Data Science and Analytics at 吃瓜网 is about turning data into insight. You鈥檒l learn to use statistics, programming, and domain knowledge to solve real-world problems鈥攚hether you鈥檙e interested in technology, business, health, or the environment. This area of study prepares you to analyze complex data, build intelligent systems, and make decisions that drive progress.
About Data Science and Analytics at FAU
At 吃瓜网 Atlantic, you鈥檒l study Data Science and Analytics in a collaborative, interdisciplinary environment. The program is designed with input from both the Wilkes Honors College and the College of Engineering and Computer Science, giving you access to expert faculty and hands-on learning. You鈥檒l build skills in statistics, programming, and data-driven problem-solving, and you can tailor your experience with electives in areas like artificial intelligence, cybersecurity, and digital ethics. The program emphasizes both technical fluency and ethical leadership, preparing you to lead in a rapidly changing field.
Degrees Offered at 吃瓜网 Atlantic
Undergraduate
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Bachelor of Science, Concentration in Data Science and Analytics (Wilkes Honors College)
Educational Objectives & Student Outcomes
2. Build the ability to analyze, interpret, and communicate data-driven insights.
3. Foster ethical awareness and leadership in the use of data and technology.
4. Prepare for advanced study or careers in data science, analytics, and related fields.
2. Design and implement software solutions for data analysis.
3. Communicate findings clearly to both technical and non-technical audiences.
4. Demonstrate ethical reasoning in data-driven decision-making.
5. Collaborate effectively in interdisciplinary teams.
at 吃瓜网 AtlanticStart Your Journey
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.
Overview and Advisory Board
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.
This track was developed in collaboration with faculty from the College of Engineering and Computer Science (COECS).
Courses indicated with a * are taken in the COECS and are typically available online.
Advisory Board
Andia Chaves Fonnegra, Ph.D.
Associate Professor of Biology
Harriet L. Wilkes Honors College
Biology
Erik Dubou茅, Ph.D.
Associate Professor of Biology
Harriet L. Wilkes Honors College
Biology
Yaouen Fily, Ph.D.
Associate Professor of Physics
Harriet L. Wilkes Honors College
Physics
Jason Hedetniemi, Ph.D.
Senior Instructor of Mathematics
Harriet L. Wilkes Honors College
Mathematics
Terje Hill, Ph.D.
Professor of Mathematics
Harriet L. Wilkes Honors College
Mathematics
Kevin Lanning, Ph.D.
Professor of Psychology and Data Science
Harriet L. Wilkes Honors College
Psychology
Warren McGovern, Ph.D.
Professor of Mathematics
Harriet L. Wilkes Honors College
Mathematics
Annina Ruest, MFA
Professor of Art
Harriet L. Wilkes Honors College
Art
A. Data Literacy and Quantitative Reasoning (13 credits)
| Course | Title | Prerequisites | Credit |
|---|---|---|---|
| STA 2023 | Honors Introduction to Statistics | 听 | 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 |
B. Foundations of Computer Programming (9-12 credits)
| Course | Title | Prerequisites | Credit |
|---|---|---|---|
|
COP 3035C or COP 2000 |
Introduction to Programming in Python or Honors Foundations of Programming |
听 | 3 |
| CEN 3062C | Introduction to Software Design | COP 3035C | 3 |
| One of the options (a) or (b) below: | 听 | 听 | 听 |
| (a) COP 3410C | Data Structures and Algorithm Analysis with Python | COP 3035C and MAD 2104 | 3 |
|
(b) COP 3275C听 听 听 听 听 听 听 听 听 and COP 3530C |
Systems Programming with C++ and Data Structures and Algorithm Analysis |
COP 3035C听 听 听 听 听 听 听 听 听 听 听 听 COP 3275C and MAD 2104 |
3听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听听 3 |
C. Data, Software, and Intelligent Systems (6 credits, choose two courses)
| Course | Title | Prerequisites | Credits |
|---|---|---|---|
| CAI 4223 | Introduction to Large Language Models | CAP 4770 and (COP 3410C or COP 3530C) |
3 |
| CAI 4304 | Introduction to Natural Language Processing | COP 3410C or COP 3530C | 3 |
| CAI 4802 | Artificial Intelligence for Cybersecurity | CAP 4773 and (COP 3410C or COP 3530C) |
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 |
| CAP 4773 | Introduction to Data Science and Analytics | COP 3035C and STA 2023 | 3 |
| CDA 3203 | Computer Logic Design | 听 | 3 |
| CDA 4102 | Computer Architecture | CDA 3203 and COP 3275C | 3 |
| CEN 4010 | Principles of Software Engineering | COP 3410C or COP 3530C | 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 4331 | Object-Oriented Design and Programming |
COP 3410C or COP 3530C | 3 |
| COP 4610 | Computer Operating Systems | CDA 4102 and COP 3530C | 3 |
| COP 4655 | Mobile App Project | COP 3410C or COP 3530C | 3 |
| COP 4703 | Advanced Database Systems | COP 3540 | 3 |
| COP 4808 | Full-Stack Web Development | COP 3834 | 3 |
| COT 4400 | Design and Analysis of Algorithms | COP 3410C or COP 3530C | 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听 | 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 |
| BSC 4892 | Honors AI Applications in Biology | 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 2312 | Honors Calculus with Analytic Geometry 2 | 4 |
| MAC 2313 | Honors Calculus with Analytic Geometry 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 (3-12 credits)
Choose one to three additional course from either group C, D, or E.
Honors thesis (IDS 4970, taken twice for a total of 6 credits)
Total credits: 46-61 credits
*Students are reminded that they need 42 upper-level (3000 or 4000-level) credits to graduate.