Program Co-Directors: Elaine Spiller, Ph.D and Michael Zimmer, Ph.D

Data Science is the emerging field that seeks to extract and quantify knowledge from data. The Interdisciplinary Data Science major (INDS) integrates statistics and mathematics with computer science, allowing students to develop the knowledge and skills necessary to discover and quantify new knowledge from data. Those prepared to integrate advanced technology with modern statistical and mathematical practices have the opportunity to use data in action to benefit society. Data scientists turn data into knowledge.

Interdisciplinary Major in Data Science

The interdisciplinary data science major consists of 56 credit hours of computer science and mathematics courses, including fourteen required courses (47 credit hours), two computer science or mathematics electives (6 credit hours) and a data science capstone course (3 credit hours).

Required Computer Science courses:
COSC 1010Introduction to Software Development4
COSC 1020Object-Oriented Software Design4
COSC 2100Data Structures3
COSC 4610Data Mining3
COSC 4800Principles of Database Systems3
Required Mathematics courses:
MATH 1450Calculus 14
MATH 1451Calculus 24
MATH 2350Foundations of Mathematics3
or MATH 2100 Discrete Mathematics
MATH 2450Calculus 34
MATH 3100Linear Algebra and Matrix Theory3
MATH 3570Introduction to Data Science3
or COSC 3570 Introduction to Data Science
MATH 4700Theory of Probability3
MATH 4720Statistical Methods3
or MATH 4740 Biostatistical Methods and Models
MATH 4780Regression Analysis3
Computer Science or Mathematics electives: Choose two of the following.6
Bioinformatics Algorithms
Visual Analytics
Fundamentals of Artificial Intelligence
Ethical and Social Implications of Data
Mathematical Modeling and Analysis
Mathematical Statistics
Time Series Analysis
INDS 4997Capstone in Data Science3
Total Credit Hours:56


  • Depending on course topic and approval by both departments, upper division COSC or MATH courses outside of the list may be substituted as a computer science or mathematics elective.

Typical Program for Data Science Major 

First TermHoursSecond TermHours
COSC 10104COSC 10204
MATH 14504MATH 14514
ENGL 1001 or ESSV1 (MCC)3ENGL 1001 or ESSV1 (MCC)3
PHIL 1001 or THEO 1001 (MCC)3PHIL 1001 or THEO 1001 (MCC)3
 14 14
First TermHoursSecond TermHours
COSC 21003MATH 31003
MATH 23503MATH 3570 or COSC 35703
MATH 24504MATH 4720 or 47403
CORE 1929 (MCC) or elective3CORE 1929 (MCC) or elective3
Elective3DSCV (MCC)1,23
 16 15
First TermHoursSecond TermHours
COSC 48003COSC 46103
MATH 47003COSC or MATH Science elective3
DSCV (MCC)1,23DSCV (MCC)1,23
Elective6DSCV (MCC)1,23
 15 15
First TermHoursSecond TermHours
MATH 47803INDS 49973
COSC or MATH science elective3CORE 4929 (MCC) or elective3
CORE 4929 (MCC) or elective3Electives9
 16 15
Total Credit Hours: 120

Interdisciplinary Minor in Data Science

The interdisciplinary data science minor consists of 19 credit hours of courses, including five required computer science and mathematics courses (16 credit hours) and an additional 3 credit hours of an advanced elective.

Required Computer Science courses:
COSC 1010Introduction to Software Development4
COSC 4610Data Mining3
Required Mathematics courses: Choose one of the following sequences:6
Modern Elementary Statistics
and Introduction to Regression and Classification
Statistical Methods
and Regression Analysis
Required Computer Science or Mathematics course:
COSC 3570Introduction to Data Science3
or MATH 3570 Introduction to Data Science
Advanced elective: Choose one of the following courses:3
Bioinformatics Algorithms
Visual Analytics
Fundamentals of Artificial Intelligence
Principles of Database Systems
Ethical and Social Implications of Data
Linear Algebra and Matrix Theory
Theory of Probability
Mathematical Statistics
Time Series Analysis
Regression Analysis
Total Credit Hours:19


  • Other courses may be approved as an advanced elective with the consent of the departments.
  • The MATH 1700 Modern Elementary Statistics and MATH 2780 Introduction to Regression and Classification sequence is recommended for students without a background in calculus. With the departments consent, MATH 1700 Modern Elementary Statistics may substituted with an equivalent statistics course.
  • The MATH 4720 Statistical Methods and MATH 4780 Regression Analysis sequence is recommended for students who have successfully completed a college level calculus course and are comfortable with calculus. MATH 4740 Biostatistical Methods and Models may be substituted for  MATH 4720 Statistical Methods.