ETC5521: Exploratory Data Analysis

Lecturer

• Prof Di Cook (Chief Examiner)

Tutors

• Jayani Lakshika
• Thomas Nguyen

Consultations

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

• Prof Di Cook: Tue 2:00-3:00 (352 Education Blg, 29 Ancora Imparo Way, Clayton campus)
• Jayani Lakshika: Fri 12:30-13:30 Menzies W9.20
• Thomas Nguyen: Wed 4:00-4:45 Menzies W9.20

Tentative Schedule

• Lecture: Thu 08:00-10:00 CL_Col-21.Eng-Lec_E1 (see Moodle for zoom link)
• Tutorials: one of the following
  • Thu 15:00-16:30 CL_Anc-19.LTB_132
  • Thu 16:30-18:00 CL_Anc-19.LTB_132

Week	Slides	Tutorial	Topic	Readings	Assessments
00	A:		Course information
01 (Jul 24)	A: ; B:		Overview. Why this course? What is EDA?	The Landscape of R Packages for Automated Exploratory Data Analysis	Tutorial preparation quizzes due each week.
02 (Jul 31)	A: ; B:		Learning from history	EDA Case Study: Bay area blues
03 (Aug 7)	A: ; B:		Initial data analysis and model diagnostics: Model dependent exploration and how it differs from EDA	The initial examination of data
04 (Aug 14)	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
05 (Aug 21)	A: ; B:		Working with a single variable, making transformations, detecting outliers, using robust statistics	Unwin (2015) Graphical Data Analysis Ch 3-4; Wilke (2019) Ch 6 Visualizing Amounts; Ch 7 Visualizing distributions;	Assignment 1 (individual) due on Fri Aug 25, 4:30pm
06 (Aug 28)	A: ; B:		Bivariate dependencies and relationships, transformations to linearise	Unwin (2015) Graphical Data Analysis Ch 5; Wilke (2019) Ch 12 Visualising associations
07 (Sep 4)	A: ; B:		Making comparisons between groups and strata	Wilke (2019) Ch 9, 10.2-4, 11.2; Unwin (2015) Graphical Data Analysis Ch 10
08 (Sep 11)	A: ; B:		Going beyond two variables, exploring high dimensions	Unwin (2015) Graphical Data Analysis Ch 6; Cook and Laa (2023) Interactively exploring high-dimensional data and models in R Chapter 1	Assignment 2 (individual) due on Fri Sep 15, 4:30pm
09 (Sep 18)	A: ; B:		Exploring data having a space and time context Part I	Reintroducing tsibble: data tools that melt the clock; brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R; Listen to Nick talking about longitudinal data; Unwin (2015) Graphical Data Analysis Ch 11
Mid-semester Break (1 week) - no lectures or tutorials
10 (Oct 2)	A: ; B:		Exploring data having a space and time context Part II	Moraga (2019) Spatial data and R packages for mapping; cubble: A Vector Spatio-Temporal Data Structure for Data Analysis; Making maps plot faster Simplify spatial polygons; sf: Simple Features for R	Assignment 3 (part 1) due on Fri Oct 6, 4:30pm
11 (Oct 9)	A: ; B:		Sculpting data using models, checking assumptions, co-dependency and performing diagnostics	Cook & Weisberg (1994) An Introduction to Regression Graphics Ch 6; Cleveland (1993) Visualising Data Ch 4; ; How to use a tour to check if your model suffers from multicollinearity
12 (Oct 16)			Extending beyond the data, what can and cannot be inferred more generally, given the data collection		Assignment 3 (part 2) due on Fri Oct 27, 4:30pm

Expectations

• The moodle discussion forum is appropriate for ALL questions related to the course. Only queries about a personal issue, a confidential or an administrative matter, will be addressed by direct email to ETC5521.Clayton-x@monash.edu.
• Email should not to be sent to individual instructor (lecture or tutor) addresses for any reason, and will not receive a response.
• Lectures will be recorded, and you are expected to have either attended the lecture, or watched the recordings fully, prior to the tutorial.
• Tutorials are not recorded, and attendance is required.
• It is your responsibility to catch up with any missed material, learn about due dates for material to be turned in, and getting assigned to a group for team work, as necessary.
• Bring your own laptop to the computer labs. If you need a laptop it is possible to get a loan from the Monash library and Monash can help by providing discounts or scholarships to purchase your own equipment.
• The computer software R and the RStudio IDE will be used for the unit, with reports produced using Rmarkdown or quarto. A good working knowledge of git and GitHub is assumed.
• We strongly encourage that all of your work is stored in your GitHub account, in a repo created for this unit. This will ensure that you have access if you need to borrow a laptop, or if your own laptop crashes. The class assignments will be done using Github classroom to enable collaborative work.
• Working collaboratively is designed to be enrich your learning, but it can be challenging. We encourage you to read Monash’s guide before starting your first group assignment.
• Students are encouraged to use Generative AI, but it needs to be acknowledged following the Monash guide. The full script of the conversation and responses needs to be included as an Appendix on your submitted assessment.

Learning outcomes

On successful completion of this unit, you should be able to:

1. learn to use modern data exploration tools with real data to uncover interesting structure and unusual observations

2. understand how to map out appropriate analyses, and to define what we would expect to see in the data

3. be able to compute null samples in order to test apparent patterns, and to interpret the results of visual inference

4. critically assess the strength and adequacy of a data analysis.