Mathematics

MTHM504 - Applied Data Science and Statistics Project (2019)

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MODULE TITLEApplied Data Science and Statistics Project CREDIT VALUE60
MODULE CODEMTHM504 MODULE CONVENER Dorottya Fekete (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 12 7
Number of Students Taking Module (anticipated) 15
DESCRIPTION - summary of the module content

In this module, you will work on a research problem in the application of Data Science and Statistics. You will apply your understanding of the underlying concepts of Data Science and Statistics together with the methods and tools that you have learned to a problem in an applied field. The project will require understanding of the setting, a critical review of possible approaches, choice of appropriate methodology, an extended piece of data analysis and a clear and concise write up of the background, data, methodology, results and conclusions. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation, describing your research and its results. Research topics can be selected from across the breadth of the application of Data Science and Statistics.

AIMS - intentions of the module

This module aims to give you in-depth experience of applying Data Science and Statistics to real-world problems, preparing you for work in a commercial setting or further post-graduate work. The module aims to build on the knowledge and skills you have acquired in the taught modules of the programme to allow you to investigate an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis, interpretation and presentation of results.

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 of relevance to the MSc Applied Data Science and Statistics programme, 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 Apply methods and tools learnt in the context of other fields to the application in question;

6 Produce full documentation as appropriate to the system and research;

Personal and Key Transferable/ Employment Skills and Knowledge:

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

8 Plan an extended project and manage time effectively;

9 Present work to a non-specialist audience;

10 Report writing and presentation.

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

Not applicable.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 20.00 Guided Independent Study 580.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 20 Project supervision
Guided Independent Study 580 Individual assessed work

 

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
Two-page project proposal in early stages of project 2 pages All Oral

 

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
Coursework – Mid-Point Presentation 10 10 mins 1, 4, 9 Written
Coursework – Final Presentation 20 20 mins 1, 4, 9 Written
Coursework – Dissertation 70 30-40 pages 2-10 Written

 

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
Coursework – Mid Point Presentation Coursework (100%) All To be agreed by consequences of failure meeting
Coursework – Final Presentation Coursework (100%) All To be agreed by consequences of failure meeting
Coursework – Dissertation Coursework (100%) All To be agreed by consequences of failure meeting

 

RE-ASSESSMENT NOTES
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 60 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 17 June 2019 LAST REVISION DATE Tuesday 17 September 2019
KEY WORDS SEARCH None Defined