Daily Schedule

Time Daily Session
9:00am - 10:30am Session 1
10:30am - 10:45am (Morning break)
10:45am - 12:15pm Session 2
12:15pm - 1:15pm (Lunch)
1:15pm - 2:45pm Session 3
2:45pm - 3:00pm (Afternoon break)
3:00pm - 4:30pm Session 4
4:30pm - 5:00pm Wrap Up Activity, Daily Journal

Day 9 5/31

  • [AM]
  • AM Project work, ML methods
  • Python ML methods, data visualization for 4 datasets presentations
  • Python ML labs
  • Project work
  • Final comments
  • What Next
  • Daily Journal
  • Post-workshop survey

Day 8. 5/30

  • [AM]
  • Final project work
  • Final project presentations
  • ML algorithms
  • ML labs
  • Project work
  • Daily Journal

Day 7. 5/29

  • [AM]
  • AM project work
  • ML group exercises and labs
  • PM: Final R Presentation
  • PM: Python group exercises presentation
  • ML methods
    • Knn
    • K means
    • LDA
    • SVC
    • Descision trees
    • Random forests
    • PCA.
  • ML Labs
  • ML project work
  • Discussion/Feedback
  • Wrap up: Questions.Look Ahead
  • Daily Journal

Day 6. 5/28

  • [AM]
  • Probability distributions and statistical inference
  • Linear and generalized linear models
  • Statistical inference and random number genration
  • Machine learning with scikit-learn
  • Lab : Pandas, matlibplot, numpy
  • Group exercises : EDA in python on 4 datasets
  • Project work
  • Project set up on Github
  • Wrap up: Questions.Look Ahead
  • Daily Journal

Day 5. 5/24

  • [AM]
  • Probability and Statistics
  • LAB on Linear Models in R
  • Mini Project 2 Presentation, Github updates of final versions.
  • Intro to Python, Jyupter notebook
  • Project Work
  • Wrap up: Questions.Look Ahead
  • Daily Journal

Reminder: No workshop on Monday. Enjoy Memorial Day and the start of Summer!

Day 4. 5/ 23

Day 3. 5/22

  • [AM] (link at top right corner)
  • 5.Data Types
  • 6.Iteration
  • 7.Model
  • Mini Project 1 presentations, discussion, peer evaluations
    • Read Data Viz mistakes and find any mistakes made during presentations
  • Lab 02: Introduction to Social Network Analysis.
  • Wrap up: Questions.Look Ahead
  • Daily Journal
  • Install Git - Follow instructions in Chapter 6 Install Git available at Happy Git With R
  • Create a GitHub account at github.com

Day 2 : 5/ 21

  • [AM] (link at top right corner)
  • 2c. Transform: mutate onwards
  • 3.Tidy
  • 4.Case Study
  • 5.Data Types
  • Few things before Project:
  • Mini-project 1
    • Data visualization of Aid Data to answer Who Donates?, How much do they donate? and Why do they donate?
    • Read and don’t commit the Data Viz mistakes
    • Group Presentations this PM.
    • Discuss other ways of visualizing the dataset here
  • Wrap up: Questions, Look Ahead
  • Daily Journal (link at top right corner)

Day 1 : 5/ 20

  • Preworkshop survey
  • [AM] (link at top right corner)
  • Login to Rstudio Cloud
  • 0.Introduction
  • 1.Visualization
  • 2.Transform
  • Quick Detour
  • LAB 01: Practice Visualization
  • Activity
    • Top 10 Visualization Mistakes
    • Something to do with data that didn’t ring true
  • Discussion: Ethics, You can’t be certain, but you can’t be wrong
  • Wrap up: Questions.Look Ahead
  • Don’t forget : Daily Journal (link at top right corner)
  • Finish Quick Detour
  • Install Git - Follow instructions in Chapter 6 Install Git available at Happy Git With R
  • Create a GitHub account at github.com