Data Science (B.S.)

Print Degree Planner (opens a new window) |

Program Coordinator/Contact

Kurt Cogswell, Department Head
Department of Mathematics and Statistics
Chicoine Architecture, Mathematics and Engineering Hall 209
605-688-6196

Program Information

The Data Science major prepares students to use the power of data to help organizations of all sorts and society as a whole to make better decisions. Students learn to use mathematics, statistics, and computational tools to analyze data of all sorts, and learn to communicate the results of their analysis in order to produce the greatest positive impact. Graduates are prepared for any post-graduation outcome including immediate entry into the professional world or further study in a pursuit of a M.S. in Data Science.

Student Learning Outcomes

Upon completion of the Data Science major, students should be able to:

Apply concepts and methods from Calculus and Linear Algebra. (Cross-curricular Skill: Inquiry and Analysis; Information Literacy)
Work as part of a team to solve a complex Data Science problem. (Cross-curricular Skill: Teamwork)
Gather requirements from professional contexts and translate them into a clearly articulated data analysis problem. (Cross-curricular Skill: Inquiry and Analysis; Critical and Creative Thinking; Problem Solving)
Identify, gather, and prepare data necessary for analysis (Cross-curricular Skill: Inquiry and Analysis)
Select the optimal combination of mathematical, statistical, and computing techniques necessary to solve a problem. (Cross-curricular Skill: Inquiry and Analysis; Critical and Creative Thinking; Information Literacy; Problem Solving)
Conduct analysis in a manner that produces well-understood and reproducible results and avoids common analytical and ethical problems associated with data analysis. (Cross-curricular Skill: Inquiry and Analysis; Critical and Creative Thinking; Information Literacy; Problem Solving)
Interpret and communicate results in the optimal combination of written, graphical/visual, and verbal means, to provide actionable intelligence to the stakeholder. (Cross-curricular Skill: Problem Solving)
Appreciate the value of and work efficiently with a diverse group of individuals. (Cross-curricular Skill: Diversity, Inclusion and Equity)

Academic Requirements

A grade of “C” or above is required in all MATH and STAT courses.

Course Delivery Format

Program courses are delivered on campus, in classroom and laboratory settings, and online.

Requirements for Data Science Major: 120 Credits

Bachelor of Science

System General Education Requirements

Goal #1 Written Communication: SGR #1 Electives Credits: 6
Goal #2 Oral Communication: SGR #2 Elective Credits: 3
Goal #3 Social Sciences/Diversity: SGR #3 Electives Credits: 6
Goal #4 Arts and Humanities/Diversity: SGR #4 Electives Credits: 6
Goal #5 Mathematics:
MATH 123 - Calculus I (COM) [SGR #5] Credits: 4
Goal #6 Natural Sciences: INFO 101 - Introduction to Informatics [SGR #6] Credits: 3  and PHYS 111-111L - Introduction to Physics I and Lab (COM) [SGR #6] Credits: 4  or
PHYS 211-211L - University Physics I and Lab (COM) [SGR #6] Credits: 4
  or
CHEM 106-106L - Chemistry Survey and Lab (COM) [SGR #6] Credits: 3, 1
  or
CHEM 112-112L - General Chemistry I and Lab (COM) [SGR #6] Credits: 3, 1
  or BIOL 151-151L - General Biology I and Lab (COM) [SGR #6] Credits: 4

Major Requirements

MATH 125 - Calculus II (COM) [SGR #5] Credits: 4
MATH 198 - The Mathematics Profession Credits: 1
MATH 225 - Calculus III (COM) [SGR #5] Credits: 4
MATH 230 - Sophomore Seminar Credits: 1
MATH 250 - Introduction to Linear Algebra and Proof Credits: 3
MATH 253 - Logic, Sets, and Proof Credits: 4
MATH 401 - Senior Capstone Credits: 1-2 (2 credits required)
STAT 382 - Probability Credits: 3
STAT 482 - Mathematical Statistics Credits: 3

Select from the following

Select twenty-four credits from the following. Credits: 24

CSC 250 - Computer Science II (COM) Credits: 3
CSC 300 - Data Structures (COM) Credits: 3
CSC 319 - Parallel Computing (COM) Credits: 3
MATH 316 - Discrete Mathematics (COM) Credits: 3
MATH 374 - Scientific Computation I Credits: 3
MATH 475-575 - Operations Research (COM) Credits: 3
STAT 101 - Introduction to Data Science Credits: 3
STAT 383 - Geospatial Data Analysis Credits: 3
STAT 410-510 - SAS Programming Credits: 3
STAT 415-515 - R Programming Credits: 3
STAT 441-541 - Statistical Methods II Credits: 3
STAT 442 - Exploratory and Cloud-Based Data Analysis Credits: 3
STAT 445-545 - Nonparametric Statistics (COM) Credits: 3
STAT 451-551 - Predictive Analytics I Credits: 3
STAT 453-553 - Applied Bayesian Statistics Credits: 3
STAT 460-560 - Time Series Analysis (COM) Credits: 3

Supporting Coursework

CSC 150 - Computer Science I (COM) Credits: 3

Electives

Taken as needed to complete any additional requirements.

Total Required Credits: 120

Summary of Program Requirements

Bachelor of Science
System General Education Requirements	32 Credit Hours
Major Requirements	50 Credit Hours
Supporting Coursework	3 Credit Hours
Electives**	38 Credit Hours

**Taken as needed to complete any additional degree requirements.

Academic Advising Guide Sheet

The goal of the academic advising guide sheets and sample plans of study is to promote undergraduate student success by guiding all students to timely completion of an undergraduate degree. Students are not limited to the course sequence provided for their academic program. Instead, the sample plan of study is one possible path to completing your degree and is meant to be used as a guide for planning purposes in consultation with an academic advisor. The plans also help students prepare for meetings with their academic advisor and track their progress in their selected academic program.

Data Science (B.S.)