Computational Data Analytics - Master of Computational Data Analytics

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
Foundation
C S 453Python Programming I3
or C S 454 Python Programming II
A ST 505Statistical Inference I4
A ST 507Advanced Regression3
Select one of the following courses3
R Programming I3
Statistical Analysis with R3
Methodologies
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 six credits from the following:6
Bioinformatics Programming3
Bioinformatics3
Applied Multivariate Analysis3
Fourier Series and Boundary Value Problems3
Elementary Stochastic Processes
Characterizing Time-Dependent Engineering Data3
Business Analytics I
Stochastic Processes Modeling3
Queuing Systems
Design and Implementation of Discrete-Event Simulation3
Multimedia Theory and Production3
Seminar in Communication Technologies
Computer Graphics I3
Multimedia Tools and Support
Advanced Seminar in Social Networks
Advanced Seminar in Text Analysis for the Social Sciences3
Advanced Seminar in Data Visualization3
Business Analytics II3
Enterprise Resource Planning & Business Processes3
Database Management Systems II3
Select one of the following courses3
Numerical and Statistical Methods in Astrophysics3
Digital Image Processing3
Advanced Bioinformatics and NCBI Database3
Capstone Experience
Select one of the following courses3
Master's Project3
Master's Thesis3
Special Research Problems3
Master's Technical Report3
Master's Thesis3
Internship
Total Credits34