Stat GU4243/GR5243 Applied Data Science

Fall 2020 - Teaching Materials (Syllabus)

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

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

(starter codes)

Week 1 (Sep 9)

• Introduction to ADS
• Project 1 description
• Overview of starter codes
• Tutorial 1 R Notebook + Example: Repo | Knitted HTML R Notebook | Presentation + More Examples: Repo1, Repo2
• A tutorial on GitHub

Week 2 (Sep 16)

• Recap on last week
• Submission and presentation for project 1
• Discussion and Q&A

Week 3 (Sep 23)

• Project 1 presentations.

Finished student projects

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Shortcuts: Project 1 | Project 3 | Project 4

Project cycle 2: Shiny App Development

(starter codes)

Week 3 (Sep 23)

• 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
    • a short tutorial to get you started.

Week 4 (Sep 30)

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

Week 5 (Oct 7)

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

Week 6 (Oct 14)

• Project 2 presentations

Finished student projects

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Shortcuts: Project 1 | Project 2 | Project 4

Project cycle 3: Predictive Modeling

(starter codes)

Week 6 (Oct 14)

• Project 3 starts.
  • Check Piazza for your project team and GitHub join link.
  • 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 (motivation)
    • an example main.rmd that provides an example structure for this project. Example main.Rmd

Week 7 (Oct 21)

• Recap on project 3 requirements and starter codes.
• Tutorials + Q&A
  • Tutorials: Basic Image Analysis (zipped folder) + Fiducial Detection
  • Tutorial on gradient boosting machines (GBM)
    • A shiny app on GBM [R codes]
  • Overview on Imbalanced Classification

Week 8 (Oct 28)

• Overview on predictive modeling
• Project submission checklist (on piazza)
• Discussion

Week 9 (Nov 4)

• Project 3 submission and presentations

Finished student projects

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Shortcuts: Project 1 | Project 2 | Project 3

Project cycle 4: Algorithm implementation and evaluation

(starter codes)

Week 9 (Nov 4)

• Introduction to Project 4

Weeks 10 (Nov 11)

• Recap on project 4 requirements.
• Overview of Causal Inference
• Overview of the Methods from the reference papers.
• Method assignment.

Weeks 11 (Nov 18)

• Q&A
• Team Meeting

Thanksgiving Break

Weeks 12 (Dec 2)

• Project 4 presentations

Finished student projects

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Project cycle 5:

Weeks 12 (Dec 2)

• Project 5 discussions
• Project 3 Summary

Week 13 (Dec 9)

Finished student projects

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