Fundamentals of Data Analytics (Level 8)

Duration i year

Stage Award

NFQ Level Level 8

ECTS Credit 10

The module aims to facilitate learners in engaging with the concept of Data Analytics, its formulation and implementation.

The objective of this module is to provide the learner with knowledge of:

The theoretical underpinning of Data Analytics
An overview of the data used for Data Analytics
A formal grounding in the background to the study area
Definitions and principles of analytics
Developing knowledge of datal analytics techniques
Applying these techniques and principles in typical real world scenarios

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Fundamentals of Data Analytics (Level 8)

Indicative Syllabus

Introduction to Analysis & Data Analytics

Role in industry, key concepts, global trends, knowledge areas, tasks, techniques, competencies, terminology.

Skills Bridging Bootcamp

Checking existing technical skillsets of learners and identifying gaps followed by intensive
focused bootcamp activities to establish foundational knowledge in these areas to support
other modules.
This bootcamp will focus on foundational Python, SQL and R programming skills in the
context of data analytics and will support the two modules included in this Higher Diploma.

Business Intelligence & Organisational Strategy

Alignment of business intelligence & organisational strategy, data analytics as a core
business tool.
Data analytics as a complementary business tool, exposure to real-world implementations
in business.

Analysis Planning, Monitoring & Elicitation

Problem statement, contextualise problem, identification of KPIs & metrics.
Plan & manage business analysis approach, activities and communication.

Data Analytics Lifecycle

Key Roles in the Data Analytics process, Discovery (Resources, Stakeholders, Developing
hypotheses, Potential data sources).
Data Preparation (Analytics sandbox, Data Inventories, Data conditioning, “Dirty” data),
Model Planning, Selection & Building, Visualisation & Communication, Operationalising & Monitoring Results.

Data Collection & Sources

Systematic approaches to data collection, Data collection process (Purpose, Identification
of requirements, Elicitation, Validation).
Sampling methods (Random, Quasi-random, Non-random, Sample size), Recognising and
minimising bias, The importance of rigorous documentation.

Solution Assessment, Validation & Presentation

Assess proposed solution, allocate requirements, assess organisational readiness, define
transition requirements, validate solution, evaluate solution performance, make evidenced-
based decisions.
Solution assessment, validation & presentation (to non-technical and managerial staff),
alignment with business strategies.
Ethics in Data Analytics.

Module Details

Minimum Intended Learning Outcomes (MIMLOS)

Demonstrate knowledge and critical understanding of the underlying concepts and principles of data analytic techniques.
Demonstrate the capability to use a range of established techniques and a reasonable level of skill in the use of basic graphical and numerical summaries of data, confidence intervals and testing for means and proportions.
Select and deploy the concepts and principles in the use of data analytics.
Make appropriate use of a statistical package, including basic graphical and numerical summaries of data, and testing for means and proportions.
Communicate straightforward arguments and conclusions reasonably accurately and clearly.

Assessments / Exams

The Fundamentals of Data Analytics module is assessed by 40% continuous assessment and 60% proctored examination. The grades will accumulate over the two semesters. The use of continuous assessment is based on developing the learner’s ability to investigate, evaluate theories and concepts and apply them to a given scenario.

The 60% Proctored Written Examination will be case study based. The case study will be issued two hours before the examination for review. The examination will be two hours in duration and all questions will be based on the case study. Learners will be required to demonstrate the learning of concepts, theories etc by identifying issues in the case study and offering recommendations, with justifications.

Questions in the examination will be linked requiring learners to complete/attempt the first question before attempting the second and so on.

Mapping of Assessments to Module Learning Outcomes

	MLO 1	MLO 2	MLO 3	MLO 4	MLO 5	MLO 6
Examination		X	X		X
Continuous Assessment	X			X		X

Reassessment Opportunity

Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board Meeting and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.

Fundamentals of Data Analytics (Level 8)

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