Data Science B.S.
All students must meet the University Requirements.
Data Science Major
The study and applications of Data Science impacts our lives in myriad ways every moment of every day. Often times we are unaware of the role this important field plays in our daily routines. We have data scientists to thank as we read the latest news on our social media feed of choice, or watch a movie suggested by our go-to streaming app. Even the food we eat has likely been guided by the study of big data. For example, researchers are working hand-in-hand with farms of all sizes to help analyze data which in turn can identify and reduce areas of inefficiency and waste, and bring food to your table in a faster, safer, and more cost-effective way.
The curriculum of the Bachelor of Science with a major in Data Science combines courses from the disciplines of Statistics, Mathematics, and Computer Science to prepare students for careers in Big Data Science & Analytics: rapidly growing fields with huge unmet demand. The unique interdisciplinary educational experience allows students the opportunity to acquire the broad base of knowledge and skills that employers are seeking.
Regulations
Students pursuing the Bachelor of Science degree with a major in Data Science are subject to the Academic Standards in CEMS outlined in this catalogue.
The Curriculum for the B.S. in Data Science
A minimum of 120 credits is required. Students must satisfy all University requirements.
| CORE (6 CREDITS): | ||
| CEMS 050 | CEMS First Year Seminar | 1 |
| CS 064 | QR: Discrete Structures | 3 |
| or MATH 052 | QR:Fundamentals of Mathematics | |
| STAT 151 | QR: Applied Probability | 3 |
| or STAT 251 | QR: Probability Theory | |
| COMPUTER SCIENCE CORE (20 to 23 CREDITS): | ||
| CS 021 | QR: Computer Programming I | 3 |
| CS 110 | QR: Intermediate Programming | 4 |
| CS 124 | QR: Data Struc & Algorithms | 3 |
| CS 204 | QR: Database Systems | 3 |
| CS 224 | QR:Algorithm Design & Analysis | 3 |
| CS 254 | QR: Machine Learning | 3 |
| or STAT 288 | QR: Statistical Learning | |
| or CS 288 | QR: Statistical Learning | |
| CS 292 | Senior Seminar | 1 |
| 100-Level (or above) CS Elective 1 | 3 | |
| STATISTICS CORE (18 to 21 CREDITS): | ||
| STAT 087 | QR: Intro to Data Science | 3 |
| STAT 141 | QR:Basic Statistical Methods 1 | 3 |
| or STAT 143 | QR: Statistics for Engineering | |
| or STAT 211 | QR: Statistical Methods I | |
| STAT 201 | QR:Stat Computing&Data Anlysis | 3 |
| STAT 221 | QR: Statistical Methods II | 3 |
| STAT 281 | Capstone Experience | 1-18 |
| or STAT 293 | Undergrad Honors Thesis | |
| or MATH 293 | Undergraduate Honors Thesis | |
| or CS 283 | Undergraduate Honors Thesis | |
| STAT/CS 287 | QR: Data Science I | 3 |
| STAT 288 | QR: Statistical Learning | 3 |
| or CS 254 | QR: Machine Learning | |
| MATHEMATICS CORE (11 CREDITS): | ||
| MATH 021 | QR: Calculus I | 4 |
| MATH 022 | QR: Calculus II | 4 |
| MATH 122 | QR: Applied Linear Algebra | 3 |
| or MATH 124 | QR: Linear Algebra | |
| Choose 12 Credits in Data Science (DS) electives selected from the list of approved courses (see below) in MATH/STAT/CS/CSYS/NR, with at least 9 of these credits at the 200-level (or above): 2 | 12 | |
| QR: Advanced Programming | ||
| QR: Database Design for Web | ||
| QR: Cybersecurity Principles | ||
| Cybersecurity Defense | ||
| QR: Software Engineering | ||
| QR:Algorithm Design & Analysis | ||
| Human-Computer Interaction | ||
| QR: Machine Learning | ||
| Modeling Complex Systems 3 | ||
| Evolutionary Computation 3 | ||
| QR: Calculus III | ||
| QR: Basic Combinatorial Theory | ||
| QR:Mathematical Models&Anlysis | ||
| QR:Intro to Numerical Analysis | ||
| QR:Chaos,Fractals&Dynmcal Syst | ||
| QR:Mathematical Biology&Ecol | ||
| Principles of Complex Systems 3 | ||
| Complex Networks 3 | ||
| QR:Basic Statistical Methods 2 | ||
| QR:Stats for Qualty&Productvty | ||
| QR: Experimental Design | ||
| QR: Categorical Data Analysis | ||
| QR: Statistical Inference | ||
| QR: Statistical Learning | ||
| QR:Survivl/Logistic Regression | ||
| Bayesian Statistics 3 | ||
| Data Science II 3 | ||
| Intro to Geog Info Systems | ||
| Appld Artificial Neural Ntwrks 3 | ||
| Applied Geostatistics 3 | ||
| CHOOSE ONE 2-COURSE NATURAL SCIENCE (W/ LAB) SEQUENCE: | 8 | |
| Principles of Biology and Principles of Biology | ||
| General Chemistry 1 and General Chemistry 2 | ||
| Fundamentals of Physics I and Fundamentals of Physics II | ||
- 1
Students should select appropriate courses from list of approved Data Science (DS) electives. Alternative courses may be approved by the DS Curriculum Committee.
- 2
Additional courses, including special topics courses, may be granted approval if appropriate (consult advisor)
- 3
Undergraduate students require instructor permission to enroll in 300-level courses.
- 4
Students are required to complete a minimum of 3 cr. Humanities and 3 cr. Social Sciences and 3 cr. Professional Development Electives.
Complex Systems and Data Science AMP
Complex Systems and Data Science M.S.
Complex Systems and Data Science Ph.D.
See the online Graduate Catalogue for more information