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# Mathematics and Data Science

## Analyze, Model, and Interpret

Data scientists are experts at analyzing, modeling, and interpreting complex data. This interdisciplinary program provides fundamental training in mathematics, data science, computer science, and statistics to start a career in the field. Data scientists are in high-demand, have excellent starting salaries, and can work in a variety of areas.

**RELATED MAJORS**

**AREAS OF STUDY**

- Computational Mathematics
- Data Science
- Differential Equations
- Linear Algebra
- Mathematical Modeling
- Optimization
- Probability and Statistics

**CAREER OPTIONS**

- Actuary
- Data Scientist
- Financial Analyst
- Operations Research Analyst
- Quantitative Analyst
- Software Engineer
- Statistician

**GRADUATE PROGRAMS**

- Actuarial Science
- Computer Science
- Data Science
- Mathematics / Applied Mathematics
- MBA
- Operations Research
- Statistics

**What’s special about this program?**

Data scientists are valuable assets in any discipline and able to apply current technological tools to assimilate data, perform analysis, and express results. Students will communicate results to quantitative specialists and wider audiences and demonstrate an ability to apply mathematics to real world situations.

**Get Involved**

Undergraduate Research

Honors Program

Math Club

Putnam Competition

Mathematical Contest in Modeling

**Sample curriculum**

FALL | SPRING |
---|---|

MATH 210 - Discrete Mathematics I | MATH 219 - Data Science I |

MATH 241 - Analytic Geometry and Calculus A | MATH 242 - Analytic Geometry and Calculus B |

CISC 106 - General Computer Science for Engineers | MATH 315 - Discrete Mathematics II |

Breadth Requirement (Group A) (1/6) | CISC 210 - Introduction to Systems Programming |

UNIV 101 - First Year Experience I | ENGL 110 - First-Year Writing |

Credits: 14 | Credits: 16 |

FALL | SPRING |
---|---|

MATH 243 - Analytic Geometry and Calculus C | MATH 350 - Probability Theory and Simulation Methods |

MATH 349 - Elementary Linear Algebra | CISC 320 - Introduction to Algorithms |

CISC 220 - Data Structures | Ethics Requirement (may count as Group A) |

Laboratory Science Requirement (1/2) | Laboratory Science Requirement (2/2) |

Breadth Requirement (Group B) (2/6) | Breadth Requirement (Group C) (3/6) |

Credits: 17 | Credits: 16 |

FALL | SPRING |
---|---|

MATH 426 - Computational Mathematics I | MATH 428 - Computational Mathematics II |

MATH 450 - Mathematical Statistics | CISC 437 - Database Systems |

MATH/CISC/STAT Requirement (1/3) | Breadth Requirement (Group B) (5/6) |

Breadth Requirement (Group A) (4/6) | Free Elective (1/6) |

Multicultural Requirement | Free Elective (2/6) |

Credits: 15 | Credits: 15 |

FALL | SPRING |
---|---|

MATH 419 - Data Science II | MATH/CISC/STAT Requirement (3/3) |

MATH 529 - Fundamentals of Optimization | Free Elective (3/6) |

Breadth Requirement (Group C) (6/6) | Free Elective (4/6) |

Discovery Learning Experience | Free Elective (5/6) |

MATH/CISC/STAT Requirement (2/3) | Free Elective (6/6) |

Credits: 16 | Credits: 15 |