| Unit name | Data Science Mini-Project |
|---|---|
| Unit code | EMATM0050 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Dave Cliff |
| Open unit status | Not open |
| Pre-requisites |
Software Development Programming and Algorithms or Large-Scale Data Engineering; Technology, Innovation, Business, and Society. |
| Co-requisites |
Introduction to Artificial Intelligence Visual Analytics. |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Students will draw on knowledge and skills acquired in other core units of the Data Science MSc programme, working in a small teams to design, implement, test, refine, and finally demonstrate a successful data science application. Whenever possible, the application will be created to meet the needs of an external client, e.g. in an industrial partner, or problem-owners elsewhere in the University. The primary aim of this unit is integrative: it provides students with a first opportunity to apply ideas and technical skills from all other units studied in the programme, tackling a genuine problem or challenge for which achieving a workable solution in the time available will require efficient division and management of work within each team.
Students will be able to
1. Demonstrate their ability for teamwork in developing a problem-solution in collaboration with
client or end-user while following a recognised software-development project management
method (e.g., Scrum).
2. Communicate effectively within the team and with external stakeholders.
3. Engineer code to production quality using recognised coding standards (e.g. PEP8).
4. Succinctly and coherently document their design decisions, clearly explaining their reasons for
choosing specific tools, services, production environments, testing regimes, and monitoring metrics.
Delivery will primarily be through project supervision coupled with a small number of technical lab classes for some practical elements.
This unit is assessed by coursework. The project task should involve all phases of the data science lifecycle
(e.g., loosely based on the CRISP-DM model). The outputs will be a group oral presentation/demonstration
(20%; ILO 4) and written report including code as an appendix and/or viewable in an online repository (65%;
ILO 1, 2, 3, 4) as well as an individual reflective account of the project experience and teamwork (15%; ILO
1,2). The assessment criteria will include technical merit, communication of the solution, team contribution,
and individual achievement.
· Marr, Bernard. Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions
and Improve Performance. John Wiley & Sons, 2015.
· Marr, Bernard. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to
Deliver Extraordinary Results. John Wiley & Sons, 2016.
· Provost, Foster and Fawcett, Tom. Data Science for Business: What you need to know about data-
mining and data-analytic thinking. O'Reilly, 2013.
· Stellman, Andrew and Greene, Jennifer. Learning Agile: Understanding Scrum, XP, Lean, and
Kanban. O'Reilly, 2013.
· Sutherland, Jeff. Scrum: The Art of Doing Twice the Work in Half the Time. Random House, 2015.
