## B.S. in Data Science

The College of Arts and Sciences now offers a B.S. in data science. The video below gives an overview of our data science program:

### What is a Data Scientist?

A data scientist analyzes complex systems and solves real-world problems through the analysis of data, and in particular, very large sets of data. Many scientific disciplines, our economy, and even our providers of streaming entertainment increasingly rely on data. You’ll work with a variety of methods including predictive/prescriptive analytics, algorithm design and execution, applied machine learning, statistical modeling, and data visualization.

### Why Data Science?

Data science, and its associated fields, is one of the fastest growing employment opportunities in the world. A strong demand in the job market — combined with a shortage of people trained in data science —means you’ll have opportunities in all sectors of society.

According to the Education Advisory Board, some of the top occupations requiring data analysis skills are:

- Marketing managers
- Financial analysts
- Computer systems engineers/architects
- Business intelligence analysts
- Computer programmers

And, some top employers for data analytics-related skills are:

- Amazon.com
- UnitedHealth Group
- Microsoft Corporation
- JP Morgan Chase Company
- General Electric Company

The new bachelor of science in data science at Valpo is designed to give graduates the interdisciplinary skills that employers need. The data science program integrates statistics, mathematics, computer science, and data science to produce graduates with the skills needed to evaluate and interpret data. You will gain a broad skill set that will be attractive to employers in this thriving field.

#### Degree Requirements — Bachelor of Science in Data Science

In order to graduate with a bachelor of science in data science at Valpo, a minimum of 40 credit hours is required. Students must take courses in data science and from the partner disciplines of statistics, mathematics, and computer science. Additionally, students should explore an area of application through selection of one or more courses from an appropriate field, as described below. Students are strongly encouraged to take a minor or a second major in their applied field of interest.

Course |
Title |
Credits |

Statistics Courses (9-10 credits) |
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One course from the following options is required: |
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STAT 140 |
General Statistics | 3 Credits |

STAT 240 (*) |
Statistical Analysis | 3 Credits |

PSY 201 |
Statistical Methods | 3 Credits |

IDS 205 |
Business Statistics | 3 Credits |

(*) STAT 240 is the recommended option. | ||

One course from the following options is required: |
||

STAT 340 |
Statistics for Decision-Making | 3 Credits |

IDS 340 |
Statistics for Decision-Making | 3 Credits |

ECON 325 |
Econometrics | 3 Credits |

One course from the following options is required: |
||

STAT 343 |
Time Series Analysis | 3 Credits |

STAT 344 |
Stochastic Processes | 3 Credits |

STAT 441 |
Probability | 4 Credits |

Mathematics Courses (8-10 credits) |
||

The following courses are required: | ||

MATH 131 |
Calculus I | 4 Credits |

MATH 220 |
Discrete Mathematics | 3 Credits |

One course from the following options is required: |
||

MATH 260 |
Linear Systems and Matrices | 1 Credit |

MATH 264 |
Linear Algebra | 3 Credits |

Computer Science (11-12 credits) |
||

The following courses are required: | ||

CS 157 |
Algorithms and Programming | 3 Credits |

CS 158 |
Algorithms and Abstract Data Types | 3 Credits |

CS 350 |
Database Management Systems | 3 Credits |

One course from the following options is required: |
||

CS 225 |
Programming Languages | 2 Credits |

CS 325 |
Simulation and Modeling | 3 Credits |

CS 345 |
Artificial Intelligence | 3 Credits |

Data Science (12 credits) |
||

The following courses are required: | ||

DATA 151 |
Introduction to Data Science | 3 Credits |

DATA 433 |
Data Mining and Applications | 3 Credits |

DATA 299 |
Data Science Colloquium I | 1 Credit |

DATA 399 |
Data Science Colloquium II | 1 Credit |

DATA 499 |
Data Science Capstone | 1 Credit |

One course from the following options is required: |
||

DATA 374 |
Computational Linear Algebra | 3 Credits |

DATA 375 |
Scientific Visualization | 3 Credits |

DATA 490 |
Advanced Topics in Data Science | 3 Credits |

ECON 473 |
Applied Data Science | 3 Credits |

Application Area (1-3 credits) |
||

One course from the following options is required: |
||

BIO 321/MATH 321 |
Mathematical Models of Infectious Diseases | 3 Credits |

GEO/MET 460 |
Data Analysis | 3 Credits |

ECON 473 |
Applied Data Science (if not taken above) | 3 Credits |

PHYS 246 |
Data Reduction and Error Analysis | 1 Credit |

POLS 210 |
Research Methods in Political Science | 3 Credits |

PSY 370 |
Laboratory in Experimental Design and Analysis | 3 Credits |

*Additional courses may be approved by the program director.