Data Analytics

Courses and Current Electives

Courses (DATA)

DATA 162 (2 Credits) – Introductory Data Analysis and Visualization
Pre- or Co-Requisite: None

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)

DATA 272 (2 credits) – Intermediate Data Analysis and Visualization
Prerequisite: CSCI  150, DATA 162, a College-Level Statistics course

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.

Current Electives

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
  • 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
  • PSYC 235 Research Methods
  • PSYC 347 Advanced Statistics and Measurement
College of Saint Benedict
Saint John’s University

Robert Hesse, Computer Science & Mathematics
CSB Main 252

Imad Rahal, Computer Science
CSB Main 262

Parker Wheatley, Economics
CSB Main 309

[email protected]