Fall 2019 - Teaching Materials (Syllabus)
- Introduction to ADS (TZ version)
- Project 1 description
- Tutorial 1 R Notebook + Example: Repo | Knitted HTML R Notebook | Presentation
- A tutorial on GitHub
- An example R notebook on presidential speeches (HTML)
- zipped folder
- known running issues & solutions, prepared by Yang Yang @jokerkeny
- Overview of starter codes
- Interactive Word Cloud
- Data Story Examples: Example 1 (repo), Example 2.
- Discussion and Q&A
- Check out our Piazza Q&A discussion board for access to (optional) additional tutorials from our ADS DataCamp Classroom.
- 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.
- Spatial data visualization
- Tutorial on project 2 - Introduction to shiny app (app)
- Shiny Tutorial (zipped folder) (online link)
- Shiny Examples from 2019 Spring (Example 1: Online, Repo; Example 2: Online, Repo)
- A note on contribution
- Discussion and Q&A
- Feebback on project 1
- Tutorial on SQL in R(zipped folder)
- Tutorial on RShiny Deployment in GCP
- Tutorial on giving presentations
- Brainstorm on project 2
- Project 2 presentations
- 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. Examplemain.Rmd
- 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)
- Overview on predictive modeling
- Project submission checklist
- Discussion
- Project 3 submission and presentations
- Introduction to Project 4
- Recap on project 4 requirements.
- Introduction to Recommender Systems.
- Overview of the starter codes
- Overview of the reference papers.
- Q&A on Algorithms
- Team Meeting
- Project 4 presentations
- Project 5 discussions
- Project 5 presentations