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 Learning from history EDA Case Study: Bay area blues
3 Emi Initial data analysis: Model dependent exploration and how it differs from EDA The initial examination of data
4 Emi 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 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 Making comparisons between groups and strata Unwin (2015) Graphical Data Analysis Ch 10
7 Di Going beyond two variables, exploring high dimensions Wilke (2019) Ch 9 Visualising many distributions & Unwin (2015) Graphical Data Analysis Ch 6
8 Emi 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 Assignment 2 due on Fri 17th Sep 11.55pm
9 Di 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
Midsemester Break (1 week)
10 Di 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
11 Emi 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 Extending beyond the data, what can and cannot be inferred more generally, given the data collection
  • Group Presentation due on Mon 25th Oct 6.00pm
  • Assignment 3 due on Fri 29th Oct 11.55pm