Like the physical universe, the digital universe is enormous and is doubling in size every two years. By 2020 the digital universe – the data we create and copy annually – is projected to reach 44 zettabytes or 44 trillion gigabytes.

Data is everywhere in the world. Without knowing how to interpret this data it would be difficult to understand its meaning or make use of the data to increase the productivity of an organisation. Data analytics is a range of processes that converts data into actionable insight using a range of statistical techniques. Data analytics is a relatively new term – it is an overarching term for all decision support and problemsolving techniques. Most of the time the term ‘data analytics’ and ‘business analytics’ are used interchangeably.

This course will introduce the theoretical foundation of data analytics and a range of data analytic processes and techniques to provide hands-on experience for enhancing students’ skills.

Topics included in this course are: data analytic terminologies, types of data analytics, data exploration and visualisation, understanding data with descriptive, predictive and prescriptive analytics.

On successful completion of this course students will be able to understand the theoretical foundation of data analytics, data analytic processes and techniques. Moreover they will gain hands-on experience of implementing data analytic processes and techniques using a programming language such as Python, R, or a tool such as Weka, KNIME, PowerBI, Excel etc.

As a result students will develop skills such as communication literacy, critical thinking, analysis, reasoning and interpretation which are crucial for gaining employment and developing academic competence.

Learning Objectives

By the end of this course a student will be able to:

1. Discuss the theoretical foundation of data analytics that determine decisionmaking processes in management or business environments.

2. Apply a range of descriptive analytic techniques to convert data into actionable insight using a range of statistical techniques.

3. Investigate a range of predictive analytic techniques to discover new knowledge for forecasting future events.

4. Demonstrate prescriptive analytic methods for finding the best course of action for a situation.