Applied Statistics and Data Science (MS)

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

Required Courses - 21 Hours

MATH 6330Linear Algebra

3

MATH 6333Statistical Learning

3

MATH 6364Statistical Methods

3

MATH 6365Probability and Statistics

3

CYBI 6302Foundations of Software and Programming Systems

3

CYBI 6305Foundations of Algorithms and Programming Languages

3

CSCI 6366Data Mining and Warehousing

3

Non-thesis option

Prescribed Electives - 9 Hours

This degree plan includes courses that appear in more than one section of the degree plan. Such courses can only be used to fulfill one requirement in the degree plan, and credit hours will only be applied once. 

Computer Science Courses – 3 Hours

Choose one from the following:

CYBI 6315Applied Database Systems

3

CSCI 6333Advanced Database Design and Implementation

3

CSCI 6350Advanced Artificial Intelligence

3

CSCI 6352Advanced Machine Learning

3

CSCI 6355Bioinformatics

3

Statistics Courses – 3 Hours

Choose one from the following:

STAT 6336Advanced Sampling

3

STAT 6379Stochastic Processes

3

STAT 6380Time Series Analysis

3

STAT 6381Mathematical Statistics

3

STAT 6382Statistical Computing

3

STAT 6383Experimental Design and Categorical Data

3

STAT 6384Biostatistics

3

Mathematics Courses – 3 Hours

Choose one from the following:

MATH 6352Analysis I

3

MATH 6375Numerical Analysis

3

Capstone Requirement – 6 Hours

Students may complete one additional Prescribed Elective and one course in the Masters Project courses OR they may complete two additional Prescribed Electives and a Comprehensive Exam. 

Elective and Comprehensive Exam Option
Additional 6 hours of electives

Written Comprehensive Exam

Master Project Course Option
 
MATH 6391Master's Project

3

Or

STAT 6390Internship

3

And

Additional 3 hours of electives

3

Thesis Option

Prescribed Electives – 9 Hours

Computer Science Courses – 3 Hours

Choose one from the following:

CYBI 6315Applied Database Systems

3

CSCI 6333Advanced Database Design and Implementation

3

CSCI 6350Advanced Artificial Intelligence

3

CSCI 6352Advanced Machine Learning

3

CSCI 6355Bioinformatics

3

Statistics Courses – 3 Hours

Choose one from the following:

STAT 6336Advanced Sampling

3

STAT 6379Stochastic Processes

3

STAT 6380Time Series Analysis

3

STAT 6381Mathematical Statistics

3

STAT 6382Statistical Computing

3

STAT 6383Experimental Design and Categorical Data

3

STAT 6384Biostatistics

3

Mathematics Courses – 3 Hours

Choose one from the following:

MATH 6352Analysis I

3

MATH 6375Numerical Analysis

3

Thesis - 6 Hours

MATH 7300Thesis I

3

MATH 7301Thesis II

3

Total Credit Hours: 36