Data Science (DSCI)
College of Arts and Sciences, Interdisciplinary Program
Director: Berg (CISC)
Advisory Committee: Curran (CISC), Dwyer (CISC), McNamara (CISC), Shemyakin (MATH), Werness (CISC)
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and actionable insights from noisy, structured, or unstructured data. Data scientists use techniques and theories drawn from many fields within the context of mathematics, statistics, and computer science.
Major in Data Science (B.S.)
56 credits
Data Science is a foundation for many different data-focused careers, including Data Scientist, Machine Learning Engineer, Data Engineer, Statistician, Data Analyst, and Infrastructure Architect.
Required Courses:
- STAT 120 Introduction to Data Sciences (4 credits)
- STAT 220 Introductory Statistics (4 credits)
- STAT 320 Applied Regression Analysis (4 credits)
- STAT 336 Data Communication and Visualization (4 credits)
- STAT 360 Computational Statistics and Data Analysis (4 credits)
- STAT 400 Data Mining and Machine Learning (4 credits)
- STAT 460 Statistical Practicum (4 credits)
* Note: A grade of C- or above must be earned by majors in these required courses
Plus 8 credits from the following:
- STAT 310 Biostatistics (4 credits)
- STAT 370 Bayesian Statistical Models (4 credits)
- STAT 380 Spatial Statistics (4 credits)
- STAT 413 Generalized Linear Mixed Models (4 credits)
- STAT 414 Network Models and Simulations (4 credits)
- CISC 451 Database II (4 credits)
Allied Requirements:
- MATH 113 Calculus I (4 credits) or MATH 109 Calculus with Review II (4 credits)
- MATH 128 Introduction to Discrete Mathematics (4 credits)
- CISC 131 Introduction to Programming and Problem Solving (4 credits)
or CISC 130 Introduction to Programming and Problem Solving in the Sciences (4 credits) - CISC 230 Object Oriented Design and Programming (4 credits)
- CISC 450 Database I (4 credits)
Minor in Data Science
24 credits
A minor in Data Science allows students to pursue an interest in fundamental concepts critical for data-driven research and careers.
Required courses:
- STAT 120 Introduction to Data Sciences (4 credits)
- STAT 220 Introductory Statistics (4 credits)
- STAT 320 Applied Regression Analysis (4 credits)
Plus 8 credits from the following:
- STAT 310 Biostatistics (4 credits)
- STAT 336 Data Communication and Visualization (4 credits)
- STAT 360 Computational Statistics and Data Analysis (4 credits)
- STAT 380 Spatial Statistics (4 credits)
- STAT 400 Data Mining and Machine Learning (4 credits)
- CISC 450 Database I (4 credits)
Allied Requirements:
- CISC 131 Introduction to Programming and Problem Solving (4 credits)
or CISC 130 Introduction to Programming and Problem Solving in the Sciences (4 credits)