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 280 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
Foundation13
Python Programming I3
Python Programming II
Statistical Inference I4
Advanced Regression3
Select one of the following courses
R Programming I3
R Programming II3
Statistical Analysis with R3
Methodologies9
Introduction to Data Mining3
Applied Machine Learning I3
Machine Learning I
Select one of the following courses
Database Management Systems I3
Database Management Systems3
Web Development and Database Applications3
Advanced Topics and Applications9
Applied Multivariate Analysis3
Fourier Series and Boundary Value Problems3
Elementary Stochastic Processes
Characterizing Time-Dependent Engineering Data3
Business Analytics I
Stochastic Processes Modeling
Queuing Systems
Multimedia Theory and Production3
Seminar in Communication Technologies
Computer Graphics I3
Multimedia Tools and Support
Business Analytics II3
Enterprise Resource Planning & Business Processes3
Database Management Systems II3
Select one of the following courses
Numerical and Statistical Methods in Astrophysics3
Digital Image Processing3
Advanced Bioinformatics and NCBI Database3
Capstone Experience3
Select one of the following courses
Master's Project3
Master's Thesis3
Special Research Problems3
Master's Technical Report3
Master's Thesis3
Internship
Total Credits34