2016-06-27-first-ds-course-user's Introduction

2016-06-27-first-ds-course-user

Slides for the "A first-year undergraduate data science course" talk at useR 2016 at Stanford.

You can watch the video of this talk here.

Course info

The course titled Better Living Through Data Science: Exploring / Modeling / Predicting / Understanding. Previous course home pages:

Fall 2015: https://www.stat.duke.edu/courses/Fall15/sta112.01/ (uses git/GitHub)
- GitHub repo for course materials: https://github.com/mine-cetinkaya-rundel/sta112_f15
Fall 2014: https://www.stat.duke.edu/courses/Fall14/sta112.01/ (does not use git/GitHub)
- Course materials not on GitHub, but available on request

Course info
- Overview: audience, content, description
- Structure:
  - Skills: data wrangling, EDA, visualization, basic inference, modeling, effective communication of results
  - Computation: R + RStudio + git/GitHub
  - Case studies: movie reviews, sports, airline delays, paris paintings, ...
  - Assessment: in class team exercises, individual HW, midterm + final project, take home final exam
Computation:
- R/RStudio: server
- R Markdown: why and how
  - Why:
    - Noble goal: Only workflow is a reproducible workflow
    - Teaching goal:
      - Seeing code and output in one place helps learning
      - Syntax highlighting
    - Efficiency goal: Easier grading!
  - How: Knit early and often
- git/GitHub via RStudio: why and how
  - Why: Early introducton + marketibility
  - How:
    - As course management system
    - For team collaboration
    - Details on org/repo
  - Challenges:
    - Remember to pull before starting work
    - Resolving merge conflicts
    - Sometimes RStudio interface isn't sufficient
    - First assignment an individual assignment (not team)
Exercise examples:
- modeling paris paintings
- basketball data scraping + shiny
Interest and impact:
- data on student interest for course
- curricular changes inspired by course
  - gender balance comment

Recommend Projects