Data Science is a growing field that blends computer science, statistics, and information systems with domain knowledge to extract knowledge from data to solve real problems. As such, it provides foundational skills in statistical analysis, programming, and business. Students will gain deep analytic knowledge that will make them well qualified to assume roles such as business analysts, domain-specific managers, data mining analysts, business intelligence specialists, and managers of analytics in an organization. Data scientists are in high demand and the demand promises to continue.
The interdisciplinary program consists of fifteen (15) courses providing students the analytical, and computational skills required within the domain of data science.
Requirements for the Major
Requirements for a major and an example of a typical program of courses are as follows:
Foundational Component (12 required courses)
- CS 151 - Computer Science through Programming
- CS 295 - Discrete Structures or MA 295 - Discrete Structures or MA 395 - Discrete Methods
- DS 303 - Discovering Information in Data
- DS 496 - Ethical Data Science Capstone
- IS 251 - Data Analytics and Information Systems or BH 251 - Data Analytics and Information Systems *
- IS 353 - Data Management and Database Systems or CS 485 - Database Management Systems
- IS 358 - Business Intelligence and Data Mining
- MA 251 - Calculus I
- ST 210 - Introduction to Statistics or ST 265 - Biostatistics or EC 220 - Business Statistics
- ST 310 - Statistical Computing
- ST 465 - Experimental Research Methods
- ST 472 - Applied Multivariate Analysis
* Restricted to Sellinger Scholars
Choose three courses from the following:
- CS 312 - Object-Oriented Software Design
- CS 456 - Web Programming or IS 458 - Web-Enabled Entrepreneurial Project
- CS 484 - Artificial Intelligence
- CS 487 - Big Data
- EC 420 - Econometrics
- EC 425 - Applied Economic Forecasting
- IS 453 - Information Systems Analysis and Design
- IS 460 - Data Visualization
- MA 301 - Introduction to Linear Algebra
- MA 481 - Operations Research
- MA 485 - Stochastic Processes
- MK 415 - Digital Marketing and Analytics
- ST 461 - Elements of Statistical Theory I: Distributions
- ST 466 - Experimental Design
- ST 473 - Statistical Learning and Big Data
- ST 475 - Survival Analysis and Generalized Linear Models
- Appropriate course as approved by program director (Eg GIS)
For course descriptions, please see the Undergraduate Academic Catalog.
Sample Four-Year Undergraduate Curriculum
CS 151 - Computer Science through Programming
MA 251 - Calculus I
Social Science Core (EC 102 recommended)
ST 210 - Introduction to Statistics or ST 265 - Biostatistics or EC 220 - Business Statistics
IS 251 - Data Analytics and Information Systems or BH 251 - Data Analytics and Information Systems
Social Science Core (EC 103 recommended)
CS 295 - Discrete Structures or MA 295 - Discrete Structures or MA 395 - Discrete Methods
DS 303/MA 303/PH 303/CS 402 - Discovering Information in Data
IS 353 - Data Management and Database Systems or CS 485 - Database Management Systems
ST 310 - Statistical Computing
IS 358 - Business Intelligence and Data Mining
ST 465 - Experimental Research Methods (offered every other year)
ST 472 - Applied Multivariate Analysis
DS 496 - Ethical Data Science Capstone
EC 102 and EC 103 are recommended to fulfill the Social Science Core.
MA 151 may be substituted for MA 251 with permission of the program director.
CS 212 is a prerequisite for CS 312. MA 351 is a prerequisite for ST 461.
Up to two graduate-level Data Science courses may be taken as a DS Elective with approval from the program director.
CS 485 and ST 465 are offered every other year.
The Data Science major cannot be combined with a minor in Computer Science, Information Systems, or Statistics. Students may double major with these programs.
An internship as DS 499 may be taken for university credit, but does not count as an elective in the BS DS. It is not anticipated that this course would substitute for the capstone as the ethical component in the capstone is particularly important. This aspect would not necessarily be present in the internship. Instead it is expected that the internship experience may enhance the capstone experience for the student by providing the relationship from which the student can develop the capstone project.