Data Analytics - Master of Data Analytics (Online)

The admission requirements for the degree program requires incoming students to have a minimum mathematical preparation at the level of Linear Algebra (MATH 2415 Introduction to Linear Algebra or equivalent course, such as E E 200 Linear Algebra, Probability and Statistics Applications).

The curriculum for the degree program is composed of 34 graduate credits.

Prefix Title Credits
C S 453Python Programming I3
or C S 454 Python Programming II
A ST 511Statistical Methods for Data Analytics can be replaced by (A ST 505 and A ST 507) 3
Select one of the following courses3
R Programming I3
Statistical Analysis with R3
C S 508Introduction to Data Mining3
C S 519Applied Machine Learning I3
or E E 565 Machine Learning I
Select one of the following courses3
Database Management Systems I3
Database Management Systems3
Web Development and Database Applications3
Advanced Topics and Applications
Choose nine credits from the following:9
Applied Multivariate Analysis3
Computational Statistics3
Python Programming II3
Computer Science I Transition3
Introduction to Data Structures Transition3
Advanced Methods in Astrophysics3
Business Analytics II3
Advanced Bioinformatics and NCBI Database3
Computer Graphics I3
Advanced Software Development Concepts
Bioinformatics Programming3
Database Management Systems II3
Digital Image Processing3
Characterizing Time-Dependent Engineering Data3
Business Analytics I
Stochastic Processes Modeling3
Queuing Systems
Design and Implementation of Discrete-Event Simulation3
Multimedia Theory and Production3
Communication Technologies
Fourier Series and Boundary Value Problems3
Elementary Stochastic Processes
Seminar in Social Networks3
Seminar in Text Analysis for the Social Sciences3
Seminar in Data Visualization3
Capstone Experience
Select one of the following courses3
Master's Project3
Master's Thesis1-15
Independent Study1-3
Special Research Problems3
Independent Study1-3
Master's Technical Report3
Master's Thesis3
Total Credits30

A Suggested Plan of Study

Additional classes may be needed based on placement test results and course prerequisites. Visit with an advisor for help with creating a customized plan.

Plan of Study Grid
First Year
A ST 511 Statistical Methods for Data Analytics 3
C S 453 Python Programming I 3
C S 508 Introduction to Data Mining 3
C S 519 Applied Machine Learning I 3
C S 458 R Programming I 3
One Elective Course from the list of Advanced Topics and Applications courses 3
Second Year
Two Elective Coures from the list of Advanced Topics and Applications courses 6
C S 502
Database Management Systems I
or Web Development and Database Applications
Choose one from the capstone experience group 3
 Total Credits30