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MODULE TITLE	Data Science Group Project 1	CREDIT VALUE	15
MODULE CODE	COM1012	MODULE CONVENER	Dr Massimo Stella (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS	0	11	0

Number of Students Taking Module (anticipated)	30

DESCRIPTION - summary of the module content

This module introduces you to the practicalities of a real data science project. 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 part of the project will be aspects of “data wrangling” – understanding the data and getting it into a form suitable for machine learning algorithms.

AIMS - intentions of the module

This module aims to give you experience of working on a practical data science project in the round, understanding the problem, selecting data and methods to solve it, wrangling the data and reporting your results. It also aims to develop your soft skills in the areas of problem definition and presentation skills.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

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

Module Specific Skills and Knowledge:

1 Capture requirements from an external “customer”;

2 Understand data, particularly text, representations and encodings;

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

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.

SYLLABUS PLAN - summary of the structure and academic content of the module

- 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;

• Data wrangling;

• Technical material related to the project;

• Presentation skills;

• Writing effective reports

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	23.00	Guided Independent Study	127.00	Placement / Study Abroad	0.00

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning and Teaching Activities	8	Introductory Lectures
Scheduled Learning and Teaching Activities	12	Weekly project review meetings
Scheduled Learning and Teaching Activities	3	Project presentations
Guided Independent Study	127	Independent study

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Not Applicable

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Presentation of project plan and preliminary data analysis	40	5-minute pitch deck presentation	1,2, 4-8	Written using customised marksheet
Coursework: Final report	60	15-page group project + 3-page personal essays outlining individual work on data and evaluating project impact	All	Written using customised marksheet

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
Presentation of project plan	Pre-recorded presentation of project plan	1,2, 4-8	August ref/def period
Final Report	Final Report	All	August ref/def period

RE-ASSESSMENT NOTES

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.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic Reading:

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

There is no standard reading list for this module.

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	COM1011
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Friday 12 April 2019	LAST REVISION DATE	Monday 23 January 2023

KEY WORDS SEARCH	Group Project; Data Science

COM1012 - Data Science Group Project 1 (2023)