M.S. in Analytics (Concentration in Computational Data Analytics)

The Computational Data Analytics concentration for the Master of Science in Analytics (MSA) provides students with a practical and theoretical background in data science and analytics. This program features a multidisciplinary curriculum that draws on insights from computer science, statistics, and business management. The program prepares students to apply the latest data analytics, data mining, machine learning, and statistical methods to growing real-world data sets. Students will learn statistical and computational methods for collecting, storing, and processing data; identifying patterns in large datasets; predicting and interpreting the findings; and making data-driven decisions. Admission to the concentration of Computational Data Analytics requires considerable computer science, programming, and mathematical competencies.

Admission Requirements

Applicants for the MSA program must satisfy the program’s general admission requirements, which include the TOEFL (for non-English-speaking international students), GRE, college transcripts, statement of background and goals, resume, and three letters of recommendation. The Concentration in Computational Data Analytics has the following additional requirements:

A baccalaureate degree in computer science, natural and physical sciences, engineering, mathematics, or statistics with a 3.0 GPA. Students from other disciplines may also be considered. The student is expected to have completed the following foundation coursework or equivalent:

  • CSc 2010  Principles of Computer Science (CS1)
  • CSc 2310  Principles of Computer Programming (CS2)
  • CSc 2510  Theoretical Foundations of Computer Science
  • CSc 3410  Data Structures
  • MATH 2211  Calculus of One Variable I
  • MATH 2212  Calculus of One Variable II
  • MATH 2215  Multivariate Calculus

Application Process

Complete the online application along with the $50 application fee.

Degree Requirements

Requirements for the MSA degree (Concentration in Computational Data Analytics) include finishing any necessary foundation coursework and completing 32 hours of graduate-level coursework as shown in the following.

Credit Hours: 32
Length of Program: 16 months or 3 semesters
Mode of Instruction: Fully residential
This is a full-time, cohort-driven, lock-step program with 23 credit hours of required courses and 9 elective hours.

1. Required Core Courses (23 credit hours):

MRM 8000  Introduction to Analytical Programming and Numerical Methods (2 hours)

MSA 8010  Data Programming for Analytics (3 hours)
MSA 8190  Statistical Foundation for Analytics (3 hours)
CIS 8040  Database Management Systems (3 hours)

MSA 8050  Unstructured Data Management (3 hours)
MSA 8150  Machine Learning for Analytics (3 hours)
CSc 6999  Data Mining for Analytics (3 hours)
STAT 8670  Computational Methods in Statistics (3 hours)

2. Elective Courses (at least 9 credit hours; at least one in statistics and one in computer science; may count seminar course from this list for 1 or 2 credit hours):

CSc 6260  Introduction to Image Processing (4 hours)
CSc 6730  Data Visualization (4 hours)
CSc 6760  Big Data Programming (4 hours)
CSc 8711  Databases and the Web (4 hours)
CSc 8740  Advanced Data Mining (4 hours)
CSc 8850  Machine Learning (4 hours)
CSc 6630  MATLAB Programming (4 hours)
CSc 6810   Artificial Intelligence (4 hours)
CSc 8260  Advanced Image Processing (4 hours)
CSc 6310  Parallel and Distributed Computing (4 hours)
CSc 8713  Spatial & Scientific Databases (4 hours)
CSc 8810  Computational Intelligence (4 hours)
CSc 8910  Seminar in Big Data Analytics (1–3 hours)
STAT 8310  Applied Bayesian Statistics (3 hours)
STAT 8440  Survival Analysis (3 hours)
STAT 8610  Time Series Analysis (3 hours)
STAT 8630  Experimental Designs (3 hours)
STAT 8680  Applied Nonparametric Methods (3 hours)
STAT 8700  Categorical Data Analysis (3 hours)
STAT 8674  Monte Carlo Methods (3 hours)
STAT 8090  Applied Multivariate Statistics (3 hours)
Other approved graduate courses

Note: Complete degree requirements can be found in the Georgia State University Graduate Catalog. To see degree requirements for previous years, please visit the Georgia State catalog archive.