Program Co-Chairs: Bob Hesse (Mathematics), Imad Rahal (Computer Science), and Parker Wheatley (Economics)
The Data Analytics (DATA) minor, rooted in the liberal arts and interdisciplinary in nature, provides opportunities for students to discover new knowledge and explore problems through the ethical acquisition and analysis of data. Data Analytics provides students with the opportunity to acquire the tools of data visualization, statistical analysis, and programming and to use those tools to answer meaningful questions related to problems and topics in a wide variety of fields. Depending on the student, data analytics will allow students to think of ways to shape policy, direct business, manage finance, understand health outcomes, assess biological and ecological systems, and even understand language and history. Upon completing the Data Analytics minor:
- Our students will be able to ask meaningful questions of data relevant to their primary disciplinary interest.
- Our students will prioritize the ethics of the collection, communication, and curation of the data they analyze.
- Our students will effectively visualize and analyze data and communicate their findings persuasively.
- Our students will be prepared to take the questions, ethics, and tools they acquire in this program to their future careers and academic studies.
Minor Requirements (22)
College-Level Statistics (4 credits)
DATA 162 (2 credits) – Introductory Data Analysis and Visualization
CSCI 150 (4 credits) – Introduction to Programming and Problem Solving
DATA 272 (2 credits) – Intermediate Data Analysis and Visualization; Prerequisite: CSCI 1xx, DATA 162, and 1 college-level statistics course
DATA 314 (2 credits) – Data Analysis Project; prerequisite: DATA 272 and completion of 1 Elective course
Current Elective Courses:
Below is the current list of approved electives, but additional courses will be added as faculty develop courses appropriate for the learning goals. The design of this minor is intended to combine both technical skill with disciplinary context. It is not intended as a purely technical approach to the problems associated with data management and analysis but seeks to help students make connections from the technical to the disciplinary. These electives serve two purposes: (1) increase breadth for students in more technical majors and (2) increase technical skills for students in less technical majors. The end goal of the experience would be that students have met all learning goals of the minor. Students will take a total of 8 credits of approved electives, at least four credits of which cannot be in their major. At least 4 credits of the electives must be at the 300-level.
ACFN 340 Accounting Information Systems
BIOL 373F Bioinformatics (4) (cross-listed as CSCI-317D (and MATH 340)))
BIOL 373L Mathematical Modelling in Biology (4) (also cross-listed with Math 340 Math Topics)
BIOL 316 Genetics
CSCI 160 Problem-Solving, Programming, and Computers (for non-CS majors/minors)
CSCI 200 Data Structures (for non-CS majors/minors)
CSCI 317D Bioinformatics (4)
CSCI 331 Database Systems (4)
CSCI 332 Machine Learning using Big Data (4)
CSCI 351 Principles of Parallel Computing (4)
ECON 314 Economics of Financial Institutions and Markets
ECON 334: Quantitative Methods in Economics
ECON 350: Introduction to Econometrics
ECON 353 Labor Economics and Public Policy
ECON 376 Industrial Organization and Public Policy
ENVR 311: Introduction to Geographical Information Systems
GBUS 342 Advanced Computer Applications (2)
GBUS 343 Information Systems and Security Concerns in Global Business (2)
MATH 318 Applied Statistical Models
MATH 339 Mathematical Modeling
PHYS 222 Fortran and C++ for Scientists (2)/ PHYS 322 Fortran & C++ for Scientists (2)
POLS 222 and POLS 223
POLS 343 Revolutions and Social Movements
POLS 355 Globalization
POLS 356 Defense, Diplomacy, and Development
POLS 358 Topics - Models of Conflict and Bargaining