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
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 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
12 (Oct 17) Michael A: ; B: 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