Final Project
Capstone project demonstrating the intersection of fashion and data science
Project Overview
The final project is your opportunity to showcase everything you've learned about the intersection of fashion and data science. You'll develop an original data-driven project that addresses a real challenge or opportunity in the fashion industry, demonstrating your mastery of the technical skills and business acumen gained throughout the semester.
Key Objectives
- • Apply data science techniques to solve a fashion industry problem
- • Demonstrate understanding of fashion business models and consumer behavior
- • Present findings to industry professionals and peers
- • Showcase technical skills in Python, data analysis, and visualization
- • Address real-world challenges with actionable business insights
Project Requirements
Technical Components
- • Data Collection: Use real fashion industry datasets or collect your own data
- • Analysis: Apply at least 3 techniques learned in class (EDA, clustering, prediction, NLP, computer vision, etc.)
- • Code: Well-documented Python code with clear methodology
- • Visualization: Clear, professional visualizations that support your findings
Business Impact
- • Address a specific fashion industry challenge
- • Provide actionable business recommendations
- • Consider ethical implications and sustainability
- • Demonstrate understanding of industry context
Deliverables
- • Comprehensive project report (3,000-4,000 words)
- • Complete Python code with documentation
- • 10-minute presentation for industry panel
- • Executive summary (1-2 pages)
Suggested Project Areas
Consumer Analytics
Analyze customer behavior, preferences, or segmentation using e-commerce or social media data
Trend Forecasting
Use NLP and computer vision to predict upcoming fashion trends from social media or runway data
Sustainability Impact
Analyze the data behind circular fashion initiatives or sustainable brand performance
Brand Analysis
Compare brand performance, sentiment analysis of reviews, or competitive positioning
Supply Chain Optimization
Use predictive modeling to optimize inventory, pricing, or demand forecasting
Fashion Tech Innovation
Develop a prototype using computer vision for styling, fit, or product recommendation
Project Timeline
Project Proposal
Submit 2-page proposal outlining your chosen problem, data sources, and planned methodology
Progress Check-in
Present preliminary findings and receive feedback from instructors and peers
Capstone Development Week
Hands-on development week with dedicated time for project work and one-on-one support from course staff
Final Presentations & Industry Panel
Present your project to the class. Top 3 projects will be selected to present to our expert industry panel
Industry Panel & Career Insights
The top three projects will have the opportunity to present to a panel of industry experts. This is a unique chance to receive feedback from professionals working at the intersection of fashion and data science, and to network with potential mentors or employers.
Panel Discussion Topics
- • Current biggest data challenges in fashion
- • Emerging technologies and their business impacts
- • Various career pathways in fashion tech
- • Skill gaps in the current job market
- • Future directions of fashion technology
- • Networking and professional development
Potential Panel Members
Fashion Tech Startup
Founder(s)
Traditional Fashion Brand
Data Science Lead
Fashion E-commerce
ML Engineer
Investment
Fashion-Tech VC/Investor
Product Strategy
Fashion-Tech Product Manager
Media & Analysis
Fashion-Tech Editor/Journalist
Evaluation Criteria
Technical Execution
Quality of data analysis, appropriate use of techniques, code quality
Business Impact & Insights
Relevance to fashion industry, actionable recommendations, business understanding
Presentation & Communication
Clarity of presentation, ability to explain technical concepts to business audience
Innovation & Creativity
Original approach, creative problem-solving, potential real-world impact
Resources & Support
Technical Resources
- • Access to fashion industry datasets through course partnerships
- • Python libraries and tools covered in class
- • Code examples and templates from weekly assignments
- • Office hours with course staff for technical guidance
Industry Support
- • Guest speaker insights and contact information
- • Fashion industry reports and case studies
- • Networking opportunities with panel members
- • Career guidance and mentorship connections