ETC5521: Diving Deeply into Data Exploration

Lecturer/Chief Examiner

  • Professor Di Cook
    • Email: etc5521.clayton-x@monash.edu
    • Consultation: TBA Clayton: Education Blg, its blg 6, 29 Ancora Imparo way, Room 352 and on zoom (see link in moodle)

Tutors

  • Krisanat Anukarnsakulchularp
    • Tutorials: 1-2pm, 2-3pm, 3-4pm CL_Anc-19.LTB_387
    • Consultation: TBA Clayton: Education Blg, its blg 6, 29 Ancora Imparo way, Room 232A

Weekly schedule

  • Lecture+workshop: Tues 9-12 pm in-person CAULFIELD B220 (also recordings in Moodle)
  • Tutorial: 1 hour
Week Topic Reference Assessments
27 Jul Overview. Why this course? What is EDA? The Landscape of R Packages for Automated Exploratory Data Analysis
03 Aug Learning from history EDA Case Study: Bay area blues
10 Aug Initial data analysis and model diagnostics: Model dependent exploration and how it differs from EDA The initial examination of data
17 Aug 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 Exercises 1
24 Aug Working with a single variable, making transformations, detecting outliers, using robust statistics Wilke (2019) Ch 6 Visualizing Amounts; Ch 7 Visualizing distributions
31 Aug Bivariate dependencies and relationships, transformations to linearise Wilke (2019) Ch 12 Visualising associations
07 Sep Making comparisons between groups and strata Wilke (2019) Ch 9, 10.2-4, 11.2 Mid-semester test
14 Sep Going beyond two variables, exploring high dimensions Cook and Laa (2023) Interactively exploring high-dimensional data and models in R Chapter 1
28 Sep Exploring data having a space and time context Part I brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R
05 Oct Exploring data having a space and time context Part II cubble: An R Package for Organizing and Wrangling Multivariate Spatio-temporal Data Project Part 1
12 Oct Sculpting data using models, checking assumptions, co-dependency and performing diagnostics How to use a tour to check if your model suffers from multicollinearity
19 Oct Project feedback Project Part 2

Assessments

  • Exercises 1: Instructions (25%) (Due Friday, 21 Aug 11:55pm)
  • Mid-semester test: Instructions (25%) (Due Monday, 7 Sep 10am-noon)
  • Project part 1: Instructions (25%) (Due Friday, 9 Oct 11:55pm)
  • Project part 2: Instructions (25%) (Due Friday, 23 Oct 11:55pm)

Software

We will be using the latest versions of R and RStudio.

If you are relatively new to R, working through the materials at https://startr.numbat.space is an excellent way to up-skill. You are especially encouraged to work through Chapter 3, on Troubleshooting and asking for help, because at some point you will need help with your coding, and how you go about this matters and impacts the ability of others to help you.

If you are new to version control using GitHub, the resources at ETC5513: Collaborative and Reproducible Practices, weeks 3-5, will help you get started.

Creative Commons License
These materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.