Skip to content

Teaching repo for Applied Data Science @ Columbia, a project-based course for data science skills (statistical thinking, machine learning, data engineering, team work, presentation, endurance of frustration, etc).

Notifications You must be signed in to change notification settings

sss2289/ADS_Teaching

Repository files navigation

Stat GR5243 Applied Data Science

Spring 2018 - Teaching Materials (Syllabus)


Project cycle 1: (Individual) R notebook on presidential addresses

(starter codes)

Week 1 (1/17)

Week 2 (1/24)


Project cycle 2: Shiny App Development

(starter codes)

Week 3 (1/31)

  • Project 1 presentations
  • 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 (2/7)

Week 5 (2/14)

Week 6 (2/21)

  • Project 2 presentations

Project cycle 3:

(starter codes)

Week 6 (2/21)

  • 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
      • the project description,
      • an example project
  • Intro to Project 3
  • Example main.Rmd (pdf)

Week 7 (2/28)

Week 8 (3/7)

Spring break

Week 9 (3/21)

Class canceled

Week 10 (3/28)

  • Project 3 presentations

Project cycle 4: Algorithm implementation and evaluation

(starter codes)

Week 10 (3/28)

Week 11 (4/4)

  • Recap on project 4 requirements.
  • Overview of collaborative filtering.
  • Overview of the reference papers (Chengliang).
  • Example testing report.

Week 12 (4/11)

Week 13 (4/18)

  • Project 4 presentations

Project cycle 5:

Week 14 (4/25)

  • Project 5 Presentations
  • [Project 3 summary]

About

Teaching repo for Applied Data Science @ Columbia, a project-based course for data science skills (statistical thinking, machine learning, data engineering, team work, presentation, endurance of frustration, etc).

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • HTML 99.9%
  • Other 0.1%