Course Syllabus
Complete course overview, policies, and schedule
Course Information
Course Title
Data 198: Fashion x Data Science
Course Number
Data 198
Semester
UC Berkeley, Spring 2026
Lead Facilitator
Janhavi Revashetti
Email: jrevashe@berkeley.edu
Office Hours
N/A
Class Schedule
N/A
Course Description and Learning Objectives
Fashion x Data Science is a DeCal course designed to introduce how data science concepts are used within the fashion industry by balancing technical skills with practical applications. Topics will build upon each other every week along with small focused projects. Students will form teams to complete a capstone project as the final assessment. The top three projects will be chosen to present to a panel of industry experts and may receive the opportunity for external collaboration.
By the end of the semester students will be able to:
- •Analyze how and why data science is relevant across various fashion sectors
- •Apply statistical analysis and machine learning techniques to find insights from fashion datasets
- •Build predictive models for sales forecasting, A/B testing and trend forecasting using fashion datasets
- •Use computer vision and natural language processing methods for classifying fashion products and performing sentiment analysis
- •Evaluate sustainability benefits in circular fashion solutions
- •Present weekly project findings through detailed reports and a final capstone project
Course Impact
The goal of this course is to demonstrate the overlap that objective fields like data science can have with creative industries by not only teaching the necessary technical skills but also providing the opportunity to make a real-world impact through potential industry collaborations. The intersection of fashion and technology is rapidly evolving but is uncommon to encounter in academic settings. Therefore, this course would position Berkeley as one of the few universities exploring this interdisciplinary field through a data-driven perspective.
Prerequisites
Basic python fundamentals are helpful, however, this course is designed to also work for those with zero to no prior programming experience. It is aiming to teach technical concepts under the lens of the fashion industry.
Grading Breakdown
• Bonus Extra Credit for winning final project pitches
In order to pass this course, each lecture must be attended (with a maximum of two absences).
Each weekly assignment must be completed (with a max of one drop and 4 total slip days over the semester). Weekly assignments will be graded on accuracy + effort. We are trying to award grades based on how well students have understood the content/technical skills to finish the project. Honest efforts (with some inaccuracy) can still get full credit, however, assignments with little to no effort shown to understand course content will get 0%.
The Final Project/Pitch is the main component needed to pass this course. Students can either choose a project from a database of provided ideas or can come up with their own idea. 10% of this grade will go to the initial proposal which will mainly be graded on implementation and is intended for feedback from course staff. 20% of the grade will go towards the creativity/potential for impact of the final idea/project. 20% of the grade will go towards the quality of the final pitch. Extra credit will be awarded to the winning groups.
Methods of Instruction/Resources
Weekly in-person lectures will require attendance for students. Lecture will include going over coding labs to make it easier for students to complete weekly assignments/capstone projects.
Weekly readings will be assigned as a supplement to lecture in additional external course resources.
Weekly projects and the final capstone project will be ways students demonstrate their learning.
Materials
Installation of Jupyter notebook or preferred IDE environment. This course will be taught in Python.