All consultations will be in using zoom. Check Moodle for the links.
There are no lectures or tutorials during the midsemester break.
Week | Lecturer | Slides | Tutorial | Topic | Readings | Assessments |
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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 |
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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 |
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