Unit name | SWBio DTP: Data Science and Machine Learning for the Biosciences |
---|---|
Unit code | BIOCM0022 |
Credit points | 20 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Barker |
Open unit status | Not open |
Pre-requisites |
None |
Co-requisites |
BIOCM0010 SWBio DTP: Statistics and Bioinformatics, BIOCM0013 SWBio DTP: Science in Society, Business and Industry, BIOCM0021 SWBio DTP: Rotation Project 1, BIOCM0020 SWBio DTP: Rotation Project 2 |
School/department | School of Biochemistry |
Faculty | Faculty of Life Sciences |
The key aim of this unit is to introduce and familiarise doctoral students with the basics of coding, machine learning and general principles of data science as applied in the analysis of data from the biosciences. It is assumed that students will have minimal previous experience with coding (but noting that they will have made limited usage of R in the co-requisite BIOCM0010 unit which is taken prior to this new unit). By the end of the unit it is anticipated that students will be able to complete a short coding project manipulating data of relevance to their doctoral research studies.
The specific aims of this unit are:
This unit will have an intensive one week of teaching, comprising lectures, workshops, practical activities including some small-group activities. This will be followed by recommended- and self-directed study, to prepare the student for the various assessment activities.
This is a pass/fail unit, with each individual assessment being assessed using the pass/fail criteria.
There will be 2 assessments:
(1) A short group project on day 3, including a verbal presentation to the whole cohort and to which all group members will need to contribute (30%), and
(2) an individual short project, involving development of simple software for elementary analysis of a large data set from their area of doctoral research (70%) (of which 50% is submission of a functioning code along with a documented log of debugging steps and 50% for a short, written summary of their code and the main outcomes from their analysis).
Suite of online training modules for Python:
https://milliams.gitlab.io/beginning_python
Python Essential Reference, 4th Edition David Beazley. Addison-Wesley Professional (July 19, 2009)
ISBN 0672329786.
Learning Python, 5th Edition Mark Lutz, O’Reilly Publishing (June 2013) ISBN 978-1-449-35573-9.