Data Science Group Project 2 - 2023 entry

This module gives you further practical experience of real data science projects and an opportunity to apply the techniques you have learned in other modules to solve a real, complicated and messy data science problem. In a team, you will help define and specify a data science problem, understand the client’s requirements and the available data sources. Your team, guided by a supervisor, will then use methods from other modules and new techniques to solve the problem and report your findings. An important aspect of the project will be coping with the common problem of missing and erroneous data.

This module aims to equip you to work on practical data science projects from start to finish: understanding the problem, selecting data and methods to solve it, wrangling the data, dealing with missing and erroneous data and reporting your results. It also aims to develop your soft skills in the areas of problem definition and presentation skills.

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Capture requirements from an external “customer”;

2 ”Wrangle” data into a form suitable for machine learning algorithms;

3 Understand mechanisms of missing data and be able to compensate appropriately;

4 Select and apply suitable machine learning algorithms;

Discipline Specific Skills and Knowledge:

5 Choose and use an appropriate research and development process;

6 Read and understand new technical methods;

7 Work as a member of a research and development team, participating in self-evaluation and peer review;

Personal and Key Transferable / Employment Skills and Knowledge:

8 Understand and apply legal, social and ethical principles;

9 Present your work to specialist and non-specialist audiences;

10 Tackle a technical problem in a new area.

• Students will work in teams, meeting their Supervisor weekly;
• The initial weeks will include lectures on the following topics, as appropriate:
  • Introduction to the project;
  • Missing data mechanisms and methods to cope with it;
  • Technical material related to the project;
  • Presentation skills;
  • Writing effective reports.

Reassessment will be by coursework in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.

Basic Reading:

ELE: http://vle.exeter.ac.uk/

Reading list for this module: