The B.S. in Data Science is an interdisciplinary program supported by the Department of Computer Science and the Department of Mathematics & Statistics. The curriculum is modeled upon guidelines for undergraduate programs in Data Science as endorsed by the American Statistical Association's Board of Directors.

## Learning Outcomes

Graduates of this program will be able to: |
---|

1. Use programming and other computer science skills to analyze and interact with data, |

2. Apply statistics to analyze data sets, |

3. Acquire and manage complex data sets, |

4. Use technical skills in predictive modeling, |

5. Visualize data to facilitate the effective presentation of data-driven insights. |

## Degree Requirements

Computer Science Requirements | 20 | |
---|---|---|

Course | Title | Hours |

CSCI 1070 | Taming Big Data | 3 |

CSCI 1300 | Introduction to Object-Oriented Programming | 4 |

CSCI 2100 | Data Structures | 4 |

CSCI 2300 | Object-Oriented Software Design | 3 |

CSCI 3710 | Databases | 3 |

CSCI 4750 | Machine Learning | 3 |

Mathematics/Statistics Requirements | 27 | |

Course | Title | Hours |

MATH 1510 | Calculus I | 4 |

MATH 1520 | Calculus II | 4 |

MATH 1660 | Discrete Mathematics | 3 |

MATH 2530 | Calculus III | 4 |

MATH 3110 (or 3120) |
Linear Algebra | 3 |

STAT 3850 | Foundations of Statistics | 3 |

STAT 4870 | Applied Regression | 3 |

STAT 4880 | Bayesian Statistics | 3 |

Data Science Integration Requirements | 6 | |

Course | Title | Hours |

DATA 1800 | Practicium I | 1 |

DATA 2800 | Practicum II | 1 |

DATA 4961 | Capstone I | 2 |

DATA 4962 | Capstone II | 2 |

Additional Electives: Three choices must include at least one CSCI course and at least one STAT course | 9 | |

Course | Title | Hours |

DATA 4930 | Topics in Data Science | 3 |

CSCI 3100 | Algorithms | 3 |

CSCI 3300 | Software Engineering | 3 |

CSCI 4850 | High-Performance Computing | 3 |

STAT 4800 | Theory of Probability | 3 |

STAT 4840 | Time Series | 3 |

STAT 4850 | Mathematical Statistics | 3 |

STAT 4860 | Statistical Models | |

other | with approval | |

Additional College Core ^{1} | 41-53 | |

Course | Title | Hours |

Lab Science Sequence (must be sequence in a single science, chosen from BIO, CHEM, EAS, PHYS) |
8 | |

Foundations of Discourse | 3 | |

Literature | 3 | |

PHIL 2050 | Ethics | 3 |

Additional Philosophy (PHIL 3410 recommended) | 3 | |

Social Science | 6 | |

THEO 1000 THEO 2000+ |
Theology | 6 |

HIST 1110 HIST 1120 |
World History | 6 |

Fine and Performing Arts | 3 | |

Foreign Language | 0-6 | |

Diversity in the U.S. | 0-3 | |

Global Citizenship | 0-3 | |

Total | 103-115 |

** ^{1}Core requirements: **For precise details of the A&S core requirements for the BS, see the College website.

## Continuation Standards

After declaring a Data Science major, students must achieve a minimum GPA of 2.00 in required CSCI/MATH/STAT courses by the conclusion of their second year as a major, and maintain such a GPA at the conclusion of each semester thereafter. Furthermore, students should require at most two attempts to successfully complete any courses required for the major (where an unsuccessful attempt is considered a D or F for CSCI 1300/2100 and MATH 1510/1520, and an F in higher-level courses). Students are also expected to make adequate progress in the major, typically by enrolling in at least one relevant course per semester until completing their coursework (with exceptions made for premed scholars during their first year, and all students if studying abroad or facing other such extenuating circumstances).

## Sample Schedule

While students' schedules depend upon their interests, other possible majors/minors, incoming credits/experience, and consultation with faculty mentors, the following shows a sample of a typical four-year schedule completing the BS in Data Science.

Year One | |
---|---|

Fall | 16 |

CSCI 1070: Taming Big Data | 3 |

MATH 1510: Calculus I | 4 |

MATH 1660: Discrete Mathematics | 3 |

Core: Foreign Language 1010 | 3 |

Core: English 1900 or 1940 | 3 |

Spring | 15 |

CSCI 1300: Intro. to Object-Oriented Programming | 4 |

MATH 1520: Calculus II | 4 |

DATA 1800: Practicum I | 1 |

Core: Foreign Language 1020 | 3 |

Core: Theology 1000 | 3 |

Year Two | |

Fall | 15 |

CSCI 2100: Data Structures | 4 |

MATH 2530: Calculus III | 4 |

Science I with lab | 4 |

Core: Philosophy 2050 (Ethics) | 3 |

Spring | 14 |

CSCI 2300: Object-Oriented Software Design | 3 |

STAT 3850: Foundations of Statistical Analysis | 3 |

Science II with lab | 4 |

MATH 3110: Linear Algebra | 3 |

DATA 2800: Practicum II | 1 |

Year Three | |

Fall | 15 |

CSCI 3710: Databases | 3 |

MATH 4880: Bayesian Statistics | 3 |

Core: Philosophy (PHIL 3410 recommended) | 3 |

Core: History 1110 | 3 |

Core: Social Science | 3 |

Spring | 15 |

CSCI 4750: Machine Learning | 3 |

STAT 4870: Applied Regression | 3 |

Core: Fine and Performing Arts | 3 |

Core: History 1120 | 3 |

Core: Social Science | 3 |

Year Four | |

Fall | 17 |

DATA 4961: Capstone Project I | 2 |

CSCI/STAT Elective | 3 |

CSCI/STAT Elective | 3 |

Core: Linerature | 3 |

Core: Theology 2xxx | 3 |

Elective | 3 |

Spring | 14 |

DATA 4962: Capstone Project II | 2 |

CSCI/STAT Elective | 3 |

Core: Global Citizenship | 3 |

Core: Cultural Diversity in the U.S. | 3 |

Elective | 3 |