M.S. Curriculum and Degree Requirements

Provides students a strong understanding of basic and advanced methods in statistical inference, machine learning, data visualization, data mining, and big data, all of which are essential skills for a high-performing data scientist.

Required Courses

Math 661 Applied Statistics
CS 644 Introduction to Big Data
CS 636 Data Analytics with R Programming
CS 675 Machine Learning
CS 677 Deep Learning

 

 

 

 

 

 

Math 630 Linear Algebra and Applications
CS 602 Java Programming
CS 610 Data Structures and Algorithms
CS 631 Data Management System Design
CS 632 Advanced Database System Design
CS 634 Data Mining
CS 670 Artificial Intelligence
CS 732 Advanced Machine Learning
CS 735 High Performance Analytics for Data Science
IS 601 Web Systems Development
IS 631 Enterprise Database Management
IS 634 Information Retrieval
IS 665 Data Analytics for Information Systems
IS 687 Transaction Mining and Fraud Detection
IS 688 Web Mining


 


 

 

 

 

 

 

 

 

 

 

 

 

 

 

To be eligible for admission, a student must have a B.S. degree with a minimum GPA of 2.8 on a 4.0 scale and have the following background (typically obtained through a BS in a STEM field):

  • Calculus: Derivatives, integrals, applications
  • Linear Algebra: Vector spaces, dot products, Euclidean norm, matrices
  • Probability and Statistics: Random variables, probability distributions, basic statistics
  • Programming: Basic programming constructs, writing and debugging programs, iteration, recursion, arrays, lists
  • Data Structures and Algorithms: Basic data structures, search and sort, algorithm analysis


Applicants lacking this background may take the Refresher boot camp to acquire it.
 

  • Be able to acquire, clean, and manage massive data sets.
  • Play an analytical role in your company where you design, implement, and evaluate advanced statistical models and approaches for application to your company’s most complex problems.
  • Be able to provide econometric and statistical models for a variety of problems including projections, classification, clustering, pattern analysis, sampling and simulations.
  • Research new ways for predicting and modeling end-user behavior as well as investigating data summarization and visualization techniques for conveying key applied analytics findings.
  • Apply modern artificial intelligence and deep learning methods to complex prediction and recognition tasks.