Big Data

Introduction

This course can count towards one of our postgraduate qualifications within our Information Management and Information Technology Awards

A key first step into the world of Big Data is to understand what it is, why it is different and how it is best managed. This module will introduce Big Data concepts and applications and compare traditional (SQL) to alternative (NoSQL) approaches to data storage and retrieval.  In addition to key concepts of data integrity and quality, you will have the opportunity to gain hands-on experience with big data tools.

Entry requirements

Participants are expected to have a first degree at 2.2 level or above (or equivalent), or alternatively have industrial experience.

If you are a non-UK student you will need to show your passport on entry to the UK. Please check your eligibility to visit and study in the UK here. If you are a non-Irish EU national currently resident in Ireland please contact us directly for further advice.

If English is not your country's first language, you will be required to provide evidence to show you meet the UK Border Agency and the University's minimum English Language requirements. Further details are available on our English Language Requirements webpage

We strongly recommend that you speak to the course tutor prior to the course if you are unsure about your suitability to complete the assessment.

Careers / Further study

This module can be taken as a standalone module, or used to build up credit towards a named postgraduate qualification (PG Certificate, PG Diploma or Masters) within our Information Management and Information Technology awards

Content

This module will cover the following topic areas:

Data Storage and Retrieval

• Importance of data for business.

• Understand the difference between data, information and knowledge.

• Traditional ways to store and retrieve data.

• Big Data challenges and opportunities.

Introduction to Big Data

• Defining Big Data: Sources of Big Data; The four dimensions of Big Data: Volume, velocity, variety, veracity; Introducing storage and MapReduce.

• Business application of Big Data: Big Data applications/examples in business;Delivering business benefit from Big Data; Establishing the business importance of Big Data.

• Addressing the challenge of extracting useful data/knowledge.

• Integrating Big Data with traditional data.

SQL Databases vs. NoSQL Databases

• Understand the growing amounts of data.

• The relational database management systems (RDBMS).

• Capabilities of traditional RDBMSs.

• Overview of Structured Query Languages (e.g. SQL).

• Introduction to NoSQL databases.

• Understanding the difference between a relational DBMS and a NoSQL database.

• Identifying the need to employ a NoSQL DB.

Storing Big Data

• Analysing data characteristics: Selecting data sources for analysis.

• Introduction of selected Big Data stores from the following list: Hadoop, Cassandra, Amazon S3, BigTable, etc.

Achieving Data Quality

• Introduction to data quality.

• Why is data quality a business problem?

• Problems when data is not "fit for purpose".

• Preparing data.

• Ways to improve data quality.

• Understand ETL - Extract, Transform, Load procedures to improve Data Quality.

Knowledge-based Information Retrieval

• Introduction to knowledge-based information retrieval.

• Use for ontologies for knowledge modelling.

• Learn how to build an ontology to link knowledge with data.

• Using ontologies for information retrieval - case study.

• Machine learning for knowledge acquisition: Introduction to machine learning and pattern recognition; Capabilities of different modelling, analysis and algorithmic techniques.

Big Data and Cloud Computing (technology, challenges and trends)

• Cost of storing Big Data.

• Is cloud computing a solution?

• Issues: Privacy and trust.

• Future of Big Data and cloud computing.

• Future research trends in Big Data.