Business Analytics course structure

The online Business Analytics course curriculum is made up of 13 units, totalling 180 credits. Each unit is designed to develop your ability to analyse data and to apply the insights in practical organisational settings.

Over a period of two years and three months, you must successfully complete all of the units, including a final research-based dissertation. The units are 8 weeks in duration, and run consecutively. Over the year, there are three short breaks, in December, April and August.

Our Business Analytics online course begins with an induction to help you get to know the faculty team, your fellow students and our Virtual Online Environment.

University of Bath School of Management--

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--is looking to train the business leaders of the future. To bridge the gap between big data and the business world. So you become an expert in the handling and analysis of big data, developing understanding, insight, transferable tools, and skills. You will become an expert in acquiring, cleaning, processing, and visualizing data, spreadsheet modeling, VBA programming, databases, business intelligence, and data mining.

Our MSc in business analytics prepares you for a career in a diverse range of sectors, including finance, health care, logistics, and marketing. Our course combines theory with practical business skills, enabling you to solve real-life business problems.

Through our partnerships with IBM and SAS, you'll immerse yourself in state-of-the-art business analytic software, develop your technical skills, critical understanding, creativity, and problem-solving skills to improve your employability, making you in high demand by global employers.

If you're ready to join our selective cohort, contact us to start your journey into big data.

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Units

Business Intelligence (10 credits)

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 business value
  • Perform exploratory data analysis
Databases (10 credits)

Introducing databases for the storage of data and retrieval of information, helping 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
Optimisation (10 credits)

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
Spreadsheet Modelling (10 credits)

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
Business Statistics (10 credits)

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
Forecasting (10 credits)

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
Data Mining (10 credits)

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
Machine Learning (10 credits)

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
Heuristics (10 credits)

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
Simulation (10 credits)

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
Analytics in Practice (10 credits)

Delivered by faculty members and industry experts in business analytics, including a specialist from IBM, 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
Project Management (10 credits)

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
Dissertation (60 credits)

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.

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

Dr Lukasz Piwek

Dr Piwek is a lecturer in Data Science at the School of Management in the Division of Information, Decisions & Operations, University of Bath, a co-founder of the interdisciplinary Psychology Sensor Lab and member of an ESRC-funded Centre for Research and Evidence on Security Threats (CREST). His projects include the investigation of psychological markers of ‘digital footprints’ and the psycho-behavioural implications of using ‘quantified self’ solutions.