Program CoDirectors: 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).
Code  Title  Hours 

Required Computer Science courses:  
COSC 1010  Introduction to Software Development  4 
COSC 1020  ObjectOriented Software Design  4 
COSC 2100  Data Structures  3 
COSC 4610  Data Mining  3 
COSC 4800  Principles of Database Systems  3 
Required Mathematics courses:  
MATH 1450  Calculus 1  4 
MATH 1451  Calculus 2  4 
MATH 2350  Foundations of Mathematics  3 
or MATH 2100  Discrete Mathematics  
MATH 2450  Calculus 3  4 
MATH 3100  Linear Algebra and Matrix Theory  3 
MATH 3570  Introduction to Data Science  3 
or COSC 3570  Introduction to Data Science  
MATH 4700  Theory of Probability  3 
MATH 4720  Statistical Methods  3 
or MATH 4740  Biostatistical Methods and Models  
MATH 4780  Regression Analysis  3 
Computer Science or Mathematics electives: Choose two of the following.  6  
Bioinformatics Algorithms  
Algorithms  
Visual Analytics  
Fundamentals of Artificial Intelligence  
Ethical and Social Implications of Data  
Mathematical Modeling and Analysis  
Mathematical Statistics  
Time Series Analysis  
INDS 4997  Capstone in Data Science  3 
Total Credit Hours:  56 
Note:

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
Freshman  

First Term  Hours  Second Term  Hours 
COSC 1010  4  COSC 1020  4 
MATH 1450  4  MATH 1451  4 
ENGL 1001 or ESSV1 (MCC)  3  ENGL 1001 or ESSV1 (MCC)  3 
PHIL 1001 or THEO 1001 (MCC)  3  PHIL 1001 or THEO 1001 (MCC)  3 
14  14  
Sophomore  
First Term  Hours  Second Term  Hours 
COSC 2100  3  MATH 3100  3 
MATH 2350  3  MATH 3570 or COSC 3570  3 
MATH 2450  4  MATH 4720 or 4740  3 
CORE 1929 (MCC) or elective  3  CORE 1929 (MCC) or elective  3 
Elective  3  DSCV (MCC)^{1,2}  3 
16  15  
Junior  
First Term  Hours  Second Term  Hours 
COSC 4800  3  COSC 4610  3 
MATH 4700  3  COSC or MATH Science elective  3 
DSCV (MCC)^{1,2}  3  DSCV (MCC)^{1,2}  3 
Elective  6  DSCV (MCC)^{1,2}  3 
Elective  3  
15  15  
Senior  
First Term  Hours  Second Term  Hours 
MATH 4780  3  INDS 4997  3 
COSC or MATH science elective  3  CORE 4929 (MCC) or elective  3 
CORE 4929 (MCC) or elective  3  Electives  9 
Electives  7  
16  15  
Total Credit Hours: 120 
^{1}  The four courses in the Discovery Tier (DSCV) of the MCC must be completed in the same theme and include the following content areas: Humanities (HUM), Social Science (SSC), Natural Science and Mathematics (NSM) and one elective (ELE), which is an additional course from any of the three content areas. A maximum of two courses in the Discovery Tier can apply towards a primary major. 
^{2}  Students must also complete the Writing Intensive (WRIT) and Engaging Social System and Values 2 (ESSV2) requirements of the MCC. These requirements can be fulfilled through designated courses in the Discovery Tier or other degree requirements. 
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.
Code  Title  Hours 

Required Computer Science courses:  
COSC 1010  Introduction to Software Development  4 
COSC 4610  Data Mining  3 
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 3570  Introduction to Data Science  3 
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 
Notes:
 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.