Data Analytics - Master of Data Analytics (Online)

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

Program 

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 
  • Charlotte Gard, PhD, University of Washington; Biostatistics; Department of Economics, Applied Statistics, and International Business
  • Marshall A. Taylor, PhD, University of Notre Dame; Computational Social Science, Cultural Sociology, Cognitive Sociology; Department of Sociology 

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 I Both A ST 505 and A ST 507 can be replaced by A ST 511 (Statistical Methods for Data Analytics) or equivalent one4
A ST 507Advanced Regression See A ST 505 3
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: A ST 616 Computational Statistics can also be used 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 courses A ST 616 Computational Statistics can also be used 3
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