Computer Science

COMM032 - Data Science MSci Individual Project (2019)

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MODULE TITLEData Science MSci Individual Project CREDIT VALUE30
MODULE CODECOMM032 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 11 11 1
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

In this module, you will analyse and work towards the solution of a selected research problem in Data Science. This is an individual, independent piece of work that will include aspects of research, analysis and implementation to solve the chosen problem. You will work with an individual supervisor and often you will interact with the supervisor’s research group in the area. The diversity of topics in data science and the methods that can be brought to bear on them means that there is a wide range of possible topics and the module provides an opportunity for an in-depth exploration of a topic of particular interest to you.

Pre-requisite Modules: COM3021 Data Science At Scale

AIMS - intentions of the module

This module builds upon the experience gained in the individual project at level 3, allowing you to conduct a more advanced project with a substantial research element. The module aims to put into practice the knowledge acquired from the taught elements of the programme and to give you experience of many aspects of research work, including literature review, planning, experimentation and analysis, interpretation of results, and presentation. You will also gain valuable experience in concisely presenting scientific results via the writing of an academic research paper. Presenting the results of commissioned research in a compact and clear form is an essential research skill in both industry and academia.

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 Demonstrate knowledge of a research topic in data science, acquired through a deep and self-motivated exploration of that topic;

2 Design and follow systematically the phases of research project development;

3 Apply sophisticated and appropriate analysis and development techniques at each stage of a project;

Discipline Specific Skills and Knowledge:

4 Show familiarity with the background and context of a new application area;

5 Produce a concise research article;

Personal and Key Transferable / Employment Skills and Knowledge:

6 Conduct independent study, including library and web-based research;

7 Reflect critically on processes and products;

8 Plan an extended project and manage your time effectively;

9 Present your work to a non-specialist audience.

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

- Students are expected to have weekly meetings with their supervisor and maintain a project log-book which will be handed in along with the final report and assessed as part of the supervisor’s report. Log-book entries should record the subjects discussed and actions agreed, and will be signed and dated by both student and supervisor;

- Students should attend relevant departmental and Institute of Data Science & AI seminars;

- The final report should be written in the style of a research paper suitable for a conference or journal appropriate for the research topic;

- 10% of the marks for the module will come from your supervisor’s report, which will take into account your progress throughout the year, including attendance at meetings and the oral presentation, submission of agreed deliverables including the log-book, demonstration of ambition and initiative.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 32.00 Guided Independent Study 268.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 12 Lectures, Seminars
Guided Independent Study 20 Supervision
Guided Independent Study 268 Self-Study and Background Reading

 

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
Project Outline Talk 20 minutes 1,2,4, 6-8 Oral commentary from supervisor and written feedback using customised marksheet

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 90 Written Exams 0 Practical Exams 10
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Literature Review and Plan 20 10 pages 4.5 Written using customised marksheet
Final Report 60 30 pages All Written using customised marksheet
Supervisor’s Report 10 N/A 2,6,7,8 Oral feedback from supervisor
Demonstration and Viva 10 30 minutes 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
All Above Coursework (100%) All Completed over summer with a deadline in August

 

RE-ASSESSMENT NOTES

Referred and deferred assessments will normally be by a single assignment together with a demonstration/viva. Deferred students will retain marks from components passed. 

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/

Reading list for this module:

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

CREDIT VALUE 30 ECTS VALUE 15
PRE-REQUISITE MODULES COM2013, COM3021
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 12 April 2019 LAST REVISION DATE Monday 19 August 2019
KEY WORDS SEARCH Research Project; Literature Review; Data Science; Machine Learning; Statistics; Data Governance; Data Visualisation; Data Exploration