Schedule

Here’s your roadmap for the semester!

• Content (): This page contains the session outline, readings, and R code examples for each week. Start here.

• Videos (): Supporting videos from YouTube to help with key concepts and coding in R. These are especially useful when working through assignments.

• Assignments (): Weekly exercises using the r4np interactive package. Assignments include a short weekly reflection on what you have learned. Assignments are due by 11:59 PM on the Sunday after each lesson. See the schedule table below to see exactly when each one is due.

• Projects (): Problem sets and the data project. Problem sets (PS02–PS06) are available roughly once every two weeks and are optional extra credit — they build deeper statistical skills using real datasets. The data project is the main course assessment and replaces a final exam.

tl;dr: Follow this general process each week:

• Work through the content page ()
• Complete the assignment () using the videos () as a reference
• Work on problem sets () when available for extra credit

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Foundations

	Title	Content	Videos	Assignment	Project
April 14–April 20 (Session 1)	Course introduction, data types, R and RStudio
April 21–April 27 (Session 2)	R basics 1: Vectors, data frames, lists, basic scripts
April 28–May 4 (Session 3)	R basics 2: Data visualisation

Core concepts

	Title	Content	Videos	Assignment	Project
May 12–May 18 (Session 4)	Sampling and probability
May 19–May 25 (Session 5)	Visualising probability
May 26–June 1 (Session 6)	Manipulating data 1: Data project intro
June 2–June 8 (Session 7)	Manipulating data 2
June 9–June 15 (Session 8)	Variable associations 1
June 16–June 22 (Session 9)	Variable associations 2

Advanced topics

	Title	Content	Videos	Assignment	Project
June 23–June 29 (Session 10)	Regression modelling 1
June 30–July 6 (Session 11)	Regression modelling 2
July 7–July 13 (Session 12)	Text as data