerequisite MATH 309 Sampling distributions, point and interval estimation, testing hypotheses, regression and correlation, and analysis of variance.
- SOAN 218 - Basic Statistics in the Social Sciences
Credits 3 Introductory statistics course designed to help students become good consumers of statistics, but especially geared for students interested in sociology, archeology, and anthropology. Topics include descriptive and inferential statistics, sampling, and regression analysis. Students also get practical experience with cleaning and analyzing real world secondary data.
- SOAN 222 - Data Science Tools for Social Policy
FDR SC Science, Math, CS Distribution Credits 3 Prerequisite One of the following: BIOL 201, CBSC 250, DCI 202, ECON 202, POL/INTR 202, MATH 310, SOAN 218, or SOAN 219; or instructor consent Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice. All work will be done in R. No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.
- Computing/Programming
- BUS 315 - Database Management for Business
Credits 3 Prerequisite At least junior class standing and BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 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.
- BUS 316 - Business Analytics
Credits 3 Prerequisite BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required. Preference to BSADM majors and DS / DSBA minors during the first round of registration.
- CSCI 111 - Introduction to Computer Science
FDR FM Math and Computer Science Foundation Credits 4 This course introduces students to fundamental ideas in computer science while building skills in software development. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. CSCI 111 is appropriate for all students who want to be able to write programs, regardless of the domain. It is the typical first course for computer science majors and minors. No previous programming experience required. Lectures and formal laboratories.
- Electives:
- BUS 314 - Introduction to Data Science for Business
Credits 3 Prerequisite ECON 100, 180, or 180A, and ACCT 100, and at least sophomore class standing 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.
- BUS 315 - Database Management for Business
Credits 3 Prerequisite At least junior class standing and BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 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.
- BUS 316 - Business Analytics
Credits 3 Prerequisite BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required. Preference to BSADM majors and DS / DSBA minors during the first round of registration.
- CSCI 111 - Introduction to Computer Science
FDR FM Math and Computer Science Foundation Credits 4 This course introduces students to fundamental ideas in computer science while building skills in software development. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. CSCI 111 is appropriate for all students who want to be able to write programs, regardless of the domain. It is the typical first course for computer science majors and minors. No previous programming experience required. Lectures and formal laboratories.
- CSCI 112 - Data Structures
FDR SC Science, Math, CS Distribution Credits 4 Prerequisite CSCI 111 This course continues the introduction to computer science begun in CSCI 111. Emphasis is on the use and implementation of data structures (i.e., how to store information and access it efficiently), introductory algorithm analysis, and object-oriented design and programming with Python. Lectures and formal laboratories.
- CSCI 315 - Artificial Intelligence
Credits 3 Prerequisite CSCI 209 Basic concepts of heuristic search, game playing, natural language processing, and intelligent systems, with a focus on writing programs in these areas. Course combines a discussion of philosophical issues with hands-on problem solving.
- DS 395 - Special Topics in Data Science
Credits 3 Prerequisite instructor consent Exploration of a topic in data science, where students craft testable research questions and retrieve an appropriate data set to answer those research questions. Includes training in some aspect of data science and incorporates extensive independent student work, with individual projects being the key product of that work, synthesizing what you have learned in the data science minor and demonstrating mastery of core data-science skills. Topics may include causal inference for data science with directed acyclic graphs (DAGs); analysis of spatial data; or multilevel models. Data Science faculty. Offered periodically depending on faculty availability and expertise and student interest.
- DS 399 - Data Science Capstone
Credits 3 Prerequisite instructor consent In this course, students have the opportunity to identify a topic of interest, craft testable research questions, and retrieve an appropriate data set to answer those research questions. Students' primary goal is to synthesize what you have learned in the data science minor and demonstrate mastery of core data-science skills.
- DS 421 - Directed Individual Research
Credits 1 Prerequisite instructor consent Each student conducts primary research in partnership with a faculty member, by prior mutual agreement. Consult the program web page to find faculty pages with descriptions of current research areas. May be repeated for degree credit. No more than three credits of work at the 400 level may apply toward the minor. May be carried out during summer.
- DS 422 - Directed Individual Research
Credits 2 Prerequisite instructor consent Each student conducts primary research in partnership with a faculty member, by prior mutual agreement. Consult the program web page to find faculty pages with descriptions of current research areas. May be repeated for degree credit. No more than three credits of work at the 400 level may apply toward the minor. May be carried out during summer.
- DS 423 - Directed Individual Research
Credits 3 Prerequisite instructor consent Each student conducts primary research in partnership with a faculty member, by prior mutual agreement. Consult the program web page to find faculty pages with descriptions of current research areas. May be repeated for degree credit. No more than three credits of work at the 400 level may apply toward the minor. May be carried out during summer.
- ECON 203 - Econometrics
Credits 3 Prerequisite ECON 202 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 POL/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.
- MATH 222 - Linear Algebra
FDR SC Science, Math, CS Distribution Credits 3 Prerequisite MATH 102 with a grade of C or greater, MATH 201, MATH 221, or MATH 221 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.
- MATH 309 - Probability
Credits 3 Prerequisite MATH 221 with a grade of C or greater Probability, probability density and distribution functions, mathematical expectation, discrete and continuous random variables, and moment generating functions.
- SOAN 222 - Data Science Tools for Social Policy
FDR SC Science, Math, CS Distribution Credits 3 Prerequisite One of the following: BIOL 201, CBSC 250, DCI 202, ECON 202, POL/INTR 202, MATH 310, SOAN 218, or SOAN 219; or instructor consent Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice. All work will be done in R. No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.
- Portfolio:
- DS 401 - Directed Individual Study
Credits 1 Prerequisite instructor consent 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.
At least three credits chosen from among the following courses:
At least three credits chosen from among the following courses:
At least three credits chosen from among the following:
At least nine additional credits chosen from among the following:
DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor, and also including at least two reflections on data science competencies.
Data Science minor
A minor in data science requires completion of at least 19 credits, as follows. In meeting the requirements of this interdisciplinary minor, a student may not use more than nine credits that are also used to meet the requirements of other majors or minors.
- Data Science Foundations: At least three credits chosen from among the following: BIOL 185, 187; BUS 310, 314; CBSC 185, 240; MATH 100A; SOAN 244
- Statistics: At least three credits chosen from among the following: BIOL 201; BUS 202; CBSC 250; DCI 202; DS 285; ECON 202; POL 202; MATH 118, 310; SOAN 218, 219, 222
- Computing/Programming: At least three credits chosen from among the following: BUS 306, 315, 316; CSCI 111; DCI 110
- Electives: At least nine additional credits chosen from among the following: BIOL 282, 302, 325, 385; BUS 306, 315, 316, 317; CBSC 359; CHEM 116; CSCI 111, 112, 315; DCI 110; DS 285, 395, 399, 421, 422, 423; ECON 203; EEG 260; MATH 222, 309, 310; PHYS 265; SOAN 219, 220, 222, 264, 265, 266, 268, 276, 365, 395; and, when appropriate BIOL 297
- Portfolio: DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor in which students reflect on data-science competencies
No more than three credits of 400-level work can be counted towards the minor. Additional prerequisites may be required depending on course choices above.
- Data Science Foundations
- BIOL 185 - Data Science: Visualizing and Exploring Big Data
Credits 3 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.
- BIOL 187 - Introduction to Data Science in Python
Credits 4 Prerequisite BIOL 111, 113, and either a Biology major, Neuroscience major, or Data Science minor 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.
- BUS 314 - Introduction to Data Science for Business
Credits 3 Prerequisite ECON 100, 180, or 180A, and ACCT 100, and at least sophomore class standing 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.
- CBSC 185 - Introduction to Data Science: Trends Over Time
Credits 3 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.
- SOAN 244 - Personal Networks and Social Capital
FDR SS4 Credits 3 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.
- Statistics
- BIOL 201 - Statistics for Biology and Medicine
Credits 3 Prerequisite BIOL 111, 113, and either a Biology major, Neuroscience major, or Data Science minor 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.
- BUS 202 - Fundamentals of Business Analytics
Credits 3 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 POL/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 POL/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, POL/INTR 202, ECON 202, MATH 118. Students who have already taken CBSC 250 should not take any of these other courses.
- CBSC 250 - Statistics and Research Design
Credits 4 Prerequisite any CBSC course and at least sophomore class standing 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.
- DS 285 - Statistical Methods for Correcting Bias
Credits 3 This course will investigate the differences and similarities between statistical bias and social bias. Students will study techniques to ameliorate or eliminate both.
- ECON 202 - Data Analytics for Economics
Credits 3 Prerequisite ECON 100, 180, 180A, or both ECON 101 and ECON 102 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 POL/INTR 202.
- POL 202 - Applied Statistics
Credits 3 Not open to students with credit for BUS 202, ECON 202, INTR 202, CBSC 250, or MATH 118. 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.
- MATH 118 - Introduction to Statistics
FDR FM Math and Computer Science Foundation Credits 3 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.
- MATH 310 - Mathematical Statistics
Credits 3 Prerequisite MATH 309 Sampling distributions, point and interval estimation, testing hypotheses, regression and correlation, and analysis of variance.
- SOAN 218 - Basic Statistics in the Social Sciences
Credits 3 Introductory statistics course designed to help students become good consumers of statistics, but especially geared for students interested in sociology, archeology, and anthropology. Topics include descriptive and inferential statistics, sampling, and regression analysis. Students also get practical experience with cleaning and analyzing real world secondary data.
- SOAN 219 - Applied Bayesian Regression for the Social Sciences
FDR SC Science, Math, CS Distribution Credits 3 This course is an introduction to applied Bayesian regression, emphasizing applications for social scientists. We begin by introducing some philosophical and mathematical bases of Bayesian inference. We then move on to a sustained focus on applied regression, starting with bivariate regression and moving on to regression with multiple predictors, up to and including models with interactions. Along the way, students will be exposed to the use of directed acyclic graphs (DAGs) in thinking about causality with observational data. Throughout the course students will carry out numerous analyses of data, learning by doing. Examples are drawn from anthropology, sociology, political science, and related fields.
- SOAN 222 - Data Science Tools for Social Policy
FDR SC Science, Math, CS Distribution Credits 3 Prerequisite One of the following: BIOL 201, CBSC 250, DCI 202, ECON 202, POL/INTR 202, MATH 310, SOAN 218, or SOAN 219; or instructor consent Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice. All work will be done in R. No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.
- Computing/Programming
- BUS 306 - Seminar in Management Information Systems
Credits 3-4 Topics vary by term and instructor. May be repeated for degree credit if the topics are different. Prerequisite vary with topics. Preference to BSADM or JMCB majors during the first round of registration.
- BUS 315 - Database Management for Business
Credits 3 Prerequisite At least junior class standing and BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 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.
- BUS 316 - Business Analytics
Credits 3 Prerequisite BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required. Preference to BSADM majors and DS / DSBA minors during the first round of registration.
- CSCI 111 - Introduction to Computer Science
FDR FM Math and Computer Science Foundation Credits 4 This course introduces students to fundamental ideas in computer science while building skills in software development. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. CSCI 111 is appropriate for all students who want to be able to write programs, regardless of the domain. It is the typical first course for computer science majors and minors. No previous programming experience required. Lectures and formal laboratories.
- DCI 110 - Web Programming for Non-Programmers
FDR SC Science, Math, CS Distribution Credits 4 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.
- Electives
- BIOL 282 - Modeling and Simulations in Public Health
FDR SL Lab Science Distribution Credits 4 Prerequisite MATH 101 Where are infections spreading? How many people will be affected? What are some different ways to stop the spread of an epidemic? These are questions that all of us ask during an outbreak or emergency. In a process known as modeling, scientists analyze data using complex mathematical methods to provide answers to these and other questions during an emergency response. Models provide the foresight that can help decision-makers better prepare for the future. In this course you will learn how to develop a simple mathematical models using data. You will learn basic epidemiological concepts, computational data analysis tools and relevant mathematical techniques to integrate existing data into the model and generate relevant predictions. In an open-ended project, you and several of your classmates will develop a model and recommendation about potential public health threat. No prior programming experience required - you will learn to use Python, a popular open-source programming language and Jupyter Notebook data analysis environment, to interactively explore data. Laboratory course.
- BIOL 302 - Modern Computational Biostatistics
Credits 3 Prerequisite BIOL 201 or CBSC 250 or permission of instructor Traditional approaches for statistical inference are based on methods developed in the pre-computer era. In most scientific fields, these methods are being replaced by more flexible and powerful methods based on computation. In this class, we will use regression-based methods to build statistical models, compare multiple models, and test models with data. We will start with linear regression, then move to mixed (random effects) models, then to hierarchical Bayesian models. The last section of the course will be an independent project; this can be based on data that a student has already collected or developed as a new project using publicly available data in a field of interest.
- BIOL 325 - Ecological Modeling and Conservation Strategies
Credits 4 Prerequisite BIOL 111, 113, and a MATH course numbered 101 or greater This course is an intensive introduction to foundational methods in ecological modeling and their application, with emphasis on the dynamics of exploited or threatened populations and developing strategies for effective conservation. Topics include managing harvested populations, population viability analysis, individual based models, and simulation modeling for systems analyses. Laboratory course.
- BIOL 385 - Molecular Mechanics of Life
Credits 4 Prerequisite BIOL 220 How do we study complex networks of interactions between molecules in cells? How do we discover what roles different molecular machines play in the development and behavior of cells and animals? How can we identify the ways in which medical illness is caused by the misregulation of biological complexes because of a pathogenic infection or genetic disease? Our approach to answering these questions reflects the same interdisciplinary strategy being used at the forefront of current biomedical research. We consider the ways in which traditional approaches in biochemistry, genetics and cell biology can be merged with new systems-level approaches such as genomics and proteomics, to allow us to probe the underlying molecular mechanics of life. In the classroom, we examine different molecular networks, while readings include selections from the primary literature. The laboratory is based on an investigation of a novel research question, designed and addressed by student participants. Laboratory course.
- BUS 306 - Seminar in Management Information Systems
Credits 3-4 Topics vary by term and instructor. May be repeated for degree credit if the topics are different. Prerequisite vary with topics. Preference to BSADM or JMCB majors during the first round of registration.
- BUS 315 - Database Management for Business
Credits 3 Prerequisite At least junior class standing and BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 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.
- BUS 316 - Business Analytics
Credits 3 Prerequisite BUS 202, CBSC 250, ECON 202, POL/INTR 202, or MATH 118 An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required. Preference to BSADM majors and DS / DSBA minors during the first round of registration.
- BUS 317 - Data Mining for Business Analytics
Credits 3 Prerequisite either BIOL 185, BUS 316, or CBSC 240; and at least junior class standing 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.
- CBSC 359 - Advanced Methods in Cognition and Emotion Research
Credits 3 Prerequisite instructor consent Directed research on a variety of topics in cognition and emotion. May be repeated for degree credit.
- CHEM 116 - Imaging Science in Art, Medicine, and Astronomy with Laboratory
FDR SL Credits 4 Prerequisite Instructor consent 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 data sets 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.
- CSCI 111 - Introduction to Computer Science
FDR FM Math and Computer Science Foundation Credits 4 This course introduces students to fundamental ideas in computer science while building skills in software development. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. CSCI 111 is appropriate for all students who want to be able to write programs, regardless of the domain. It is the typical first course for computer science majors and minors. No previous programming experience required. Lectures and formal laboratories.
- CSCI 112 - Data Structures
FDR SC Science, Math, CS Distribution Credits 4 Prerequisite CSCI 111 This course continues the introduction to computer science begun in CSCI 111. Emphasis is on the use and implementation of data structures (i.e., how to store information and access it efficiently), introductory algorithm analysis, and object-oriented design and programming with Python. Lectures and formal laboratories.
- CSCI 315 - Artificial Intelligence
Credits 3 Prerequisite CSCI 209 Basic concepts of heuristic search, game playing, natural language processing, and intelligent systems, with a focus on writing programs in these areas. Course combines a discussion of philosophical issues with hands-on problem solving.
- DCI 110 - Web Programming for Non-Programmers
FDR SC Science, Math, CS Distribution Credits 4 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.
- DS 285 - Statistical Methods for Correcting Bias
Credits 3 This course will investigate the differences and similarities between statistical bias and social bias. Students will study techniques to ameliorate or eliminate both.
- DS 395 - Special Topics in Data Science
Credits 3 Prerequisite instructor consent Exploration of a topic in data science, where students craft testable research questions and retrieve an appropriate data set to answer those research questions. Includes training in some aspect of data science and incorporates extensive independent student work, with individual projects being the key product of that work, synthesizing what you have learned in the data science minor and demonstrating mastery of core data-science skills. Topics may include causal inference for data science with directed acyclic graphs (DAGs); analysis of spatial data; or multilevel models. Data Science faculty. Offered periodically depending on faculty availability and expertise and student interest.
- DS 399 - Data Science Capstone
Credits 3 Prerequisite instructor consent In this course, students have the opportunity to identify a topic of interest, craft testable research questions, and retrieve an appropriate data set to answer those research questions. Students' primary goal is to synthesize what you have learned in the data science minor and demonstrate mastery of core data-science skills.
- DS 421 - Directed Individual Research
Credits 1 Prerequisite instructor consent Each student conducts primary research in partnership with a faculty member, by prior mutual agreement. Consult the program web page to find faculty pages with descriptions of current research areas. May be repeated for degree credit. No more than three credits of work at the 400 level may apply toward the minor. May be carried out during summer.
- DS 422 - Directed Individual Research
Credits 2 Prerequisite instructor consent Each student conducts primary research in partnership with a faculty member, by prior mutual agreement. Consult the program web page to find faculty pages with descriptions of current research areas. May be repeated for degree credit. No more than three credits of work at the 400 level may apply toward the minor. May be carried out during summer.
- DS 423 - Directed Individual Research
Credits 3 Prerequisite instructor consent Each student conducts primary research in partnership with a faculty member, by prior mutual agreement. Consult the program web page to find faculty pages with descriptions of current research areas. May be repeated for degree credit. No more than three credits of work at the 400 level may apply toward the minor. May be carried out during summer.
- ECON 203 - Econometrics
Credits 3 Prerequisite ECON 202 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 POL/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.
- EEG 316 - GIS and Remote Sensing
Credits 4 Prerequisite or Corequisite: EEG 100, EEG 101, EEG 102, EEG 103, EEG 105, EEG 107, or EEG 200 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.
- MATH 222 - Linear Algebra
FDR SC Science, Math, CS Distribution Credits 3 Prerequisite MATH 102 with a grade of C or greater, MATH 201, MATH 221, or MATH 221 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.
- MATH 309 - Probability
Credits 3 Prerequisite MATH 221 with a grade of C or greater Probability, probability density and distribution functions, mathematical expectation, discrete and continuous random variables, and moment generating functions.
- MATH 310 - Mathematical Statistics
Credits 3 Prerequisite MATH 309 Sampling distributions, point and interval estimation, testing hypotheses, regression and correlation, and analysis of variance.
- PHYS 265 - Modeling and Simulation of Physical Systems
Credits 4 Prerequisite PHYS 112 and MATH 102 An introduction to the innovative field of modeling and analysis of complex physical systems from such diverse fields as physics, chemistry, ecology, epidemiology, and a wide range of interdisciplinary, emerging fields such as econophysics and sociophysics. Topics vary according to faculty expertise and student interest. The goal is to seek the underlying physics laws that govern such seemingly diverse systems and to provide contemporary mathematical and computational tools for studying and simulating their dynamics. Includes traditional lectures as well as workshops and computational labs, group presentations, and seminars given by invited speakers.
- SOAN 219 - Applied Bayesian Regression for the Social Sciences
FDR SC Science, Math, CS Distribution Credits 3 This course is an introduction to applied Bayesian regression, emphasizing applications for social scientists. We begin by introducing some philosophical and mathematical bases of Bayesian inference. We then move on to a sustained focus on applied regression, starting with bivariate regression and moving on to regression with multiple predictors, up to and including models with interactions. Along the way, students will be exposed to the use of directed acyclic graphs (DAGs) in thinking about causality with observational data. Throughout the course students will carry out numerous analyses of data, learning by doing. Examples are drawn from anthropology, sociology, political science, and related fields.
- SOAN 220 - A World of Data: Baseball and Statistics
Credits 4 Prerequisite CBSC 250, ECON 203, POL/INTR 202, SOAN 218, or SOAN 219 An introduction to the world of data and data analysis, emphasizing Bayesian methods. Taking the case of contemporary sports, with a particular focus on baseball, it teaches students how to build models of player performance while also asking important questions about the limitations of such approaches to human activities. What is gained and lost in the world made by measuring human actions in reliable ways? How is our experience in the world--in this case as athletes playing and spectators living sports--affected when we see it in terms of statistics and predictive models? What interests and what concerns make up our lives when we engage the world in this way? What interests and concerns may be obscured? The course offers a rare opportunity to acquire some expertise in producing data-driven knowledge and decisions while also reflecting on what it is like to be a non-expert living in the world shaped by such expertise.
- SOAN 222 - Data Science Tools for Social Policy
FDR SC Science, Math, CS Distribution Credits 3 Prerequisite One of the following: BIOL 201, CBSC 250, DCI 202, ECON 202, POL/INTR 202, MATH 310, SOAN 218, or SOAN 219; or instructor consent Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice. All work will be done in R. No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.
- SOAN 264 - States, Data, and Population Policies in the Americas
FDR SS4 Social Science - Group 4 Distribution Credits 3 Prerequisite SOAN 101, SOAN 102, POV 101, or LACS 101 While concentrating on the societies of the Americas, students focus on two of the main domains within which states seek to understand and influence populations: policies governing the collection of information about their residents, such as the census, and those governing migration. The course is made up of two interwoven parts, a traditional seminar portion that examines such policies from the perspective of historical sociology and a data-lab portion in which we perform exploratory visualization of historical and contemporary census and migration data from the region, using the "tidyverse" suite of R packages. We reflect critically on our own work, making use of perspectives afforded by the historical sociology portion of the course.
- SOAN 265 - Exploring Social Networks
FDR SS4 Social Science - Group 4 Distribution Credits 3 This course will be a hybrid seminar/research lab that covers some of the most important findings and methods in the study of social networks (SNA), with a focus on analyzing sociocentric data (i.e., data about a whole network, as opposed to data about people's personal networks, about which I teach a different course), with an emphasis on the application of network methods to the study of social inequalities. In the lab portion of the class, we will learn how to do network analysis in R, covering topics like (a) basic network descriptive statistics; (b) visualization of networks; and (c) community detection and the identification of subgroups and roles in network data, along with other tools and ideas.
- SOAN 266 - Neighborhoods and Inequality
FDR SS3 Social Science - Group 3 Distribution Credits 3 Prerequisite One of the following: BIOL 201, CBSC 250, ECON 202, POL/INTR 202, MATH 118, MATH 310, SOAN 218, or SOAN 222; or instructor consent This course examines the ways in which residential context relates to social and economic inequalities. Drawing on empirical literature from sociology and related fields, we consider both (a) how residential contexts may shape individuals' opportunities and (b) the factors that may shape the persistence or change of concentrated advantage and disadvantage across those residential settings. Half of the course is a traditional seminar and half is a data analysis lab in which we learn tools of spatial data analysis and then apply them in individual student projects on contemporary cities.
- SOAN 268 - Migration, Identity, and Conflict
FDR SS4 Social Science - Group 4 Distribution Credits 3 Prerequisite SOAN 102, POV 101, or POL 105 Same as POL 268. This course focuses on the complex relationship between migration, political institutions, group identities, and inter-group conflict. The course is a hybrid of a seminar and research lab in which students (a) read some of the key social-scientific literature on these subjects, and (b) conduct team-based research making use of existing survey data about the integration of migrant populations into various polities.
- SOAN 276 - Art & Science of Survey Research
Credits 3 Prerequisite SOAN 102 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).
- SOAN 365 - Modeling Social Networks
FDR SC Credits 3 This course focuses on the statistical modeling of social networks. We will learn how to build models to develop and test theories about why any given network has the structure that it does. This generally means predicting who has ties with whom and developing and testing theories about why the pattern of ties in any given network emerges as it does. We will begin with a review of OLS and logistic regression. Our main focus, and student data assignments, will be on exponential random graph models (ERGMs) and on latent space models. We will also briefly cover the basics of models used to study diffusion and influence.
- SOAN 395 - Senior Seminar in Quantitative Analysis
Credits 3 Prerequisite SOAN 102 and Methods requirement for the Sociology and Anthropology major 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.
and, when appropriate,
- BIOL 297 - Topics in Biology
Credits 3-4 Intermediate-level biology topics. Topics vary with instructor and term. Repeatable for credit if topics are different. Prerequisites vary with topic.
- Portfolio:
- DS 401 - Directed Individual Study
Credits 1 Prerequisite instructor consent 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.
At least three credits chosen from among the following:
At least three credits chosen from among the following:
At least three credits chosen from among the following:
At least nine additional credits chosen from among the following:
DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor in which students reflect on data-science competencies
No more than three credits of 400-level work can be counted towards the minor. Additional prerequisites may be required depending on course choices above.