Business Analytics course curriculum

Get to grips with key Business Intelligence software, including graphical report design, dashboard design, and visualisation techniques to develop high-level insights.

You’ll learn to:

• Identify the underlying data model of a business process
• Visualise processed data in multiple dimensions to maximise its business value.
• Perform exploratory data analysis

Learning Outcomes:

• Understand the uncertainties associated with data analysis.
• Perform statistical analysis to a set of given data.
• Make managerial conclusions about a set of given data using descriptive statistics.

This introduction to databases for storing data and retrieving information helps you optimise and process data to derive managerial insights.

You’ll learn to:

• Identify the underlying data model of a business process
• Design a database for efficient data storage and retrieval
• Eliminate data duplication to achieve information consistency
• Present multidimensional data in tabular format

Learning Outcomes:

• Understand and implement data models.
• Design a database that will best fit the needs of a business in terms of both flexibility and efficiency.
• Use optimised queries to process data and derive managerial insights.
• Use SQL and general database software.

Learn how to make the best possible decisions through the use of mathematics and computers, and present the optimised results to a non-technical audience.

You’ll learn to:

• Model and solve real-life managerial challenges as optimisation problems
• Recognise components of an optimisation problem within a given managerial context
• Assess the complexities to solve issues using an optimisation model

Understand the advanced functions of Excel as well as the programming language (VBA) embedded within it, to support business decision-making.

You’ll learn to:

• Understand and critique the capabilities and limitations of Excel
• Use advanced functions of Excel to provide business decision support
• Assess ways in which to solve business problems using Excel and VBA

Review concepts of data analysis, using statistical analytics software to develop your interpretations.

You’ll learn to:

• Explore the uncertainties associated with data analysis
• Perform statistical analysis on a set of given data
• Make managerial conclusions about a given set of data using descriptive statistics

Examine forecasting techniques to achieve the best possible estimates for unknown parameters such as customer demand.

You’ll learn to:

• Interpret patterns and trends in data, while understanding the uncertainties associated with forecasting
• Draw managerial conclusions from data using forecasting methods and software
• Prepare business forecasting model reports to a non-technical audience

Building on from ‘Databases’ and ‘Business Intelligence’, you will learn how to spot patterns in data using algorithms, detecting previously unknown rules and identifying the business implications.

You’ll learn to:

• Model business challenges as data mining models, using state-of-the-art data mining software
• Measure the accuracy and precision of the rules and patterns detected
• Prepare business reports and present the results of an optimisation model to a non-technical audience

Learning Outcomes:

• Model business challenges as data mining models.
• Choose appropriate algorithms to detect previously unknown rules and patterns within data and infer their business implications.
• Measure the accuracy and precision of the rules and patterns detected.

Learn to use the latest machine learning software and libraries to prepare business reports and present the results of an optimisation model to a non-technical audience.

You’ll learn to:

• Choose appropriate machine learning models to analyse data and infer their business implications
• Estimate the effects of the machine learning models on a business
• Apply ethical principles in the collection, conversion and analysis of data

Learning Outcomes:

• Choose appropriate machine learning algorithms based on data types and specific business context.
• Develop evaluate and improve machine learning models in order to make predictions or decisions with business implications.
• Estimate the effects of the machine learning models on the business operations.
• Apply ethical principles in the collection conversion and analysis of data.

Building on skills and knowledge acquired during the ‘Optimisation’ and ‘Spreadsheet Modelling’ units, you’ll learn how to develop algorithms and implement them to optimise your business decisions.

You’ll learn to:

• Design heuristic algorithms for optimisation problems
• Understand and improve the computational complexity of an algorithm
• Provide solutions for managerial decision problems using a programming language, such as VBA

Learning Outcomes:

• Design heuristic algorithms for solving optimisation problems.
• Understand and improve the computational complexity of an algorithm.
• Provide solutions for managerial decision problems using a programming language.

Understand how to simulate business processes using computer software, and to apply what-if analysis.

You’ll learn to:

• Use of state-of-the-art simulation software
• Construct simulation models of real-world business processes
• Test and compare business scenarios using simulation

Learning Outcomes:

• Construct simulation models of real- -world business processes.
• Identify terminating and non-terminating systems and choose appropriate statistical methods to evaluate their performance.
• Test and compare business scenarios using simulation.

Delivered by faculty members and industry experts in business analytics, this unit focuses on business analytics software, its applications in practice in different sectors and its implications in a wider international context.

You’ll learn to:

• Use contemporary business analytics software to identify problems, devise solutions and implement them
• Assess past and current business analytics implementations, considering ethical implications
• Project the future needs, solutions and trends for business analytics solutions across diverse industries
• Critically evaluate state-of-the-art software for business analytics, and its presentation to non-technical audiences

Learning Outcomes:

• Use contemporary business analytics software to discover unidentified problems, devise solution approaches and implement them.
• Assess the past and current business analytics implementations, in particular with respect to ethical considerations.
• Project the future needs solutions, and trends for business analytics solutions across diverse industries.
• Critically evaluate state of the art software for business analytics, and its presentation to non- technical audiences.

Apply concepts, frameworks and tools of project management and strategies in the context of business analytics projects, using case studies to examine the real-world management challenges.

You’ll learn to:

• Select appropriate management approaches and analytics goals for each project, applying techniques to reduce uncertainty
• Assess the project data requirements and associated time-budgets for data collection
• Plan for project success within the constraints of time, cost and quality
• Understand the risks and uncertainties associated with managing analytics projects, applying principles to manage project teams
• Understand the importance of integrating analytics teams with business teams
• Apply basic tools of project management at strategic, systems and operational levels in real-world analytical contexts

Learning Outcomes:

• Make assessments of the data requirements of the project and associated time - budgets for data collection.
• Assess and plan for project success beyond the triple constraints of time cost and quality.
• Understand the general risks and uncertainties associated with managing analytics projects.
• Apply principles of managing project teams.
• Use and apply basic tools of project management at strategic, systems and operational levels in analytics contexts likely to be encountered in practice.

The final dissertation is an opportunity to work independently to apply everything you’ve learned, covering all stages of a research-based project, including problem definition, literature review, methodology and analysis. The research project may be based upon a real-world problem from a sponsor company.

The dissertation can be taken over 3 months, 6 months, or 12 months. Students choosing the shorter pathways may have a substantially higher workload per week.

You’ll learn to:

• Identify a business analytics problem in an area of interest, such as a real-world problem from a sponsor company
• Select, analyse and present numerical or non-numerical data, developing rigorous arguments through the appropriate use of concepts and models
• Synthesise multidisciplinary perspectives, derive managerial insights and implement an appropriate course of action
• Present the problem and the solution in written form, conforming to acceptable standards of presentation and expression