Data Analytics - Master of Data Analytics

PMS in Data Analytics (100% Online)

Data analytics is an inherently interdisciplinary discipline, dealing with methods and systems to synthesize knowledge or insights from large quantities of data collected from heterogeneous sources and diverse spatial and time scales. Data analytics employs theories, methodologies, and tools drawn from many fields, within the broad areas of mathematics, statistics, and computer and information sciences, and applies them to a diversity of data-rich domains, such as life sciences, medicine, physical sciences, social sciences, engineering, business, and education.

The PMS in Data Analytics will provide students with a strong foundation in data management and analysis, the computational and statistical thinking, and understanding of computer systems. After completing this program, students will have gained the skills and ability to:

  • Analyze real-life data from diverse sources and domains
  • Effectively apply analytics tools to large data sets
  • Apply mathematical and statistical models to data analysis problems
  • Apply computational thinking to develop effective data analytics solutions
  • Apply programming and debugging skills to problem solving
  • Understand and use computer technology and software in solving real-life data analysis problems
  • Understand and address unfamiliar problems related to data analytics
  • Develop effective instrument to communicate solutions to diverse audiences


The professional focus of the degree will prepare students for success in the workplace, with an emphasis on enriching the preparation of students who are already in the workplace and are seeking technical skills to advance their careers in the data analytics domain.

Program Features 

  • Degree granted from New Mexico State University
  • Asynchronous courses delivery to accommodate student schedules & needs
  • 30 credits [3 semesters and a summer, 9 credits/semester; students may enroll part time]
  • In person courses are permissible 
  • Industry experience encouraged 

Affiliated Faculty (Non-Computer Science Faculty)   

  • Laura Boucheron, PhD, University of California Santa Barbara; Image processing, machine learning and deep learning applied to image analysis, interdisciplinary applications including astronomy and biomedical; Klipsch School of Electrical & Computer Engineering 
  • Hansuk Sohn, PhD, University of Iowa; Mathematical Programming (Linear, Integer, and Stochastic) and Dynamic Programming, Algorithm development (Optimization, Heuristic, and Hybrid algorithms), Statistical Data Analysis and Data Mining; Industrial Engineering
  • Charlotte Gard, PhD, University of Washington; Biostatistics; Department of Economics, Applied Statistics, and International Business
  • Clint Loest, PhD, Kansas State University; Ruminant Nutrition, Animal Nutrition; Animal and Range Sciences
  • Carlo A. Mora-Monge, PhD, The University of Toledo; Business Analytics, Supply Chain Analytics, E-commerce Use; Management Department
  • Marshall A. Taylor, PhD, University of Notre Dame; Computational Social Science, Cultural Sociology, Cognitive Sociology; Department of Sociology 


The curriculum for the degree program is composed of 30 graduate credits.

Prefix Title Credits
C S 453Python Programming I3
or C S 454 Python Programming II
A ST 511Statistical Methods for Data Analytics3
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
Advanced Methods in Astrophysics3
Advanced Bioinformatics and NCBI Database3
Business Analytics II3
Enterprise Resource Planning & Business Processes3
Bioinformatics Programming3
Computer Graphics I3
Advanced Software Development Concepts
Database Management Systems II3
Digital Image Processing3
Multimedia Theory and Production3
Seminar in Communication Technologies
Characterizing Time-Dependent Engineering Data3
Business Analytics I
Stochastic Processes Modeling3
Queuing Systems
Design and Implementation of Discrete-Event Simulation3
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 Thesis3
Special Research Problems3
Master's Technical Report3
Master's Thesis3
Total Credits30