Replication Project Part 1: EDA, Running Data Science Teams
Class Coverage
- Lecture: How do we run a data science project? What skills are necessary on AI teams? How can/do data scientists work effectively with other roles? Plus, a brief introduction to EDA.
- Workshop: Initiate Q1 Project team groupings and discuss team roles.
Pre-Class Readings
Please read the following:
- Taxonomy of AI Risk (NIST)
- Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (Holstein et al.)
Participation Questions
Submit to Gradescope by 10AM PT on Thursday, October 20th
- What role do you see yourself taking on in a data science team, and why?
- Describe a time when you struggled to complete an assignment (career or academic). What was missing (resources, support, etc.)? How did you resolve it?
This Week’s Slides
Assigned for Week 05
- Complete next week’s readings
- If you signed up to present on “Big Data’s Disparate Impact” (Barocas and Selbst) come prepared to present next week and submit your presentation on Gradescope by 10AM PT on Friday, October 28th
- Submit your answers to next week’s participation questions to Gradescope by 10AM PT on Thursday, October 27th
- Get started on your Q1 Project Checkpoint, due October 30th
- Replication Project Part 01: EDA