Data Analytics


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 (22 credits)

Required Courses

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 150, DATA 162, and 1 college-level statistics course
  • DATA 314 (2 credits) – Data Analysis Project
    Prerequisite: DATA 272 and completion of 1 Elective course

Elective Courses

8 credits of approved electives:

  • at least 4 credit outside the student’s major and
  • at least 4 credits at the 300-level. 

Current approved electives are listed below.

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 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


PSYC 235 Research Methods

PSYC 347 Advanced Statistics and Measurement

Contact the Program Co-Chairs
College of Saint Benedict
Saint John’s University

Robert Hesse, Mathematics
CSB Main 212

Imad Rahal, Computer Science
CSB Main 262

Parker Wheatley, Economics
CSB Main 309

[email protected]