DATA9003 - Research Project -Data Science

Module Details

Module Code: DATA9003
Title: Research Project -Data Science
Long Title: Research Project -Data Science
NFQ Level: Expert
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 30
Field of Study: 4816 - Data Format
Module Delivered in: 1 programme(s)
Module Description: In this module, the learner will undertake a research project in a specialised area of data science. The module will develop the learner's knowledge, skills, and competences required to research, develop and scope a data science project, and to successfully complete it in accordance with an approved plan. The learner is expected to be self motivated whilst working under direction of a project supervisor, and to communicate the process and outcomes of their work on a regular basis. The learner will disseminate their research work and findings via a written thesis and an oral presentation.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Undertake a data science research project in a specialised area.
LO2 Conduct a literature review of the up-to-date methodologies and techniques appropriate to the specified area of research.
LO3 Research and detail appropriate and effective objectives and final deliverables for a data science/analytics project. Conduct a feasibility study and plan for the project.
LO4 Systematically review and adapt the employed data science methodologies during implementation in response to practical, real-world data considerations and constraints.
LO5 Produce a final written thesis detailing the work undertaken, methodologies used, findings and recommendations of the research work.
LO6 Present the project findings using appropriate presentation and visual communication skills.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
13331 MATH9001 Research Methods
13336 COMP9060 Applied Machine Learning
13801 STAT8010 Intro to R for Data Science
13803 DATA9005 Data Analytics & Visualisation
13896 COMP8060 Scientific Prog in Python
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Literature Survey
Gather, critically analyse research and other appropriate literature in the relevant area of data science and analytics.
Research Question
Formulate the core research question, identifying data set(s) relevant to the chosen data science/analytics application area.
Methodology Development
Formulate and assess viable methodologies and technologies to address the identified research question with a view to identifying the most appropriate methodologies and technologies.
Project Implementation
Design and implement a data science solution using supervised self-directed learning and utilising the researched data science methodologies.
Written Report
Write a thesis that details the project work, the research question, the methodologies used, the findings and recommendations arising from the research.
Oral Presentation
Present the project research findings in person/video; this will include an in-depth question and answer session. Design a visual presentation detailing the project and its main findings using appropriate data visualisation techniques.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Reflective Journal % of Total Mark 10
Timing Every Second Week Learning Outcomes 1,2,3,4
Assessment Description
Professional reports outlining the project process in the different stages of the research.
Assessment Type Other % of Total Mark 10
Timing Every Week Learning Outcomes 1,2,3,4,5,6
Assessment Description
Methods of Working: Mark associated with initiative, decision making, application and demonstration/performance of project outcomes.
Assessment Type Written Report % of Total Mark 60
Timing Sem End Learning Outcomes 1,2,5
Assessment Description
Submit a written thesis detailing the research question, methodologies used, project findings and recommendations.
Assessment Type Presentation % of Total Mark 20
Timing Sem End Learning Outcomes 6
Assessment Description
Create a presentation describing the project and its main research findings and results. This will include an oral/video presentation of the project and research findings, followed by an in-depth question and answer session.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecturer-Supervised Learning (Contact) Contact Interaction with project supervisor Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Independent research and project work Every Week 41.00 41
Total Hours 42.00
Total Weekly Learner Workload 42.00
Total Weekly Contact Hours 1.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecturer-Supervised Learning (Contact) Contact Interaction with Project Supervisor Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Independent research and project work Every Week 41.00 41
Total Hours 42.00
Total Weekly Learner Workload 42.00
Total Weekly Contact Hours 1.00
 
Module Resources
Recommended Book Resources
  • Joyner, R. L.,Rouse, W. A., Glatthorn, A. A.. (2013), Writing the winning thesis or dissertation : a step-by-step guide, 3rd Edition. Corwin Press, Thousand Oaks, Calif, p.303, [ISBN: 9781452258782].
  • Murray, Rowena. (2006), How to write a thesis, 2nd Edition. Open University Press, Maidenhead, p.301, [ISBN: 9780335219681].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SDAAN_9 Master of Science in Data Science & Analytics 3 Mandatory