Concentration in Data Science and Analytics
Students must earn a "C" or better in each course taken to fulfill a concentration requirement.
The following requirements for this concentration will apply to students entering in Fall 2024 or later. For students entering before Fall 2024, click here to see the old requirements.
Program Overview
Data science is a broad, interdisciplinary field, and data scientists may have particular expertise in statistics, in programming, or in understanding of problems and data structures in particular areas of study. Students concentrating in Data Science and Analytics at the Wilkes Honors College should manifest proficiency in all three areas, together with fluency or leadership in at least one of these.
In the Data Science and Analytics concentration, students will be expected to attain fluency in computational skills, and proficiency in both statistical knowledge and domain expertise. This track was developed in collaboration with faculty from the College of Engineering and Computer Science (COECS).
Advisory Board:
Dr. Andia Chaves-Fonnegra | Dr. Yaouen Fily | Dr. Terje Hill | Dr. Kevin Lanning | Dr. Warren McGovern | Prof. Annina Ruest
Courses indicated with a * are taken in the COECS and are typically available online.
A. Data literacy and quantitative reasoning (13 credits)
| Course | Title | Prerequisites | Credit |
|---|---|---|---|
| STA 2023 | Honors Introduction to Statistics | 3 | |
| COP 3076 | Honors Introduction to Data Science | STA 2023 | 3 |
| MAC 2311 | Honors Calculus with Analytic Geometry I | MAC 1147/ placement | 4 |
| MAD 2104 | Honors Discrete Mathematics | MAC 1105/ permission | 3 |
Recommended:
MAC 2312 Honors Calculus w/ Analytic Geometry II (Prerequisite MAC 2311): 4 credits
B. Foundations of computer programming (9-10 credits)
| Course | Title | Prerequisites | Credit |
|---|---|---|---|
|
COP 3035C or COP 2000 |
Introduction to Programming in Python or Honors Foundations of Programming |
3 | |
| CEN 3062C | Introduction to Software Design | COP 3035C | 3 |
| COP 3410C | Data Structures and Algorithim Analysis with Python | COP 3035C and MAD 2104 | 3 |
C. Data proficiency (6 credits, choose two courses)
| Course | Title | Prerequisites | Credits |
|---|---|---|---|
| CEN 4400 | Introduction to Computer Systems Performance Evaluation | COP 3014 and STA 2023 | 3 |
| CAP 4613 | Introduction to Deep Learning | COP 3410C or COP 3530 | 3 |
| CAP 4630 | Introduction to Artificial Intelligence | COP 3410C or COP 3530 | 3 |
| CAP 4770 | Introduction to Data Mining and Machine Learning | (COP 3410C or COP 3530) and STA 2023 | 3 |
| COP 3540 | Introduction to Database Structures | COP 3410C or COP 3530 | 3 |
| COP 3275C | Systems Programming with C++ | COP 3035C and CEN 3062C | 3 |
| COP 3834 | Introduction to Web Programming | COP 3410C or CEN 3062C | 3 |
| COP 4045 | Python Programming | COP 3410C or COP 3530 | 3 |
| COP 4703 | Advanced Database Systems | COP 3540 | 3 |
D. Ethics and Technology Studies (3-4 credits, choose one course)
| Course | Title | Credits |
|---|---|---|
| ANT 4930 | Honors Digital Ethnography | 3 |
| ART 4640 | Honors Game Studies | 4 |
| PHI 2642 | Honors Ethics of Social Diversity | 3 |
| PHI 3633 | Honors Biomedical Ethics | 3 |
| PHI 3653 | Honors Ethics in Business, Government and Society | 3 |
| PHI 3682 | Honors Environmental Philosophy | 3 |
| PHI 3692 | Honors Artificial Intelligence Ethics | 3 |
| PHI 4930 | Honors Philosophy of Video Games | 3 |
| POS 3626 | Honors Privacy | 3 |
| SYD 4792 | Honors Race, Gender, Class, Sexuality and Science | 3 |
| SYP 4803 | Honors Gender and Technology | 3 |
E. Electives (6-8 credits, choose two courses)
| Course | Title | Credits |
| ART 3657C | Honors Introduction to Programming for Visual Arts | 4 |
| ART 4645C | Honors Electronics and Electronic Objects for Art | 4 |
| ART 4653C | Honors 3D Computer Game Development | 4 |
| ART 4658C | Honors 2D Computer Game Development | 4 |
| BSC 3452C | Honors Experimental Design and Data Analysis | 3 |
| CHM 3121/L | Honors Quantitative Analysis/Lab | 4 |
| ECO 4412 | Honors Econometrics: Applied regression Analysis | 3 |
| EXP 3604 | Honors Cognition | 3 |
| EXP 4631 | Honors Thinking and Decision Making | 3 |
| GIS 3044C | Honors Geographic Information Systems | 3 |
| IDS 3930/QMB 3302 | Honors Excel/Data Management and Analysis with Excel | 3 |
| IDS 3932 | Honors Empirical Analysis of Investments/Financial Markets | 3 |
| IDS 3932 | Honors Beginner's Programming for Biologists | 3 |
| ISS 4304 | Honors Computational Social Science | 3 |
| MAC 2313 | Honors Calculus 3 | 4 |
| MAP 2302 | Honors Differential Equations | 3 |
| MAS 2103 | Honors Matrix Theory | 3 |
| MAT 4930 | Honors Introduction to Computational Science | 3 |
| PHY 4523 | Honors Statistical Physics | 3 |
| PSY 3213/L | Honors Research Methods in Psychology/Lab | 4 |
| STA 3164 | Honors Intermediate Statistics (or any upper-level course with STA prefix) | 3 |
F. Additional Course (3-4 credits)
Choose one additional course from either group C, D, or E.
Honors thesis (IDS 4970, taken twice for a total of 6 credits)
Total credits: 46-50 credits
*Students are reminded that they need 42 upper-level (3000 or 4000-level) credits to graduate.