Applied Statistics and Data Science (MS)

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

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

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 (Choose one)

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 (Choose one)

MATH 6336Advanced Sampling

3

MATH 6379Stochastic Processes

3

MATH 6380Time Series Analysis

3

MATH 6381Mathematical Statistics

3

MATH 6382Statistical Computing

3

MATH 6383Experimental Design and Categorical Data

3

MATH 6384Biostatistics

3

Mathematics Courses (Choose one)

MATH 6352Analysis I

3

MATH 6375Numerical Analysis

3

Capstone Requirement

Choose one of the following options:

Thesis Option - 6 Hours

MATH 7300Thesis I

3

MATH 7301Thesis II

3

Master Project Option - 6 Hours

Choose-one-of-the-following
MATH 6390Internship

3

MATH 6391Master's Project

3

Choose-one-of-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

MATH 6336Advanced Sampling

3

MATH 6352Analysis I

3

MATH 6375Numerical Analysis

3

MATH 6379Stochastic Processes

3

MATH 6380Time Series Analysis

3

MATH 6381Mathematical Statistics

3

MATH 6382Statistical Computing

3

MATH 6383Experimental Design and Categorical Data

3

MATH 6384Biostatistics

3

Non-Thesis Option (Comprehensive Exam) - 6 Hours

Choose-two-of-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

MATH 6336Advanced Sampling

3

MATH 6352Analysis I

3

MATH 6375Numerical Analysis

3

MATH 6379Stochastic Processes

3

MATH 6380Time Series Analysis

3

MATH 6381Mathematical Statistics

3

MATH 6382Statistical Computing

3

MATH 6383Experimental Design and Categorical Data

3

MATH 6384Biostatistics

3

Total Credit Hours: 36