- Concentrations and course offerings may vary by campus and/or by program modality. Please consult with your advisor or admissions coach for the course availability each term at your campus or within your program modality.
- Certain options within the program may be
*required*at certain campuses or for certain program modalities. Please consult with your advisor or admissions coach for requirements at your campus or for your program modality.

Complete all courses and requirements listed below unless otherwise indicated.

## Core Requirements

Code | Title | Hours |
---|---|---|

Modeling and Linear Algebra | ||

Complete 4 semester hours from the following: | 4 | |

Applied Linear Algebra and Matrix Analysis | ||

Algebra 1 | ||

Introduction to Mathematical Methods and Modeling | ||

Probability and Analysis | ||

Complete 4 semester hours from the following: | 4 | |

Analysis 1: Functions of One Variable | ||

Probability 1 | ||

Statistics | ||

Complete 8 semester hours from the following: | 8 | |

Machine Learning and Statistical Learning Theory 1 | ||

Mathematical Statistics | ||

Applied Statistics |

## Concentration or Electives Option

A concentration is not required. Students may complete the electives option in lieu of a concentration.

## Program Credit/GPA Requirements

32 total semester hours required

Minimum 3.000 GPA required

Data Science Concentration

Code | Title | Hours |
---|---|---|

No more than 8 semester hours of coursework outside of the MATH subject code may be applied to the requirements of this concentration. | ||

Core | ||

Complete 8 semester hours from the following. Students may take other Khoury College of Computer Sciences courses not on the list in consultation with their faculty advisor: | 8 | |

Algorithms | ||

Machine Learning | ||

Data Mining Techniques | ||

Collecting, Storing, and Retrieving Data | ||

Introduction to Data Mining/Machine Learning | ||

Supervised Machine Learning and Learning Theory | ||

Unsupervised Machine Learning and Data Mining | ||

Introduction to Machine Learning and Pattern Recognition | ||

Machine Learning and Statistical Learning Theory 1 | ||

Electives | ||

Complete 8 semester hours of courses at the 5000 level or above in the following subject area. See suggested elective course list. | 8 | |

MATH |

** **Electives Option

Code | Title | Hours |
---|---|---|

Complete 16 semester hours in the following subject area. Students may take MATH courses at the 5000 level or above listed in other concentrations or the suggested elective course list. Courses outside of MATH may be chosen with faculty approval. | 16 | |

No more than 8 semester hours of coursework outside of the MATH subject code may be applied to requirements of this option. | ||

MATH |

## Suggested Elective Course List

Code | Title | Hours |
---|---|---|

Students may complete other MATH courses not listed below and courses outside of MATH in consultation with their faculty advisor: | ||

Introduction to Data Management and Processing | ||

Fundamentals of Computer Engineering | ||

Quantum Computation and Information | ||

Numerical Analysis 1 | ||

Numerical Analysis 2 | ||

Riemannian Optimization | ||

Graph Theory | ||

Optimization and Complexity | ||

Machine Learning and Statistical Learning Theory 2 | ||

Probability 2 | ||

Mathematical Statistics | ||

Regression, ANOVA, and Design | ||

Additional Courses | ||

The following are some theoretical MATH courses usually taken in the PhD program (these may not be offered every academic year): | ||

Analysis 2: Functions of Several Variables | ||

Algebra 2 | ||

Topology 1 | ||

Partial Differential Equations 1 | ||

Topology 2 | ||

Commutative Algebra | ||

Algebraic Number Theory | ||

Modern Algebraic Geometry | ||

Morse Theory | ||

Topics in Combinatorics | ||

Topics in Probability | ||

Readings in Graph Theory | ||

Readings in Probability and Statistics | ||

Research Seminar in Mathematics |