Module Details

Module Code: DATA8006
Title: Data Science Analytics Project
Long Title: Data Science Analytics Project
NFQ Level: Advanced
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 10
Field of Study: 4816 - Data Format
Module Delivered in: 1 programme(s)
Module Description: This module develops within the learner the knowledge, skills, and competences required to research, develop and scope a data science/analytics project, and to successfully complete it in accordance with an approved plan. The module requires the learner either individually or as part of a team to develop, implement and critically assess a detailed methodology to address a defined data science or analytics problem within a prescribed time-frame. 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 in a style and manner appropriate for professional practitioners in the discipline.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Undertake a review of relevant and appropriate literature to determine current knowledge in a field of data science and analytics.
LO2 Conduct a feasibility study of the proposed data science methodologies and technologies.
LO3 Plan the creation of effective final deliverables for a data science/analytics project that will meet the needs of stakeholders and others.
LO4 Systematically review and adapt the employed data science methodologies during implementation in response to practical, real-world constraints.
LO5 Critically assess the project outcomes.
LO6 Use appropriate written and oral communication skills required of a professional practitioner, with a particular emphasis on conveying the underlying message of the research to stakeholders at all stages of the data science/analytics project.
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).
13586 STAT8006 Applied Stats & Probability
13801 STAT8010 Intro to R for Data Science
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.
None
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.
None
 
Indicative Content
Literature Survey
Gather, critically analyse and reference research and other appropriate literature in an emerging field of data science and analytics.
Core Problem
Formulate the core research question and identify data sets relevant to the chosen data science/analytics area.
Development of Methodology
Formulate and assess viable methodologies and technologies to address the chosen research question with a view to identifying the most appropriate methodologies and technologies.
Project Implementation
Supervised self-directed learning, utilising the identified data science methodologies.
Written Report
Write a professional report that conveys the findings of the research in the chosen area of specialisation. Critically evaluate the results and give appropriate recommendations.
Oral Presentation
Make an oral presentation on the undertaken data science/analytics project.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Reflective Journal % of Total Mark 10
Timing Every Second Week Learning Outcomes 1,2,3,4,6
Assessment Description
Professional reports outlining project progress
Assessment Type Other % of Total Mark 10
Timing Every Week Learning Outcomes 1,2,3,4,5,6
Assessment Description
Method of Working: Mark associated with initiative, decision making, application and demonstration/performance of project outcomes.
Assessment Type Presentation % of Total Mark 20
Timing Sem End Learning Outcomes 1,2,4,5,6
Assessment Description
Oral presentation on the chosen data science specialisation and research outcomes.
Assessment Type Written Report % of Total Mark 60
Timing Sem End Learning Outcomes 1,2,4,5,6
Assessment Description
Comprehensive Professional Report with an appraisal of the outcomes of the project.
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
Independent & Directed Learning (Non-contact) Non Contact Independent Work Every Week 13.50 13.5
Lecturer-Supervised Learning (Contact) Contact Interaction with project supervisor Every Week 0.50 0.5
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 0.50
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Independent & Directed Learning (Non-contact) Non Contact Independent Work Every Week 13.50 13.5
Lecturer-Supervised Learning (Contact) Contact Interaction with project supervisor Every Week 0.50 0.5
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 0.50
 
Module Resources
Recommended Book Resources
  • Neville, Colin. (2010), The complete guide to referencing and avoiding plagiarism, 2nd. Open University Press, McGraw-Hill Education, London, p.223, [ISBN: 0335241034].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 2 Mandatory