MBA Specialism in Data Analytics


MBA Specialism in Data Analytics

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  • Study Hours

    Study Hours

    450 Hours

  • Total Fees



  • Delivery



  • Modality


    Self Paced

About the programme

The MBA Specialisation in data analytics will provide you with the knowledge and skills to examine and strategically apply data to make informed business decisions that help organisations increase revenue, streamline operations, and recognise opportunities to improve organisational performance. Through a series of three modules, you will learn how to convert semi-structured, structured or unstructured Big Data into useful insights that help in operational and strategic decision-making processes; present data and analysis to executive-level decision-makers; and how to utilise data to make sound business decisions and influence the strategic direction of an organisation.

Whether you are seeking a position as an analyst, manager, or consultant, the Mini-MBA in data analytics will provide you with the knowledge and hands-on analytical skills needed to support a flourishing career in marketing, management, or finance.

Programme Modules

Applied Data Analytics for Decision Making

Programme Overview

This module will challenge students by introducing them to making business decisions using data analysis. Data analysis is the use of statistics combined with analytical methods to give insights into a business. After starting with a refresher on basic statistics, students will learn how to create statistical experiments to create a process for decision-making. These experiments will use methods such as regression and optimisation to determine the optimal business decisions. The analysis of these experiment outcomes will give you a deeper insight into your business units. This will result in an increase in your ability to add value to your organisation. 

Business Intelligence & Visualisation Tools

Programme Overview

Over the years, organisations have accumulated a vast amount of data in their enterprise-wide information systems. These data typically represent daily operations and transactions within a business context. Organisations rely on computer-based information systems for capturing, analysing, and distributing the information required to develop, implement, and evaluate strategies in all functional areas. Managing data as a corporate resource requires an understanding of business processes and of the underlying structure of the data needed to support them. Business intelligence (BI) is the process of collecting and turning this resource into Business value. 


The course provides an introduction to decision-making and technologies used to support organisational decision-making. (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Part of this process is to display the results in graphical images for easier understanding. This course will provide an understanding of data organisation, and examine the BI processes and techniques used in transforming data into knowledge and value. Various functions and applications of business intelligence are described, including but not limited to reporting, online analytical processing, data visualization and business process management.

Data Mining and Business Strategies

Programme Overview

This module offers an in-depth understanding of Data Mining and Strategic Management techniques for improving business decision-making. Through an examination of data mining and machine learning terminology and techniques, students will be introduced to data blending and wrangling concepts applicable to formulating a strategic data management plan. The challenge is to select the appropriate method. Students will learn to use and design data mining-based solutions to solve “real-time” business problems. 


Students will explore the strategic planning concept for using decision support systems or hybrid platforms when wrangling NoSQL and text data. The coursework will explore data mining concepts for blending unstructured data sets or working with semi-structured data using the Extract, Load, Transform (ELT) process. Also, students will briefly examine automated processes, like Extract Transformation and Load (ETL) or loading data sets as use cases. This course will theorize on data optimisation techniques for using machine learning and artificial intelligence for deep learning of the data. 


Finally, students will conduct statistical data modeling used in predictive and descriptive analysis such as classification trees, segmentation, clustering, and perform basic exploratory data analysis with Excel to create data analytical presentation projects.

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MBA Specialism in Data Analytics

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