ETC5521: Exploratory Data Analysis

Lecturers

• Prof Di Cook (Chief Examiner)
• Dr Michael Lydeamore

Tutor

• Brendi Ang

Consultations

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

• Prof Di Cook: Wed 1:30-2:30 on zoom
• Dr Michael Lydeamore: Wed 1:30-2:30 on zoom
• Brendi Ang: Wed 10:00-11:30 on zoom

Tentative Schedule

• Lecture: Thu 10:00-12:00 on zoom (see Moodle for link)
• Tutorials: one of the following
  • Fri 10.00-11:30am CL_19 Ancora Imparo Way, Room 221 - LTB (Bldg 92) Extra details
  • Fri 2:00-3:30pm Online (See Moodle for zoom link) Extra details

There are no lectures or tutorials during the midsemester break.

Week	Lecturer	Slides	Tutorial	Topic	Readings	Assessments
00	Di	A:		Course information
01 (Jul 25)	Di	A: ; B:		Overview. Why this course? What is EDA?	The Landscape of R Packages for Automated Exploratory Data Analysis	Reading quizzes due each week.
02 (Aug 1)	Di	A: ; B:		Learning from history	EDA Case Study: Bay area blues
03 (Aug 8)	Di	A: ; B:		Initial data analysis: Model dependent exploration and how it differs from EDA	The initial examination of data
04 (Aug 15)	Michael	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 7 Visualising distributions;
05 (Aug 22)	Michael	A: ; B:		Bivariate dependencies and relationships, transformations to linearise	Unwin (2015) Graphical Data Analysis Ch 5; Wilke (2019) Ch 12 Visualising associations	Assignment 1 due on Fri 26th Aug 4:30pm
06 (Aug 29)	Michael	A: ; B:		Making comparisons between groups and strata	Wilke (2019) Ch 9 Visualising many distributions; Unwin (2015) Graphical Data Analysis Ch 10
07 (Sep 5)	Di	A: ; B:		Going beyond two variables, exploring high dimensions	Unwin (2015) Graphical Data Analysis Ch 6; tourr: An R Package for Exploring Multivariate Data with Projections; How to use a tour to check if your model suffers from multicollinearity
08 (Sep 12)	Di	A: ; B:		Exploring data having a space and time context Part I	Reintroducing tsibble: data tools that melt the clock; Unwin (2015) Graphical Data Analysis Ch 11
09 (Sep 19)	Di	A: ; B:		Exploring data having a space and time context Part II	Healy (2018) Data Visualization, Chap 7, Draw maps; Perpinan Lamigueiro (2018) Displaying Time Series, Spatial and Space-Time Data with R	Assignment 2 due on Fri 23rd Sep 4:30pm
Midsemester Break (1 week)
10 (Oct 3)	Michael	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
11 (Oct 10)	Michael			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 (Oct 17)	Michael			Extending beyond the data, what can and cannot be inferred more generally, given the data collection		Group Presentation due on Mon 24th Oct 4:30pm Assignment 3 due on Fri 28th Oct 4:30pm

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 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, with reports produced using Rlmarkdown. A good working knowledge of git and github is assumed.