| Unit name | Perspectives in Data Science |
|---|---|
| Unit code | MATH20018 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
| Unit director | Professor. Snaith |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
NONE |
| Units you must take alongside this one (co-requisite units) |
NONE |
| Units you may not take alongside this one |
NONE |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
Lecturers: Nina Snaith and Jayne Stansfield
This course is excellent preparation for your future career, whether that be in big industry, start-ups, academia or education. You will use your maths background to develop your written and oral communication, create independent work and support peers in group work. The unit will also provide you with a broad grounding in the ethical and legal implications involved in the use of personal data.
The unit aims to prepare students for the corporate world and the specificities linked to the use of personal data and algorithms in a commercial environment.
Overview of content:
In more detail, the unit comprises three periods of activity. For the first task you will write a short article on a mathematical topic for a target audience, for example writing for the general public. As a group you will also produce a video on a selected topic. For the second task you will pursue guided reading of relevant sources on the topic of ethics in Data Science, which will lead to a presentation to your peers.
The third part of the course will involve you working in a group to undertake substantial research into a new area focussing upon one of two approaches. In one option you will have the freedom to explore a maths subject of your own choice, working collaboratively with staff and fellow students, culminating in presenting to your peers what you have learned. In the other option you will work in small groups collaboratively with your peers to generate and evaluate a business case for a new company based upon a mathematical idea, learning the skills to write business plans and present to working entrepreneurs. In both options, these skills will help you stand out from your competition, wherever your career leads you.
Every aspect of this unit is based on collaboration with peers. Even where tasks are undertaken independently, students review and give feedback on each other’s work. If you engage fully with this you will emerge with an improved sense of collegiality and the benefit of sharing a diverse range of skills and opinions - this is in addition to improving your written and oral communication skills. Students will have developed an awareness of the issues surrounding the use of personal data and algorithms to analyse it.
Learning outcomes:
This unit is very interactive, and we expect students to attend all scheduled sessions. Students are grouped for all tasks, including individually assessed tasks, and the scheduled sessions often involve exchanging ideas or giving advice to peers. Most sessions involve a part delivered by the lecturer and then a practical discussion or activity carried out within student groups.
Formative tasks: For two of the assignments, you will submit a draft of your work to peers and receive formal, structured feedback before completing the final work for assessment. In addition, in all aspects of the unit you work in groups and can always gain informal feedback and advice from peers or lecturers.
Summative tasks:
Writing mathematics article 10%
Peer review of other students’ articles 5%
Creating a mathematical video 15%
Presentation on aspects of ethics in the context of Data Science 20%
Group research/enterprise project report 30%
Group research/enterprise project presentation 20%
Reassessment: Students who fail the unit or have not completed the assessment will be given reassessment coursework tasks for any component that they have not completed or failed.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. MATH20018).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the Faculty workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
