| Activity | Day | Time | Location |
|---|---|---|---|
| Lecture | Tuesday | 09:00 – 11:00 | 23WW T2 Lecture Theatre |
| Tutorial | Tuesday | 12:00 – 14:00 | 12SW 318 Tutorial/Computer Room |
| Tutorial | Tuesday | 14:00 – 16:00 | 12SW 318 Tutorial/Computer Room |
| Tutorial | Wednesday | 09:00 – 11:00 | 12SW 318 Tutorial/Computer Room |
| Tutorial | Wednesday | 11:00 – 13:00 | 12SW 318 Tutorial/Computer Room |
| Tutorial | Thursday | 13:00 – 15:00 | 12SW 318 Tutorial/Computer Room |
| Tutorial | Thursday | 15:00 – 17:00 | 12SW 318 Tutorial/Computer Room |
| Tutorial | Friday | 13:00 – 15:00 | 12SW 318 Tutorial/Computer Room |
| Tutorial | Friday | 15:00 – 17:00 | 12SW 318 Tutorial/Computer Room |
| WEEK | TOPIC |
|---|---|
| Week 1 | Introduction to data, analysis, and R programming |
| Week 2 | Introduction to data, analysis, and R programming |
| Week 3 | Introduction to data, analysis, and R programming |
| Week 4 | Introduction to data, analysis, and R programming |
| Week 5 | Null Hypothesis Significance Testing (NHST) |
| Week 6 | Null Hypothesis Significance Testing (NHST) |
| Week 7 | Null Hypothesis Significance Testing (NHST) |
| Week 8 | Null Hypothesis Significance Testing (NHST) |
| Week 9 | Linear models and inference: model fitting and model comparison |
| Week 10 | Linear models and inference: model fitting and model comparison |
| Week 11 | Linear models and inference: model fitting and model comparison |
| Week 12 | Linear models and inference: model fitting and model comparison |
| Week 13 | Preparing for the final exam |
Lectures and tutorials will be delivered on campus in the rooms indicated above unless special circumstances require otherwise.
There will be no livestream of the lectures. However, the lectures will be recorded and made available on iLearn.
Useful humans: By the end of the course, you will be fully prepared to grapple with the most common data exploration and analysis situations that occur in cognitive neuroscience. You will learn how to draw inferences from data and how to communicate those inferences to others.
Code everyday: Learning to program is very much like learning a new language crossed with learning a new motor skill. Daily practice and full immersion will help you immensely. There is no replacement for learning by doing. Semi-weekly problem sets and quizzes are designed to keep you motivated and on the hook.
Learn how to think: Intro courses can sometimes leave students simply consulting a cheat sheet of “apply test X in situation Y”. Ultimately, the needs of your future research will be unique. You will need to apply the general principles of statistics and programming to get the job done.
Learn how to teach yourself: The ultimate goal is for you to develop the ability to teach yourself more statistics and more programming. No matter how many courses you take, your research will eventually take you here. Might as well get started early.
In the spirit of the last bullet point, you may have to recognise when a programming or statistics concept has not been covered in lecture or tutorials in exactly the way a quiz question or homework problem is asking. This is a feature, not a bug. This is very important for you to develop deep and flexible knowledge of the material. However, pleae do not hesitate to come see me or your tutors for help.
25% quizzes
40% problem sets
35% final exam
Quizzes: 10 online quizzes (multiple choice and numeric response) designed to assess and provide feedback regarding your understanding of core programming and statistics concepts. Graded on a continuous 0%-100% scale. The 2 lowest quizzes (missed or marked non-credit) may be dropped without penalty. These will challenging and they will take time to complete. Please do not leave them to the last minute.
Problem sets: 4 problem sets will be released approximately 1 every three weeks. They will follow the tutorial activities closely and will culminate in a full statistical analysis and report of real cognitive neuroscience data.
Final exam: 2 hour exam using only pen and paper. The exam will occur sometime during the exam period as scheduled by MQ. Though the exam is pen and paper, it will nevertheless assess your R programming knowledge by asking you to interpret code blocks and read the output of statistical analyses performed in R.
Grading: Quizzes will be delivered and marked through iLearn. Problem sets and the final exam will be marked by hand. Where possible, we will give partial credit on these two assessment types.
Attendance: Attendance is not compulsory, but is highly recommended. This applies both to lecture and tutorial.