Big Data (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 management strategy, its formulation and implementation.

Module Objectives:

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

The theoretical and practical differences between traditional datasets and Big Data datasets.

An overview of the initial collection of data will be explored for multiple data sources.

A formal grounding in analytical statistics.

Apply principles of statistical analytics to solve problems and inform decision making.

Developing knowledge and understanding of statistical analytics techniques and principles.

Applying these techniques and principles in typical real-world scenarios.

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Indicative Syllabus

Introduction

Introduction to the concept and characteristics of Big Data (volume, velocity, variety) and associated challenges.
Overview of Big Data applications in domains such as finance, medicine, social media, transportation, etc.

Big Data Platforms

Challenges associated with programming for big data: Parallelism for computational processes, Storage and compute locality, Distributed computing, Utilisation of cloud computing platforms for big data processing.
Distributed programming paradigms Distributed programming environments (e.g., Hadoop/HBase) MapReduce algorithm design.
Big data programming tools and languages (e.g., Pig, Hive)

Big Data Analytics

Introduction to algorithms for the analysis of high velocity data.
Creating Spark sessions, dataframes, datasets.
Performing analytics with the dataset API.

Stream Processing

Distributed stream processing for data real-time analysis using a distributed framework such as Spark Streaming.
Advantages and disadvantages of Spark streaming. Architecture and application flow for Spark streaming.
Stateless and stateful processing. Fault tolerance.
Spark streaming. Performance monitoring and tuning.

Graph Analytics

Introduction to graph processing using a package such as Sparks GraphX.
Graph algorithms and views.

Module Details

Minimum Intended Learning Outcomes (MIMLOS)

Perform data gathering of large data from a range of data sources.
Critically analyse existing Big Data datasets and implementations, taking practicality, and usefulness metrics into consideration.
Understand and demonstrate the role of statistics in the analysis of large of datasets.
Select and apply suitable statistical measures and analyses techniques for data of various structure and content and present summary statistics.
Understand and demonstrate advanced knowledge of statistical data analytics as applied to large data sets.
Employ advanced statistical analytical skills to test assumptions, and to generate and present new information and insights from large datasets.

Assessments / Exams

The Data Management module is assessed by 100% continuous assessment. 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.

Mapping of Assessments to Module Learning Outcomes

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

Reassessment Opportunity

Where the marks of the assessment 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.

Big Data (Level 8)

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