| Unit name | Perspectives in Data Science |
|---|---|
| Unit code | MATH20018 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Lawson |
| 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 |
Why is this unit important?
This course is excellent preparation for your future career, whether that be in big industry, startups, academia or education. You will use your data science background to develop your written and oral communication, understanding how technologies such as AI are changing how we communicate and work. You will experience working in groups and how to do this effectively, supporting each other through constructive peer feedback. The course provides options to learn about developing business ideas in data science that extends your knowledge.
Whatever your future destination, being able to communicate to others is likely to be key. You may need to communicate technical mathematics to non-mathematical colleagues or to enthuse young people about the excitement and beauty of mathematics. Most workplaces will require you to work in teams and expect you to be an effective group member. You will need to meet deadlines and manage your time effectively.
How does this unit fit into your programme of study?
This unit is a required unit for the Data Science BSc and Mathematics with Statistics BSc. The focus within this Unit is on developing a range of communication skills as well as learning to work effectively with others, through providing constructive feedback and working in groups. You will learn the practical skills considered essential for a data scientist, learn and practise the skills needed to develop a business proposal, and develop broad data-science skills spanning working collaboratively via remote tools, ways to view data science through the lens of ethics and the law.
An overview of content
The unit is composed of individual and group activities that enable you to become a rounded data scientist.
As you undertake (task 1) data science training in the principles of systematic data collection, management and curation, as well as (task 2) data ethics and (task 3) enterprise data science, your soft-skill training will include:
How will students, personally, be different as a result of the unit
You will know about different approaches to communication to non-technical and technical audiences and will be able to apply different communication techniques as appropriate to the audience. You will be able to understand how to effectively prompt an AI tool to communicate to a desired audience. You will know how to constructively critique work produced by an AI or a person. You will understand different team roles and what your own strengths can bring to
group working. You will be able to manage conflict within a team. You will know how to structure a business development proposal or will know more about an area of data science. You will be able to manage your time and meet deadlines.
Learning Outcomes
At the end of this Unit a successful student will be able to:
This Unit is coursework-assessed. There are three components within the Unit:
ILOs 4-6 are assessed through all tasks.
Tasks which help you learn and prepare you for summative tasks (formative):
Tasks which count towards your unit mark (summative):
When assessment does not go to plan
If group work does not go to plan you will have an individual assessment with the same character, i.e. written content will be replaced with written content, oral presentations by oral (presented to teaching staff). It will be reduced in content to match the workload expected per group-member. If you did not perform peer-assessment, you will be given a sample to perform this on and assessed by teaching staff. If you do not receive peer-assessment feedback, you will instead receive teaching staff feedback.
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 University 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. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
