Lecturers

Tutors

Consultations

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

Tentative Schedule

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? The Landscape of R Packages for Automated Exploratory Data Analysis
  • Reading quizzes due each week.
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
5 Di A: ; B: Bivariate dependencies and relationships, transformations to linearise Wilke (2019) Ch 7 Visualising distributions & Unwin (2015) Graphical Data Analysis Ch 5 Assignment 1 due on Fri 27th Aug 11.55pm
6 Emi A: ; B: Making comparisons between groups and strata Wilke (2019) Ch 12 Visualising associations &Unwin (2015) Graphical Data Analysis Ch 10
7 Di A: ; B: Going beyond two variables, exploring high dimensions Wilke (2019) Ch 9 Visualising many distributions
8 Emi A: ; B: Sculpting data using models, checking assumptions, co-dependency and performing diagnostics 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 Assignment 2 due on Fri 17th Sep 11.55pm
9 Di A: ; B: Exploring data having a space and time context Part I Cook & Weisberg (1994) An Introduction to Regression Graphics Ch 6 and Cleveland (1993) Visualising Data Ch 4
Midsemester Break (1 week)
10 Di Exploring data having a space and time context Part II Reintroducing tsibble: data tools that melt the clock; Unwin (2015) Graphical Data Analysis Ch 11
11 Emi Using computational tools to determine whether what is seen in the data can be assumed to apply more broadly Healy (2018) Data Visualization, Chap 7, Draw maps; Perpinan Lamigueiro (2018) Displaying Time Series, Spatial and Space-Time Data with R
12 Emi Extending beyond the data, what can and cannot be inferred more generally, given the data collection Wickham et al. (2010) Graphical inference for Infovis
  • Group Presentation due on Mon 25th Oct 6.00pm
  • Assignment 3 due on Fri 29th Oct 11.55pm

Expectations