All consultations will be in using zoom. Check Moodle for the links.
There are no lectures or tutorials during the midsemester break.
|1||Di||A: ; B:||Overview. Why this course? What is EDA?||The Landscape of R Packages for Automated Exploratory Data Analysis||
|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||