# PhD in Data Science

Data science is an emerging discipline that combines mathematics, computing and statistics to develop and apply methodologies required for data-driven industries. There is a high demand for data science professionals in many industries including technology, government, utilities and banking.

The department of Mathematical Sciences offers a PhD program in Data Science to prepare and train individuals who can immediately obtain positions in industry using data to guide decision-making. The program is interdisciplinary and open to individuals from many backgrounds.

Each student's program will be designed to meet individual interests and goals.

## Application Deadlines

### Deadlines for Spring admission:

- The department begins to review applications and make decisions about teaching assistant awards for Spring admission after the following deadline:
**October 1**for International and domestic applicants.

### Deadlines for Fall admission:

- The department begins to review applications and make decisions about teaching assistant awards for Fall admission after the following deadline:
**February 1**for International and domestic applicants.

**For both semesters, there may be special cases after the deadlines that will merit review for admission and funding.**

## Admission Requirements

Entry into the program requires Calculus I, II, and III (MATH 1411, MATH 1312, and MATH 2313), Matrix Algebra (MATH 3323), Principles of Mathematics (MATH 3325), Introduction to Analysis (MATH 3341), Probability (STAT 3330), and Statistics Inference (STAT 4380) or the equivalent of these courses.

In addition, it is recommended that the student possesses a good working knowledge of a high level computer language such as C++, JAVA, SAS, R (Splus), Python, or Matlab.

A complete application to the graduate program requires official transcripts, a statement of purpose, GRE scores, and 3 letters of recommendation. This application packet is reviewed for admission and funding for Teaching Assistantships by the program's faculty committee. A separate application for a Teaching or Research Assistantship is not needed.

A minimum TOEFL score of 213 (550 or higher on paper based TOEFL; minimum score of 6.5 on the IELTS; minimum score of 79 on the IBT - Internet Based - TOEFL) is required for international applicants whose first language is not English or who have not completed a university degree in the U.S. or other English-Speaking institution.

Applicants who do not have all the prerequisite coursework for full admission into the Data Science PhD program may be provisionally admitted and will be required to complete the leveling courses after entry into the program.

## Degree Requirements

The required number of credit hours depends on the candidate's previous course history and will be evaluated by the Director of the Ph.D. program. The general degree plan requirements, pending evaluation of previously taken courses, are:

Course Types |
SCH (Bachelor's) |
SCH (Master's) |

Core | 16 | 0-16 |

Prescribed Electives | 12 | 6-12 |

Statistical Data Science Theory | 3 | 0-3 |

Statistical Data Science Applications | 3 | 0-3 |

Mathematical Applications | 3 | 0-3 |

Computing | 3 | 0-3 |

Free Electives | 12 | 0 |

Research (Collaborations) | 21 | 21 |

Dissertation | 6 | 6 |

TOTAL: |
67 |
33-55 |

All students in the program will be expected to complete and take qualifying exams for the following core required graduate courses:

- DS 5380 and DS 5381: Mathematical Foundations of Data Science I and II
- DS 5474: Introduction to Data Mining
- MATH 5330: Computational Methods of Linear Algebra
- STAT 5385: Statistics in Research

The remaining courses will usually be selected from the list below:

- CS 5322: Topics in Advanced Database Systems
- CS 5334: Parallel and Concurrent Programming
- CS 5350: Advanced Algorithms
- CS 5361: Machine Learning
- CS 5362: Data Mining
- DS 5339: Data Visualization
- DS 5494: Statistical Data Mining
- DS 6335: Introduction to Data Science Collaborations (SC Lab)
- DS 6336: Mathematical Applications in Data Science
- DS 6381: Advanced Inference
- DS 6382: Statistical Theory for Big Data
- DS 6384: Linear Models for Data Science
- DS 6386: Stochastic Differential Equations and Appl.
- DS 6388: Multivariate Stat. Met. High Dimensional Data
- DS 6390: Data Science Research Collaborative
- DS 6392: Advanced Computational Data Science
- MATH 5311: Topics in Applied Mathematics
- MATH 5329: Numerical Analysis
- MATH 5335: Techniques in Optimization
- MATH 5343: Numerical Solutions to Partial Diff. Eq.
- MATH 6321: Measure and Probability Theory
- STAT 5329: Statistical Programming
- STAT 5331: Sampling Theory
- STAT 5335: Experimental Design
- STAT 5336: Categorical Data Analysis
- STAT 5354: Post Genomic Analysis
- STAT 5370: Special Topics
- STAT 5386: Stochastic Processes
- STAT 5388: Multivariate Data Analysis
- STAT 5390: Non-parametric Statistics
- STAT 5391: Time Series Analysis
- STAT 5392: Statistical Computing
- STAT 5393: Survival Analysis
- STAT 5397: Longitudinal Data Analysis
- STAT 5428: Introduction to Statistical Analysis

For course description search the UTEP's *Course Catalog. *

Apply on the Graduate School website, specifying the semester you want to start the PhD degree in Data Science.

Accepted applicants are eligible and will be considered for Teaching or Research Assistantships. IF funded, applicants will receive an offer letter for either a Teaching Assistant (TA) or Research Assistant (RA) position. These positions provide in-state tuition.

*For the most current information on degrees offered and their requirements, please visit the**Mathematical Sciences section**of UTEP's**Graduate Catalog. *