Data Science
Course arrangements
- Lecture notes
– Parts I & II as printed
– Parts III & IV as printed
These printed notes have some sections marked *, which are non-examinable, and most of the * sections are omitted. The omitted sections can be found in the
– extended online notes — these are drafts, and editing is in progress. - Examinable material
– Everything in the videos, excluding the final video labelled 'Epilog'
– Everything in the lecture note printouts, excluding starred sections
– Methods used in the mock exam questions are examinable
– Methods used in the example sheets, excluding supplementary questions and coding walkthroughs
(The lecture notes and the videos cover exactly the same material. There are just some small differences in the examples I've worked through.) - Q&A You can ask questions in the Q&A forum on Moodle. I encourage you to work through the coding walkthroughs associated with the example sheets, as a way to really get to grips with the material. In case you don't have time to work on it in the middle of a busy term and want to return to these walkthroughs later, I'll monitor the Q&A forum until February.
- Schedule The table below shows what the lecture timetable would have been, were it not for COVID measures. It lists example sheets at the stage at which the material has been covered. Follow the course at your own pace, and use the lecture breakdown as a guide about how much work to expect and when to work on example sheets.
- Watching videos The videos have exactly the same material as a normal lecture, but they are short because there's no need for the lecturer to pause. You have a pause button — use it! You'll need to watch these videos several times, with the printed notes beside you. When there are handwritten equations in the videos, you should copy out the equations yourself to make certain you understand the method. When the video presents code, you should download the code and try it yourself. This will make the example sheets much easier. (The videos can also be found on the Modelling and Machine Learning YouTube playlist.)
- Supervisions
Your college should have arranged three supervisions for this course. The suggested breakdown is:
– Not for supervision: example sheet 0
– Supervision 1: example sheet 1
– Supervision 2: example sheet 2, and half of 3
– Supervision 3: half of example sheet 3, and example sheet 4