ETC5521: Exploratory Data Analysis

Lecturers

• Prof Di Cook
• Dr Emi Tanaka (Chief Examiner)

Tutors

• Sayani Gupta
• Sherry Zhang

Consultations

All consultations will be in using zoom. Check Moodle for the links.

• Prof Di Cook: Thu 11.30-12.30pm (Wk 1-2, 5-6, 9-10)
• Dr Emi Tanaka: Thu 11.30-12.30pm (Wk 3-4, 7-8, 11-12)
• Sayani Gupta: Tue 1.45-2.30pm (Wk 1-12)
• Sherry Zhang: Fri 2.45-3.30pm (Wk 1-12)
• R Help Desk: Mon, Wed, Fri 12.30-1.30pm (Wk 10-12) by Nick Tierney

Expectations

• Unless your question is a personal issue or an administrative matter, please do not send us direct email. The moodle discussion forum is appropriate for questions related to the course.
• For personal or private administrative issues, the email contact is: ETC5521.Clayton-x@monash.edu
• Lectures are recorded, and you are expected to have either attended the lecture, or watched the recordings fully, prior to the tutorial for the week.
• Tutorials are not recorded, and attendance is expected.
• Bring your own laptop to the computer labs. If you do not have a laptop you can borrow one from MoVE
• If you register after the start of the semester or if you miss a lecture/tutorial, it is your responsibility to catch up with missed material, learn about due dates for material to be turned in, and getting assigned to a group for team work, as necessary.
• The computer software R and RStudio will be used for the unit.

Tentative Schedule

• Lectures: Mon 4-6pm on zoom (see Moodle for link)
• Tutorials: Mon 6-7.30pm on zoom (see Moodle for link)

There are no lectures or tutorials during the midsemester break.

Week	Lecturer	Slides	Tutorial	Topic	Readings	Assessments
0	Di	A:		Course information
1	Di	A: ; B:		Overview. Why this course? What is EDA?	50 Years of Data Science	Reading quizzes each week. Engagement each week counts.
2	Di	A: ; B:		Learning from history	EDA Case Study: Bay area blues
3	Emi	A: ; B:		Initial data analysis: Model dependent exploration and how it differs from EDA	The initial examination of data
4	Emi	A: ; B:		Working with a single variable, making transformations, detecting outliers, using robust statistics	Case Study: Behaviours of dairy calves & Unwin (2015) Graphical Data Analysis Ch 3-4	Assignment 1 due Fri 28 Aug 11:55 PM
5	Di	A: ; B:		Bivariate dependencies and relationships, transformations to linearise	Wilke (2019) Ch 7 Visualising distributions & Unwin (2015) Graphical Data Analysis Ch 5
6	Di	A: ; B:		Going beyond two variables, exploring high dimensions	Wilke (2019) Ch 9 Visualising many distributions & Unwin (2015) Graphical Data Analysis Ch 6
7	Emi	A: ; B:		Making comparisons between groups and strata	Unwin (2015) Graphical Data Analysis Ch 10	Assignment 2 due Fri 18 Sep 11:55 PM
Midsemester Break (2 weeks)
8	Emi	A: ; B:		Sculpting data using models, checking assumptions, co-dependency and performing diagnostics	Cook & Weisberg (1994) An Introduction to Regression Graphics Ch 6 and Cleveland (1993) Visualising Data Ch 4
9	Di	A: ; B:		Exploring data having a space and time context	Reintroducing tsibble: data tools that melt the clock; Unwin (2015) Graphical Data Analysis Ch 11
10	Di/Guest	A:		Exploring data having a space and time context	Healy (2018) Data Visualization, Chap 7, Draw maps; Perpinan Lamigueiro (2018) Displaying Time Series, Spatial and Space-Time Data with R
11	Emi	A: ; B:		Using computational tools to determine whether what is seen in the data can be assumed to apply more broadly	Wickham et al. (2010) Graphical inference for Infovis
12	Emi/Di			Your turn to present your project work		Group presentation Mon 2 Nov 4:00PM. Assignment 3 due Fri 6 Nov 11:55 PM