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Unit information: Data Science Mini-Project in 2021/22

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing, student choice and timetabling constraints.

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 Mr. Forsyth
Open unit status Not open
Pre-requisites

EMATM0048 Software Development: Programming and Algorithms or EMATM0061 Statistical Computing and Empirical Methods;

EMATM0051 Large-Scale Data Engineering;

EMATM0049 Technology, Innovation, Business, and Society.

Co-requisites

None

School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

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 or exploratory/proof-of-concept project.

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.

Intended learning outcomes

On successful completion of the unit, students will be able to:

  1. Demonstrate their ability for teamwork in developing a data science application or exploration in collaboration with client or end-user, preferably while following a recognised software-development project management method (e.g., Scrum).
  2. Communicate effectively within the team and with external stakeholders.
  3. Deliver a workable proof-of-concept system (code, scripts, data, analytics, and a written report) that addresses the needs of the client/end-user.
  4. Succinctly and coherently document their design decisions, clearly explaining their reasons for choosing specific tools, services, production environments, testing regimes, and monitoring metrics.
  5. Use online repositories such as GitHub and associated tools, for version control and collaborative working.

Teaching details

Delivery will primarily be through project supervision coupled with a small number of technical lab classes for some practical elements.

Assessment Details

This unit is assessed by coursework. 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, 5) 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.

Reading and References

  • Loeliger, Jon and MCullough, Matthew. Version Control with Git: Powerful tools and techniques for collaborative software development. O'Reilly, 2012.
  • 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.

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