Data Analytics - Master of Data Analytics
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)
- 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
Curriculum
The curriculum for the degree program is composed of 30 graduate credits. It is divided into different categories. One course can be used to satisfy only one category.
| Prefix | Title | Credits |
|---|---|---|
| Foundation | ||
| CSCI 4520 | Python Programming I | 3 |
| or CSCI 4525 | Python Programming II | |
| A ST 511 | Statistical Methods for Data Analytics Can be replaced by A ST 505 and A ST 507 | 3 |
| Select one of the following courses | 3 | |
| R Programming I | 3 | |
| Statistical Analysis with R | 3 | |
| Methodologies | ||
| CSCI 5415 | Introduction to Data Mining | 3 |
| CSCI 5420 | Applied Machine Learning I | 3 |
| or E E 565 | Machine Learning I | |
| Select one of the following courses | 3 | |
| Database Management Systems I | 3 | |
| Database Management Systems | 3 | |
| Web Development and Database Applications | 3 | |
| Advanced Topics and Applications | ||
| Choose nine credits from the following: | 9 | |
| Applied Multivariate Analysis | 3 | |
| Computational Statistics | 3 | |
| Advanced Methods in Astrophysics | 3 | |
| Solar Astrophysics | 3 | |
| Advanced Bioinformatics and NCBI Database | 3 | |
| Business Analytics II | 3 | |
| Enterprise Resource Planning & Business Processes | 3 | |
| Python Programming II | 3 | |
| Computer Science I Transition | 3 | |
| Introduction to Data Structures Transition | 3 | |
| Bioinformatics Programming | 3 | |
| Bioinformatics | 3 | |
| Computer Graphics I | 3 | |
| Database Management Systems II | 3 | |
| Photovoltaic Devices and Systems | 3 | |
| Selected Topics (Numerical Computational Methods for Smart Grid) | 3 | |
| Digital Image Processing | 3 | |
| Multimedia Theory and Production | 3 | |
or COMM 5220 | Communication Technologies | |
| Characterizing Time-Dependent Engineering Data | 3 | |
or BCIS 561 | Business Analytics I | |
| Stochastic Processes Modeling | 3 | |
or I E 522 | Queuing Systems | |
| Design and Implementation of Discrete-Event Simulation | 3 | |
| Fourier Series and Boundary Value Problems | 3 | |
or STAT 5230 | Elementary Stochastic Processes | |
| Land Cover Analysis for Natural Resources | 3 | |
| Seminar in Social Networks | 3 | |
| Seminar in Text Analysis for the Social Sciences | 3 | |
| Seminar in Data Visualization | 3 | |
| Capstone Experience | ||
| Select one of the following courses Can be replaced by one course from the Advanced Topics and Applications group | 3 | |
| Master's Project | 1-6 | |
| Special Research Problems | 1-6 | |
| Master's Thesis | 1-15 | |
| Independent Study | 1-3 | |
| Special Research Problems | 3 | |
| Independent Study | 1-3 | |
| Master's Technical Report | 3 | |
| Special Research Programs | 1-3 | |
| Special Research Problems | 1-3 | |
Internship | ||
| Total Credits | 30 | |
A Suggested Plan of Study
Additional classes may be needed based on placement test results and course prerequisites. Visit with an advisor for help with creating a customized plan.
| First Year | ||
|---|---|---|
| Fall | Credits | |
| A ST 511 | Statistical Methods for Data Analytics | 3 |
| CSCI 4520 | Python Programming I | 3 |
| CSCI 5415 | Introduction to Data Mining | 3 |
| Credits | 9 | |
| Spring | ||
| CSCI 5420 | Applied Machine Learning I | 3 |
| CSCI 4530 | R Programming I | 3 |
| One Elective Course from the list of Advanced Topics and Applications courses | 3 | |
| Credits | 9 | |
| Second Year | ||
| Fall | ||
| Two Elective Courses from the list of Advanced Topics and Applications courses | 6 | |
| CSCI 5140 or ICT 458 | Database Management Systems I or Web Development and Database Applications | 3 |
| Credits | 9 | |
| Spring | ||
| One from the Capstone experience group or one course from Advanced Topics and Applications | 3 | |
| Credits | 3 | |
| Total Credits | 30 | |