Why statisticians learn to program

• Independence: otherwise, you rely on someone else giving you exactly the right tool
• Honesty: otherwise, you end up distorting your problem to match the tools you have
• Clarity: often, turning your ideas into something a machine can do refines your thinking
• Fun: these were the best of times (the worst of times)

Cool example: catching an intruder

How this class will work

• Professor Ryan Tibshirani
• TAs Shannon Gallagher, Kwhangho Kim, Kevin Lin, Alan Mishler, Zixu Wang
• No programming knowledge assumed
• Some statistics knowledge assumed
• Focus almost entirely on R
• Class will be cumulative, so keep up with the material and assignments!

• Each class period: 1 or 2 “mini-lectures” (10-20 minutes), then “lab” (30-40 minutes)
• Lab work due at 11:59pm on each class day
• Graded \(\approx\) half on attendence, half on completion
• Homework due at 6pm on each Sunday
• Final group project (2-3 weeks)

• Grading breakdown:
  • Labs: 30%
  • Homework: 50%
  • Final project: 20%
• Class website: http://www.stat.cmu.edu/~ryantibs/statcomp-F16/
• Piazza group: for announcements and discussions
• Blackboard: used to collect submissions, and keep track of grades

R, R Studio, R Markdown

• R is a programming language for statistical computing
• R Studio is an integrated development environment for R programming
  
• R Markdown is a markup language for combining R code with text

All 3 are free, and all 3 will be used extensively in this course

(survey, demo)

Read the syllabus

It’s on the course website

Copying

Don’t do it

Things you should have done, by the end of this week

• Install R Studio on your laptop
• Get comfortable with R Studio, knitting Rmd documents into HTML files
• Get comfortable with navigating R help files
• Get comfortable with installing R packages
• Complete “Interactive R Online” introductory course, by 6pm on Sunday

Note: you should do the labs, as usual (but they won’t be graded this week)