DO NOT RELY ON THIS ROUGH DRAFT.
An official version will be posted by 1/19/27.

(Google Docs folder)

(Syllabus)

(course ideas)

(assessment ideas)

guest lecture ideas:

• UW Library folks: Jenny Patino, Brady Krien; can talk about data ethics and data management; how do we fit these into 479? Also library can archive the public part of our projects so that sudents can refer to them in resumes, portfolios, etc.
• Collen Conroy, UW Theater might help with voice and physical presentation
• UW Writing Lab: check in again
• Erwin Lares is willing to give us a lecture on Quarto
• Adam Nelson, WISCURDS might help us in many ways; especially Quarto
• Kris Sankaran can lecture on simulation and data analysis

Day #: Date	Subject	Homework Due (11:59 p.m.)
1: Tu 1/19/27	course overview: description of tasks and deadlines	Read introductory email Q00: background survey (extended to Tu 2/2 to accommodate Fr 1/29 add deadline)
2: Th 1/21		UW Data Privacy training (2 online courses, __ and __ minutes)
3: Tu 1/26		select client
4: Th 1/28		data manifest for actual and desired data
5: Tu 2/2		generate simulated data from manifest
6: Th 2/4		EDA on simulated data
7: Tu 2/9		basic models on simulated data
8: Th 2/11		...
9: Tu 2/16
10: Th 2/18
11: Tu 2/23
12: Th 2/25
13: Tu 3/2
14: Th 3/4
15: Tu 3/9
16: Th 3/11
17: Tu 3/16
18: Th 3/18
[Tu 3/23, Th 3/25]	[no class--spring break]
19: Tu 3/30
20: Th 4/1
21: Tu 4/6
22: Th 4/8
23: Tu 4/13
24: Th 4/15
25: Tu 4/20
26: Th 4/22
27: Tu 4/27
28: Th 4/29

• Phase I: The Synthetic Sandbox (Weeks 1-2)
  • Week 1 (Individual): Each student builds a full analysis pipeline (Notebooks) using synthetic data to support both a positive and a negative conclusion.
  • Week 2 (Team Synthesis): Teams meet the client, realize their assumptions were likely wrong, and collapse individual pipelines into two refined, high-promise strategies with fresh synthetic data.
• Phase II: Data Audit & Scoping (Weeks 3-7)
  • Week 3: Real-world data intake and "Data Interrogation" (checking for bias, missingness, and structural anomalies).
  • Weeks 4-6: Exploratory Data Analysis (EDA) and preliminary modeling. Internal "Peer Review" sessions within the class.
  • Week 7: Preparation of the Mid-Point Strategic Alignment package.
• Phase III: The Mid-Semester Pivot (Week 8)
  • Milestone: Formal 30-minute video conference with partners.
    
  • Goal: Present the "Refined Project Charter" and get a "Go/No-Go" on the proposed modeling path.
• Phase IV: Execution & Containerization (Weeks 9-14)
  • Weeks 9-11: Model refinement, iterative testing, and "Dual-Team" cross-validation of results.
  • Weeks 12-13: Finalizing the Technical Evidence Report and hardening the code for the Docker container.
  • Week 14: Drafting the Executive Briefing and final walkthroughs.
• Phase V: The Final Hand-off (Week 15)
  • Milestone: Final on-campus presentations.
  • Deliverable: Delivery of the Project Hand-off Package (Code, Containers, Reports, and One-Pagers).