Schedule

Here’s your roadmap for the course

Content (): This contains the readings, slides, data files, etc. for each session. These will also be added on Canvas on the day of each session. It helps to read the material before each session.
Example (): This page contains worked examples of fully annotated R code that you can use as a reference. This is only a reference page—you don’t have to necessarily do anything here.
Exercise (): These are interactive exercises where you have to provide R code in your browser to solve a problem, much like Datacamp. These are not graded, but are always there for your reference.
Assignment (): This page contains instructions for the three workshop exercises (3-4 brief tasks plus a challenge), for the individual portfolio website project, and the final group project. Assignments are due by 11:59 PM UTC on the day they’re listed.

		Foundations: EDA and Intro to Data Science	Content	Example	Exercise	Assignment
1	01 Sep	Lecture 1: Exploratory Data Analysis
2	01 Sep	Workshop 1: Import, visualise, and manipulate data
2	02 Sep	Github + portfolio website workshop
	05 Sep	Homework 1 Due
		Inferential Statistics	Content	Example	Exercise	Assignment
3	06 Sep	Lecture 2: Sampling and Probability Distributions
4	06 Sep	Workshop 2: Confidence Intervals; reshape data
	07 Sep	Homework 2 Due
5	08 Sep	Lecture 3: Hypothesis Testing; there is only one test
6	08 Sep	Workshop 3: Hypothesis testing; A/B testing; simulating with `infer`
	12 Sep	Homework 3 Due
		Regression Modelling	Content	Example	Exercise	Assignment
7	13 Sep	Lecture 4: Introduction to regression models
8	13 Sep	Workshop 4: Workshop on regression
9	15 Sep	Lecture 5: Further regression topics; regression diagnostics
	21 Sep	Final project due
	21 Sep	Portfolio website due
10	22 Sep	Group Case Presentations; course wrap up
	23 Sep	Revision Session
	24 Sep	Final Exam