# 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:

- Bachelor’s degree in any field with a minimum of 12 hours of upper-division mathematics or statistics course work.
- Undergraduate GPA of at least 3.0 in upper-level Mathematics and/or Statistics courses.
- Official transcripts from each institution attended (must be submitted directly to UTRGV).
- 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 6330 | Linear Algebra | 3 |

MATH 6333 | Statistical Learning | 3 |

MATH 6364 | Statistical Methods | 3 |

MATH 6365 | Probability and Statistics | 3 |

CYBI 6303 | Principles of Information Technology Systems | 3 |

CYBI 6305 | Foundations of Algorithms and Programming Languages | 3 |

CSCI 6366 | Data 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:

CSCI 6333 | Advanced Database Design and Implementation | 3 |

CSCI 6350 | Advanced Artificial Intelligence | 3 |

CSCI 6352 | Advanced Machine Learning | 3 |

CSCI 6355 | Bioinformatics | 3 |

CSCI 6379 | Neural Networks and Deep Learning | 3 |

CYBI 6315 | Applied Database Systems | 3 |

CYBI 6375 | Data Science for Business Intelligence with Python | 3 |

CYBI 6378 | Statistics and Data Analysis with Python | 3 |

##### Statistics Courses – 3 Hours

Choose one from the following:

STAT 6336 | Advanced Sampling | 3 |

STAT 6379 | Stochastic Processes | 3 |

STAT 6380 | Time Series Analysis | 3 |

STAT 6381 | Mathematical Statistics | 3 |

STAT 6382 | Statistical Computing | 3 |

STAT 6383 | Experimental Design and Categorical Data | 3 |

STAT 6384 | Biostatistics | 3 |

##### Mathematics Courses – 3 Hours

Choose one from the following:

MATH 6340 | Computing for Mathematical and Data Sciences | 3 |

MATH 6352 | Analysis I | 3 |

MATH 6375 | Numerical Analysis | 3 |

MATH 6399 | Special Topics in Mathematics | 3 |

#### Capstone Requirement – 6 Hours

Students may complete one additional Prescribed Elective and a Masters Project course, one additional Prescribed Elective and a Internship course, OR they may complete two additional Prescribed Electives and a Comprehensive Exam.

##### Elective and Comprehensive Exam Option

Additional 6 hours of prescribed electives | ||

Written Comprehensive Exam |

##### Master Project Course Option

MATH 6391 | Master's Project | 3 |

Or | ||

STAT 6390 | Internship | 3 |

And | ||

Additional 3 hours of prescribed electives | 3 |

### Thesis Option

#### Prescribed Electives – 9 Hours

##### Computer Science Courses – 3 Hours

Choose one from the following:

CSCI 6333 | Advanced Database Design and Implementation | 3 |

CSCI 6350 | Advanced Artificial Intelligence | 3 |

CSCI 6352 | Advanced Machine Learning | 3 |

CSCI 6355 | Bioinformatics | 3 |

CSCI 6379 | Neural Networks and Deep Learning | 3 |

CYBI 6315 | Applied Database Systems | 3 |

CYBI 6375 | Data Science for Business Intelligence with Python | 3 |

CYBI 6378 | Statistics and Data Analysis with Python | 3 |

##### Statistics Courses – 3 Hours

Choose one from the following:

STAT 6336 | Advanced Sampling | 3 |

STAT 6379 | Stochastic Processes | 3 |

STAT 6380 | Time Series Analysis | 3 |

STAT 6381 | Mathematical Statistics | 3 |

STAT 6382 | Statistical Computing | 3 |

STAT 6383 | Experimental Design and Categorical Data | 3 |

STAT 6384 | Biostatistics | 3 |

##### Mathematics Courses – 3 Hours

Choose one from the following:

MATH 6340 | Computing for Mathematical and Data Sciences | 3 |

MATH 6352 | Analysis I | 3 |

MATH 6375 | Numerical Analysis | 3 |

MATH 6399 | Special Topics in Mathematics | 3 |

#### Thesis - 6 Hours

MATH 7300 | Thesis I | 3 |

MATH 7301 | Thesis II | 3 |

Total Credit Hours: 36