Stat GU4243/GR5243 Applied Data Science

Spring 2023 - Teaching Materials (Syllabus)

Shortcuts: Project 1 | Project 2 | Project 3 | Project 4 | Project 5

---

Project cycle 1: (Individual) R notebook for exploratory data analysis

(starter codes)

Week 1 (Jan 18)

• Introduction to ADS
• Project 1 description(Github Classroom Link)
• Tutorial 1 R Notebook + Example: Repo | Knitted HTML R Notebook | Presentation + More Examples: Repo1, Repo2, Repo3, Repo4
• A tutorial on GitHub

Week 2 (Jan 25)

• Recap on last week
• Overview of starter codes
• An example R notebook on presidential speeches (HTML)
• Interactive Word Cloud
• Submission and presentation for project 1
• Discussion and Q&A

Week 3 (Feb 1)

• Project 1 presentations.

Finished student projects

---

Shortcuts: Project 1 | Project 3 | Project 4 | Project 5

Project cycle 2: Shiny App Development

(starter codes)

Week 3 (Feb 1)

• Project 2 starts.
  • Check Piazza for your project team and GitHub join link.
  • After you join project 2, you can clone your team's GitHub repo to your local computer.
  • You can find in the starter codes:
    • the project description,
    • an example toy shiny app.

Week 4 (Feb 8)

• Spatial data visualization
• Tutorial on project 2 - Introduction to shiny app (app)
• A note on contribution
• Shiny Tutorial (zipped folder) (online link)
• Shiny Examples from previous semesters (Example 1: Online, Repo; Example 2: Online, Repo)
• Peer review of Project 1
• Discussion and Q&A

Week 5 (Feb 15)

• Tutorial on SQL in R(zipped folder)
• Tutorial on RShiny Deployment in GCP
• Tutorial on giving presentations
• Discussion on project 2

Week 6 (Feb 22)

• Project 2 presentations

Finished student projects

---

Shortcuts: Project 1 | Project 2 | Project 4 | Project 5

Project cycle 3: Predictive Modeling

(starter codes)

Week 6 (Feb 22)

• Project 3 starts.
  • Check Piazza for your project team and GitHub join link at the end of this week.
  • After you join project 3, you can clone your team's GitHub repo to your local computer.
  • You can find in the starter codes
    • Intro to Project 3

Week 7 (Mar 1)

• Recap on project 3 requirements and starter codes.
• Tutorials + Q&A
  • Tutorials: Basic Image Analysis in Python, in R (zipped folder)
  • Overview on Weakly Supervised Learning

Week 8 (Mar 8)

• Overview on predictive modeling
• Project 3 reminders
• Discussion

Spring Break (Mar 15)

Week 9 (Mar 22)

• Project 3 submission and presentations

Finished student projects

---

Shortcuts: Project 1 | Project 2 | Project 3 | Project 5

Project cycle 4: Algorithm implementation and evaluation

(starter codes)

Week 9 (Mar 22)

• Introduction to Project 4

Week 10 (Mar 29)

• Recap on project 4 requirements.
• Machine Learning Fairness Introduction (slides used in class)
• Overview of the Methods from the reference papers (hand-written notes)
• Method assignment on Piazza

Week 11 (Apr 5)

• Q&A
• Team meeting

Week 12 (Apr 12)

• Project 4 presentations

Finished student projects

---

Shortcuts: Project 1 | Project 2 | Project 3 | Project 4

Project cycle 5: Free topic

Week 12 (Apr 12)

• Project 5 discussions

Week 13 (Apr 19)

• Project 5 Presentations
• Project 3 performance: a summary
• Take home message for the class

Finished student projects

---

Shortcuts: Project 1 | Project 2 | Project 3 | Project 4