Business Data Analytics MSc

Cranfield University

Prescriptive analytics has the power to help businesses use data to determine the optimal course of action. Prescriptive analysis works with data collected or generated from a wide range of descriptive and predictive sources and creates algorithms to facilitate decision making. It accounts for existing conditions, constraints and the results of each possible decision, while also evaluating potential consequences in different scenarios. Prescriptive analytics is a valuable tool that informs decisions and strategies and can be used alongside subjective judgement to find the best possible solutions among various options.

The module aims to provide you with a comprehensive understanding of prescriptive analytics techniques and their application within a business context. It aims to equip you with both knowledge and transferable skills necessary for making data-driven decisions and generating optimal solutions to complex business problems. This process will be facilitated through the use of spreadsheet-based software packages and Python software. You will have an opportunity to develop your own prescriptive models and apply them to various business problems in areas such as marketing, finance, operations and supply chain management, and HR.

The usefulness of the outputs of data analytics is dependent on the quality of data used in the analysis. In a world awash with data, it is critical that analysts understand how to recognise and harness appropriate data. Further, data visualisation is a key method of communicating important outcomes to stakeholders.

This module is designed to provide you with the knowledge, skills and behaviours for acquiring data and creating datasets that are fit-for-purpose. Using data, you will learn to apply a range of data visualisation techniques, such as scenario building, data mining and descriptive statistics, which will enable you to communicate research findings effectively to key stakeholders. The module will also introduce the R software environment and give you experience of using R to produce descriptive outputs.

The Python programming language has become a key language for business analysts and software developers in both desktop and internet/cloud network-based environments.

This module aims to provide you with the necessary skills and knowledge to develop software solutions to problems in these fields using Python. The principle and advanced elements of Python, associated libraries/toolboxes, programming methodologies and good design principles are covered. Hands-on programming exercises culminating in the construction of a fully functional three-tier application form an essential part of the course.

The aim of the Accounting and Finance module is to introduce a number of traditional and contemporary accounting approaches that will increase the visibility of financial information and support management decision making. It aims to integrate basic financial knowledge in the management of a business organisation.

Business analysts are frequently asked to gather, review and analyse business and industry data to produce robust, meaningful recommendations to senior managers. This requires the combination of a range of knowledge, skills and behaviours. For instance, the selection of quality data, building reliable databases and models, applying statistical models, deriving and communicating meaning from the findings.

This module is designed to provide you with the required skills for structuring predictive research projects including conceptualising research questions and managing data. It explores the use of different methods for making predictions about future outcomes, using historical data. It also explores the validity of these empirical models and the nature of the uncertainty inherent in them.

The module is primarily designed to provide you with an understanding of what is required to undertake successful academic studies and to conduct research in business contexts considering that todays’ managers:

• • are paid to make decisions
  • are expected to make ‘informed’ decisions (i.e. based on evidence)
  • are evaluated on the basis of the outcomes from their decisions

Therefore, understanding the process of producing evidence will ensure you have the core skills to inform management decisions.

The usefulness of the outputs of data analytics is dependent on the quality of data used in the analysis. In a world awash with data, it is critical that analysts understand how to recognise and harness appropriate data. Further, data visualisation is a key method of communicating important outcomes to stakeholders.

This module is designed to provide you with the knowledge, skills and behaviours for acquiring data and creating datasets that are fit-for-purpose. Using data, you will learn to apply a range of data visualisation techniques, such as scenario building, data mining and descriptive statistics, which enables you to communicate research findings effectively to key stakeholders. The module will also introduce the R software environment and give you experience of using R to produce descriptive outputs.

To introduce core Artificial Intelligence (AI) concepts, architectures, methods and tools. This will highlight the potential of AI for aiding innovation, enabling you to develop a practical knowledge of AI-enabled solutions development process for product and service innovation. Further this module will introduce you to machine learning for big data applications.