Data Science B.S.
Data Science Major
The study and applications of Data Science impacts our lives in myriad ways every moment of every day. Often 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
All students must meet the Degree and University Requirements.
All students must meet the Catamount Core Curriculum Requirements.
A minimum of 120 credits is required. Students are required to complete a minimum of 3 cr. Professional Development Electives that are listed on the College Requirements page.
| CORE (6 CREDITS): | ||
| CEMS 1500 | CEMS First Year Seminar | 1 |
| CS 1640 | Discrete Structures | 3 |
| or MATH 2055 | Fundamentals of Mathematics | |
| STAT 2510 | Applied Probability | 3 |
| or STAT 5510 | Probability Theory | |
| COMPUTER SCIENCE CORE (20 to 23 CREDITS): | ||
| CS 1210 | Computer Programming I | 3 |
| CS 2100 | Intermediate Programming | 4 |
| CS 2240 | Data Struc & Algorithms | 3 |
| CS 3040 | Database Systems | 3 |
| CS 3240 | Algorithm Design & Analysis | 3 |
| CS 3540 | Machine Learning | 3 |
| or STAT 3880 | Statistical Learning | |
| or CS 3880 | Statistical Learning | |
| CS 3920 | Senior Seminar | 1 |
| 2000-Level (or above) CS Elective 1 | 3 | |
| STATISTICS CORE (18 to 21 CREDITS): | ||
| STAT 1870 | Intro to Data Science | 3 |
| STAT 1410 | Basic Statistical Methods 1 | 3 |
| or STAT 2430 | Statistics for Engineering | |
| STAT 2830 | Basic Statistical Methods 2 | 3 |
| STAT 3010 | Stat Computing&Data Anlysis | 3 |
| STAT 3210 | Advanced Statistical Methods | 3 |
| STAT 4810 | Capstone Experience | 1-8 |
| or STAT 3996 | Undergrad Honors Thesis | |
| or MATH 4996 | Undergraduate Honors Thesis | |
| or CS 4996 | Undergraduate Honors Thesis | |
| STAT/CS 3870 | Data Science I - Pinnacle | 3 |
| MATHEMATICS CORE (11 CREDITS): | ||
| MATH 1234 | Calculus I | 4 |
| MATH 1248 | Calculus II | 4 |
| MATH 2522 | Applied Linear Algebra | 3 |
| or MATH 2544 | 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 in courses numbered 3400 or above: 2 | 12 | |
| Advanced Programming | ||
| Database Design for Web | ||
| Cybersecurity Principles | ||
| Cybersecurity Defense | ||
| Software Engineering | ||
| Algorithm Design & Analysis | ||
| Human-Computer Interaction | ||
| Machine Learning | ||
| Calculus III | ||
| Basic Combinatorial Theory | ||
| Mathematical Models&Anlysis 3 | ||
| Intro to Numerical Analysis | ||
| Chaos,Fractals&Dynmcal Syst | ||
| Mathematical Biology&Ecol 3 | ||
| Stats for Qualty&Productvty | ||
| Experimental Design 3 | ||
| Categorical Data Analysis 3 | ||
| Statistical Inference | ||
| Statistical Learning | ||
| Survivl/Logistic Regression 3 | ||
| Intro to Geog Info Systems | ||
| CHOOSE ONE 2-COURSE NATURAL SCIENCE (W/ LAB) SEQUENCE: | 8 | |
| Principles of Biology 1 and Principles of Biology 2 | ||
| 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 5000-level courses.
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