Minor in Data Science
The Data Science minor gives students a background in data analysis and visualization in the natural sciences and engineering, including exposure to algorithms and lower-level programming tools.
Declaring the minor
The Data Science minor has the following prerequisites before it can be declared:
- Introduction to Computer Programming (pick one):
- CSS 112: Programming for Scientific Applications (4 credits)
- CSS 132 + CSSSKL 132: Computer Programming for Engineers I + Skills Lab (6 credits)
- CSS 142 + CSSSKL 142: Computer Programming I + Skills Lab (6 credits)
- CSE 142: Computer Programming I (4 credits) at UW Seattle, or equivalent
- Statistics (pick one):
- BBUS 215: Business Statistics (5 credits)
- BIS 215: Understanding Statistics (5 credits)
- BMATH 215: Statistics for Health Sciences (5 credits)
- STMATH 341: Statistical Inference (5 credits)
- STMATH 390: Probability and Statistics in Engineering (5 credits)
- STMATH 392: Probability (5 credits)
The prerequisites must be completed (not in-progress) before declaring the minor. Once you have completed the prerequisites, contact the academic advisor for your major at any time to declare the minor.
Program requirements
Minimum credits and grades
- A minimum of 25 credits must be earned for this minor.
- A minimum of 15 credits used for this minor must be taken at UW Bothell, which means that a maximum of 10 credits may be taken elsewhere (including other UW campuses).
- No more than 10 credits of this minor can overlap with your major requirements.
- Each course used for the minor must be completed with a grade of 2.0 or higher.
Core requirements (15-18 credits)
- Data Science Programming (pick one):
- CSS 123: Programming for Data Science (3 credits)
- CSS 133 + CSSSKL 133 (6 cr)
- CSS 143 + CSSSKL 143 (6 cr)
- CSE 143: Computer Programming II (5 credits) at UW Seattle, or equivalent
- CSSSKL 123: Programming for Data Science Programming Skills Lab (2 credits)
- Computers, Ethics, and Society (pick one):
- CSS 211: Computers and Society (5 credits)
- BISSTS 307: Science, Technology, and Society (5 credits)
- BIS 232: Data Visualization (5 credits)
Elective requirements (10 credits)
You must complete ten (10) credits (usually two courses) from the following list. Click the links for course descriptions and prerequisites.
- BBIO 340: Computational Biology (5 credits)
- BBIO/CSS 383: Bioinformatics (5 credits)
- BCHEM 310: Molecular Modeling (5 credits)
- BENGR 310: Computation Physical Modeling (4 credits)
- BIS 412: Advanced Data Visualization (5 credits)
- BME 450: Ocean Engineering and Sciences (4 credits)
- BPHYS 450: Computational and Theoretical Modeling in Physics (5 credits)
- CSS 340: Applied Algorithmics (5 credits)
- CSS 382: Artificial Intelligence (5 credits)
- CSS 483: Bioinformatics Algorithms (5 credits)
- CSS 486: Machine Intelligence (5 credits)
- STMATH 208: Matrix Algebra (5 credits)
- STMATH 381: Discrete Mathematical Modeling (5 credits)
- STMATH 405: Numerical Analysis I (5 credits)
- STMATH 406: Numerical Analysis II (5 credits)
- STMATH 407: Linear Programming (5 credits)
- STMATH 408: Nonlinear Optimization (5 credits)
- STMATH 409: Advanced Linear Algebra (5 credits)
Registration
The Data Science minor is highly interdisciplinary, and its courses are offered by different programs on campus.
CSS courses
Due to high demand for CSS courses, we cannot guarantee that there will always be space available for Data Science minor students. CSS course registration for students in minors is restricted until close to the beginning of each quarter.
To ensure you have the best chance of taking the courses you need, we strongly recommend completing the registration request form.
Petitions
Data Science-related courses that are not included on the elective list are considered on a case-by-case basis. If you have a syllabus, letter from the instructor, or other detailed description for the class you wish to use as an elective, you may submit a petition to request for the course to count.