Chapter 2 Schedule
This is the schedule for the course. You can check itsLearning for the dates, times and locations.
Also check itsLearning for details of other tasks/assignments/reading etc.
Part | Session | Type | Topic | Instructor |
|---|---|---|---|---|
Intro | 1 | Lecture | Overview and philosophy | OJ |
Intro | 2 | Lecture | Hypotheses and questions | OJ |
Intro | 3 | Lecture | Getting acquainted with R | OJ |
Intro | 4 | Practical | An R refresher | OJ |
Data handling | 5 | Lecture | Data wrangling | OJ |
Data handling | 6 | Practical | Data wrangling with dplyr | OJ |
Data handling | 7 | Lecture | Data management | OJ |
Data handling | 8 | Practical | Exercise: Wrangling the Amniote Life History Database | OJ |
Data handling | 9 | Practical | Combining data sets | OJ |
Data handling | 10 | Practical | Exercise: Temperature effects on egg laying dates | OJ |
Data visualistion | 11 | Lecture | Visualising data with ggplot2 | OJ |
Data visualistion | 12 | Practical | Visualising data with ggplot2 | OJ |
Data visualistion | 13 | Lecture | Summary statistics, distributions and probability | OJ |
Data visualistion | 14 | Practical | Distributions and summarising data; Exercise: Virtual dice | OJ |
Data visualistion | 15 | Practical | Pimping your plots | OJ |
Data visualistion | 16 | Practical | Pimping your plots | OJ |
Statistics | 17 | Lecture | Randomisation tests | OJ |
Statistics | 18 | Practical | Exercise: Sexual selection in Hercules beetles | OJ |
Statistics | 19 | Practical | t-tests | OJ |
Statistics | 20 | Practical | t-test in R (and exercises) | OJ |
Statistics | 21 | Lecture | ANOVA | OJ |
Statistics | 22 | Practical | ANOVA in R (and exercise Apple tree crop yield) | OJ |
Statistics | 23 | Lecture | Linear Regression | OJ |
Statistics | 24 | Practical | Linear Regression in R | OJ |
Statistics | 25 | Lecture | ANCOVA and multiple regression | OJ |
Statistics | 26 | Practical | ANCOVA : Linear models with categorical and continuous explanatory variables | OJ |
Statistics | 27 | Lecture | Evaluating models | OJ |
Statistics | 28 | Practical | n-way ANOVA: Linear models with >1 categorical explanatory variables | OJ |
Statistics | 29 | Lecture | GLMs with count data | OJ |
Statistics | 30 | Practical | Generalised linear models in R | OJ |
Statistics | 31 | Lecture | GLMs with binomial data | OJ |
Statistics | 32 | Practical | Binomial GLMs in R | OJ |
Statistics | 33 | Lecture | Statistical power | OJ |
Statistics | 34 | Practical | Power analysis by simulation | OJ |
Statistics | 35 | Lecture | AI/ChatGPT workshop | OJ |
Statistics | 36 | Practical | AI/ChatGPT workshop | OJ |
Statistics | 37 | Lecture | Intro to Written Assignment | OJ |
Statistics | 38 | Practical | Written Assignment 2 | OJ |
Assignment | 39 | Practical | Written Assignment 3 | OJ |
Assignment | 40 | Practical | Written Assignment 4 | OJ |
Assignment | 41 | Practical | Written Assignment 5 | OJ |
Assignment | 42 | Practical | Written Assignment 6 | OJ |
Assignment | 43 | Practical | Written Assignment 7 | OJ |
Assignment | 44 | Practical | Written Assignment 8 | OJ |
Exam | 45 | Exam | MCQ Exam | OJ |
Exam | 46 | Exam | MCQ Exam | OJ |