Module Details

Module Code: DATA8012
Title: Data Analytics Case Study
Long Title: Case Study in Process Data Analytics
NFQ Level: Advanced
Valid From: Semester 1 - 2020/21 ( September 2020 )
Duration: 1 Semester
Credits: 5
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 scope and implement a data analytics project. The module requires the learner to develop, implement and critically assess a detailed methodology to address a defined 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 Communicate project progress to meet professional industry standards
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).
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 and critically analyse appropriate literature in the relevant area of data science and analytics.
Problem Statement
Formulate the core question/opportunity, identifying dataset(s) relevant to the chosen application area.
Implementation
Examine a data analytics case study in industry This is predominantly self-directed learning with support from the project supervisor. This phase of the project should primarily be about analysing a case study, drawing conclusions and making recommendations. The project plan may be revisited and revised as necessary. The learner should utilise the curriculum content as a whole to support this endeavor, in addition to external sources e.g. Internet, Library and other professionals.
Report
Write a professional report that conveys the project outcomes in accordance with the Departments template and recommendations. The report should be succinct and focus on results, analysis, recommendations and conclusions.
Oral Presentation
Describe the project and main outcomes via an oral presentation. Defend and explain decisions, solutions, results and analysis in a question and answer session.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Presentation % of Total Mark 15
Timing Week 4 Learning Outcomes 1
Assessment Description
Summarise key resources in an initial literature review and an overall project plan in a short presentation.
Assessment Type Presentation % of Total Mark 15
Timing Week 8 Learning Outcomes 2,3
Assessment Description
Present the key findings of a feasibiltiy study of the methods and tools used and present the plan for how the case study meets the need of stakeholders.
Assessment Type Project % of Total Mark 70
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
Comprehensive professional report with an appropriate literature review, feasibility study, detail of the implementation of the case study.
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 Contact with Supervisor Every Week 0.50 0.5
Independent & Directed Learning (Non-contact) Non Contact Independent Work Every Week 6.50 6.5
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 0.50
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecturer-Supervised Learning (Contact) Contact Contact with Supervisor Every Week 0.50 0.5
Independent & Directed Learning (Non-contact) Non Contact Independent Work Every Week 6.50 6.5
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 0.50
 
Module Resources
Recommended Book Resources
  • Neville, Colin. (2010), The Complete Guide to Referencing and Avoiding Plagiarism, 2nd Ed., Open University Press, McGraw-Hill Education London, [ISBN: 0335241034].
  • Kjell Johnson, Max Kuhn. (2013), Applied Predictive Modelling, First Edition. Springer, [ISBN: 978149397936].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SPRDA_8 Certificate in Process Data Analytics 2 Mandatory