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

This unit covers the fundamentals and applications of machine learning (ML) algorithms. This is accompanied by some programming exercises to apply theoretical knowledge in practice (using available AI libraries).

The unit also covers deep learning, which has been revolutionising many applications in the recent decade. Students will implement these deep learning algorithms (e.g., using Keras - a very powerful and easy-to-use Python library for developing and evaluating such models) and gain experience in training deep neural networks with Cloud GPU in Google Colab.

Other than supervised learning algorithms, they will learn unsupervised approaches that include Gaussian mixture models and dimension reduction techniques (e.g., principal component analysis), and will get an overview of current research and applications within the area.

You’ll learn to:

• Distinguish between different formulations of the machine learning challenge such as supervised and reinforcement learning
• Demonstrate understanding of a wide range of machine learning techniques, their strengths, and their limitations
• Write code in a relevant programming language (e.g. Python) and employ software libraries to solve problems in machine learning

Topics covered in this unit may include (but are not limited to) the following:

• Central concepts and algorithms of supervised and unsupervised learning
• Deep learning
• Convolution neural networks
• Intelligent control and cognitive systems (will cover data augmentation and transfer learning)
• Current research and applications of Deep learning (e.g., unsupervised, geometric)

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

This unit develops proficiency in advanced data visualisation techniques vital for the business world. Use flexible programming methods to craft impactful static and interactive graphs that effectively convey insights. Learn best practices, design principles, and techniques for visualising diverse data types like geospatial, network, time series, and textual content. Enhance your ability to analyse complex datasets and present compelling visual narratives to inform decision-making in the business context.

You’ll learn to:

• Understand the principles and best practices of advanced data visualisation
• Create sophisticated static and interactive graphs using flexible programming methods
• Design and deploy interactive dashboards for data exploration and presentation
• Visualise various types of data, including geospatial, network, time series, and textual data
• Communicate insights effectively using well-designed visualisations

The unit content covers core and advanced data visualisation concepts and techniques, with a focus on practical applications in the business context. Throughout the unit, students will gain hands-on experience in creating effective visualisations, leveraging cutting-edge tools and technologies, and developing their skills in flexible coding, data analysis and communication.

This unit builds on the skills and knowledge gained in the 'Business Statistics’ and ‘Forecasting' units, teaching you how to apply advanced forecasting methods with extensive use of statistical software. The content covers state-of-the-art forecasting models and their advanced implementations and applications.

You’ll learn to:

• Analyse data and produce time series forecasts using advanced forecasting techniques
• Perform cross-sectional and temporal analyses using hierarchical structures
• Apply practical aspects of forecasting
• Critically evaluate the limitations of analytical methods 
• Develop expert-level programming skills in statistical software
• Analyse large collections of data and produce forecasts

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

In this unit, students will follow an appropriate problem-solving route, building on the detailed dissertation project proposal written in the Research Project Preparation unit. They will analyse possible problem solutions based on an extensive literature and technological review of related research work and choose appropriate methods and approaches. This will lead to the implementation of the chosen solution, its testing, and its evaluation. In most cases the project will be a synthesis of both an analytical and a computational approach to solving or investigating a substantial computer science problem. However, projects will vary in style, and some may be more experimentally based while some may be purely theoretical. A comprehensive dissertation will be submitted at the completion of the project.

The dissertation can be taken over 3 months, 8 months, or 12 months. Students choosing the shorter pathways may have a substantially higher workload per week.

You’ll learn to:

• Identify the tasks to be completed in a research project, plan a scheme of work, and complete the project to a professional standard
• Conduct independent research following the ethical principles and processes
• Assemble and create the necessary analysis, design, and development tools, carry out the development of the solution of a technical problem in computer science with a focus in Artificial Intelligence, and evaluate the effectiveness of the solution against common standards of quality
• Demonstrate the successful completion of these tasks in a well-structured and coherently written dissertation, which will include a discussion of the research outcomes of the work, and future directions
• Evaluate and critique the project