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Applied Statistics and Data Science (MS) - Online Program

CIP Code

27.0503.00

Overview

Graduates of the Master of Science (MS) in Applied Statistics and Data Science will be trained in the data science process, machine learning, data visualization, statistical inference, algorithmic and computational thinking, experimental design, coding, ethics, and algorithmic accountability. Moreover, they will acquire competency in the following areas.

  • Computational and statistical thinking.
  • Mathematical foundations.
  • Algorithms and software foundation.
  • Data curation.
  • Knowledge transference—communication and responsibility.

Admission Requirements

To be admitted to the graduate program in mathematics, prospective candidates must first meet all requirements for graduate admission to UT Rio Grande Valley, as well as the other requirements listed below:

  1. Bachelor’s degree in any field with a minimum of 12 hours of upper-division mathematics or statistics course work.
  2. Undergraduate GPA of at least 3.0 in upper-level mathematics and/or statistics courses.
  3. Official transcripts from each institution attended (must be submitted directly to UTRGV).
  4. Letter of intent detailing professional goals and reasons for pursuing the graduate degree.

Application for admission must be submitted prior to the published deadline. The application is available at www.utrgv.edu/gradapply.

Program Requirements

Students in the MS in Applied Statistics and Data Science complete 36 hours for the degree. The program includes a thesis and a non-thesis option.  

Required Courses

Core Courses – 21 Hours

CourseCourse Name
MATH 6330Linear Algebra
MATH 6333Statistical Learning
MATH 6364Statistical Methods
MATH 6365Probability and Statistics
CSCI 6366Data Mining and Warehousing
CYBI 6304Foundations of Informatics & Cyberspace I
CYBI 6306Foundations of Informatics & Cyberspace II

Prescribed Electives

Computer Science Electives – 3 hours

CourseCourse Name
CSCI 6333Database Design and Implementation
CSCI 6344Introduction to Data Science
CSCI 6353Reinforcement Learning
CSCI 6355Bioinformatics
CSCI 6379Deep Learning
CYBI 6315Applied Database Systems
CYBI 6375Data Science for Business Intelligence with Python
CYBI 6378Statistics and Data Analysis with Python

Statistics Electives – 3 hours

CourseCourse Name
STAT 6336Advanced Sampling
STAT 6379Stochastic Processes
STAT 6380Time Series Analysis
STAT 6381Mathematical Statistics
STAT 6382Statistical Computing
STAT 6383Experimental Design and Categorical Data
STAT 6384Biostatistics
STAT 6391Topics in Statistics
STAT 6392Directed Study

Mathematics Electives – 3 hours

CourseCourse Name
MATH 6340Computing for Mathematical and Data Sciences
MATH 6352Analysis I
MATH 6375Numerical Analysis
MATH 6396Topics in Applied Mathematics
MATH 6399Special Topics in Mathematics

Non-Thesis Option

Students on the non-thesis option may complete: 1) a three-credit hour master’s project and one additional prescribed elective, 2) an internship and one additional prescribed elective, or 3) they must choose to take two additional prescribed electives with a comprehensive exam.  

Project or Internship Option – 6 hours

Complete a master’s project or internship and one additional prescribed elective.  

CourseCourse Name
MATH 6391Master's Project
Or
STAT 6390Internship

Additional Electives Option – 6 hours

Complete any two 6000-level courses in the School of Mathematical and Statistical Sciences not fulfilling other program requirements and a written comprehensive exam.  

Thesis Option

Thesis Courses – 6 hours

CourseCourse Name
MATH 7300Thesis I
MATH 7301Thesis II

Total Credit Hours: 36