All consultations will be in using zoom. Check Moodle for the links.
There are no lectures or tutorials during the midsemester break.
|01 (Jul 25)||Di||A: ; B:||Overview. Why this course? What is EDA?||The Landscape of R Packages for Automated Exploratory Data Analysis||
|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||