- Lecturer: Professor Di Cook
- Tutors: Stuart Lee, Priyanga (Dilini) Talagala, Thiyanga Talagala, Nathaniel Tomasetti, Earo Wang

- Lectures: (R4) M 9-10, F 12-1
- Labs:
- Tue (E157) 4:00-5:30 (Earo)
- Wed (E157) 1-2:30 (Nat), 2:30-4:00 (Nat), 4:00-5:30 (Dilini)
- Thu (E157) 8:00-9:30 (Earo), 9:30-11:00 (Thiyanga), 11:00-12:30 (Stuart), 12:30-2:00 (Stuart)
- Fri (E163) 8:00-9:30 (Thiyanga), (E160) 9:30-11:00 (Dilini)

- Consultation times:
- Dilini M 2-3 (W1105)
- Thiyanga Tu 9-10 (W1105)
- Earo Th 9:30-10:30 (W1106)
- Nat M 10-11 (W1105)
- Stuart Th 3-4:30 (W1105)
- Di W 2-4 (E762A)

- Week 1: Introduction and motivation Slides 1 (Rmd); Slides 2 (Rmd);
**Reading:**Yihui’s fun package, Textbook: Chapter 1. 1.1-1.5 - Week 2: Games Slides 1 (Rmd); Slides 2 (Rmd); Videos: 1, 2, 3, 4, 5, 6, 7; Textbook Reading: Chapter 2.1-2.4
- Week 3: Hypothesis testing Slides 1 (Rmd) Slides 2 (Rmd); Reading: Data Scientistâ€™s Crib Sheet, Gallery of distributions, Distributions for Actuaries
- Week 4: Distribution theory; Slides 1 (Rmd) Slides 2 (Rmd) Data science explanation of MLE PSU Stat 414 olympics2012.csv olympics2008.csv olympics2004.csv gdp2016.csv olympics_gdp_all.csv
- Week 5: Linear models: Slides 1 (Rmd), Slides 2 (Rmd); Reading: Chapter 5 of textbook, linear models
- Week 6: Bootstrap intervals (Rmd); Generalised linear models (Rmd); Reading: Reading: Chapter 4.5 of textbook (its a bit misleading, though), and A really simple explanation, wikipedia page on bootstrap
- Week 7: Mixed effects models (Rmd), Models by partitioning (Rmd); Reading: Fitting linear mixed effects models sections 1, 2 and 5
- Week 8: Ensemble models with bootstrapping(Rmd) and boosting(Rmd); Reading: Introduction to Recursive Partitioning, Gentle intro to boosting; tennis_2012_wta_fixed.csv
- Week 9: Bayesian inference(Rmd)); Reading: Conditional probability and Bayesian methods, Intro to Bayesian methods, A visual guide to Bayesian thinking, The Odds, Continually Updated, Think Bayes
- Week 10: Data wrangling 1(Rmd) Data wrangling 2(Rmd); Reading: Tidy data
- Week 11: Data wrangling 3(Rmd); Reading: Handling dates
- Week 12: Project presentations

- Week 1: Setting up your computing environment (Rmd) SOLUTION (Rmd); Tutorial slides (Rmd)
- Week 2: Making a Monty Hall game (Rmd); Tutorial slides SOLUTION (Rmd); Tutorial slides
- Week 3: Hypothesis testing (Rmd) SOLUTION (Rmd); Tutorial slides (Rmd)
- Week 4: Fitting distributions (Rmd) SOLUTION (Rmd); Tutorial slides (Rmd) usworkcomp.rda
- Week 5: Linear models (Rmd) SOLUTION (Rmd); Tutorial slides (Rmd)
- Week 6: Diagnosing models (Rmd); SOLUTION (Rmd) Tutorial slides (Rmd) pisa_au.rda
- Week 7: Bootstrap intervals (Rmd); SOLUTION (Rmd); Tutorial slides (Rmd)
- Week 8: Tree models (Rmd); SOLUTION (Rmd); Tutorial slides (Rmd) melb_auctions.csv
- Week 9: Bayesian Thinking (Rmd); SOLUTION (Rmd); Tutorial slides (Rmd); Nat’s shiny app
- Week 10: Working with Data (Rmd); SOLUTION (Rmd) ; melb_ghcn.csv; Tutorial slides (Rmd)
- Week 11: Modeling Risk and Loss with Data (Rmd); SOLUTION (Rmd) ; melb_ped_weather.csv; Tutorial slides (Rmd)

Note that it was open book, 3 hours, and worth 70%. This year’s exam is 2 hours long, closed notes and worth 60%.

Please just use these to help you study, and don’t distribute them to next year’s students.

- R (Friday 2017-06-30, Single Candle) R-3.4.1, install on your computer from https://cran.r-project.org
- RStudio Desktop 1.0.143, install on your computer from https://www.rstudio.com/products/rstudio/download/
- Packages will be installed as we need them