Data Analytics Minor
Curriculum for Data Analytics Minor (22 Credits)
Required Courses (14 Credits)
College-Level Statistics (4 credits)
Rationale for Requirement: The curricular purpose of this requirement is that students will have or be obtaining an understanding of basic statistical measurement, proper statistical testing methods, and a clear understanding of proper/improper uses of statistical tools. Such understanding should provide students with the ability to draw appropriate conclusions about visual and statistical findings.
Courses that Meet Requirement: This course could be satisfied in multiple ways provided that the course generally satisfies the three learning goals below. The steering committee of the minor would review curriculum of on-campus offerings as well as off-campus offerings PSEO, AP, IB to ensure that these goals are met.
DATA 162 (2 Credits) – Introductory Data Analysis and Visualization
Pre- or Co-Requisite: None
Grounded in the liberal arts, students will learn how data can be used to understand the world through multiple disciplinary lenses (e.g., business, economics, history, music, political science, et cetera). Students will learn introductory data organization and visualization skills, and they will understand both the limitations and ethical use/misuse of data. Students will pose interesting questions about real-world data, learn the computational tools needed to organize and visualize data related to the question, draw meaningful conclusions from such analyses, and communicate their findings. Some basic statistical measures will be discussed in the course (e.g., mean, median, measures of variation)
CSCI 150 (Introduction to Programming and Problem Solving) (4 credits)
As part of the Data Analytics minor, this course is intended to strengthen students understanding of programming and problem solving with computers in general and data related problems more specifically. Learning the Python programming language is expected.
DATA 272 (2 credits) – Intermediate Data Analysis and Visualization
Prerequisite: CSCI 1xx, DATA 100, a College-Level Statistics course
Description: This course provides students an opportunity to gain greater proficiency in computer programming, data management, and visualization. The emphasis will be on leveraging the skills acquired in earlier courses and providing students with the opportunity to more fully develop their abilities to organize, visualize, and analyze real-world data as well as communicate findings.
DATA 314 – Data Analysis Project (2 credits)
Prerequisite: DATA 272 and completion of 1-Elective course
This course will provide an integrative experience where students use computing, statistics, and disciplinary skills to explore a particular data-related project. The final project should demonstrate satisfaction of the learning goals of the minor. Students complete a standalone activity distinct from their major under the supervision of a faculty member outside of their major.
Elective Courses (8 credits)
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 is intended to help students make connections from the technical to the disciplinary. Consistent with the structure of such minors at many liberal arts colleges, these electives are intended to serve 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.
Current Elective Courses
The minor would welcome additional course proposals from the arts, digital humanities and other sciences and social sciences.
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
CS 160 Problem-Solving, Programming, and Computers (for non-CS majors/minors)
CS 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 (current methods courses – if POLS goes to a standalone methods course that might change)
POLS 343 Revolutions and Social Movements
POLS 355 Globalization
POLS 356 Defense, Diplomacy, and Development
POLS 358 Topics – Models of Conflict and Bargaining
Program Coordinators (Fall 2020)
Robert Hesse, Mathematics
Main 212; 320-363-5518
Parker Wheatley, Economics
Main 309; 320-363-5917