This unit highlights the importance of statistical methods and tools for today's managers and analysts and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

Growing volumes of data and, more importantly, the computation power to analyse it are now widely recognised as key business assets. No single discipline has the tools to make the most of these assets. Instead successful "big data" capability requires (a) the ability to understand how data can (and often cannot) be used to generate new insights into substantive problems (b) knowledge of how data are generated and used and (c) the ability to understand connections between variables captured in data. This unit provides an overview of principles from the disciplines of Business Information Systems and Business Analytics, applied in the context of Marketing problems, relevant for using 'big data' in business planning, decision-making and operations.

Business analytics and the ability to interpret and respond to related outputs, is a major source of competitive advantage in the information age and is therefore a leading business priority globally. In recent times, this field has evolved from a technology topic to a management priority, creating an unprecedented demand for new competencies in managing with data. Taking a business rather than technology perspective, this unit covers the enterprise ecosystem in the context of strategic and operational analytics and decision making. Topics include innovation through advanced analytics, data driven performance management, strategic business improvement and management of complex BI projects. The unit offers hands-on experience in using a commercial platform, combined with in-depth analytical skills, and enables students completing the unit to help any organization to derive more value from data and information and compete on analytics.

Data visualisation, storytelling, and scenario development are prominent analytical practices that are increasingly used by professionals seeking to use data for decision making. This unit aims to equip students with necessary knowledge and data visualisation skills, acquired through real-world projects and applications inspired by leading industry practices. Students develop a holistic view of data visualisation and acquire knowledge of related tools to deal with organisational and societal challenges. This unit focuses on business/organisational decision makers and their use of data visualisation. As such this unit does not require any prior IT, computer science or data science experience.

As the amount of data generated continues to grow dramatically, managing data at scale presents a set of challenges for which organizations have to be uniquely prepared. This unit provides students with what they need to know about generating, collecting, processing, storing, managing, analysing and interpreting data. The unit provides an understanding of the importance of data strategies, data governance and data quality, challenges associated with data protection, privacy and security, data management practices, and modern platforms and architecture to streamline data operations within an organisation. Students learn the importance of confidentiality, integrity, and availability of data in mitigating information risks and emergent issues around data ethics.

Enterprises can access increasing volumes of spatial data (associated with time and space) drawn from a variety of sources including the internet of things, sensors, mobile phone locations and other diverse and unlinked data sets. Managing these data to create useful management insights is a demanding task, and spatial data analysis presents a unique set of challenges and opportunities. Effective management and analysis of spatial data provides strategic value for organisations, across logistics, transport, marketing and other business functions, allowing enterprises to manage strategic challenges in sustainability and resilience. This unit uses real-world data and problem-based learning to develop hands-on experience with managing, processing and modelling spatial data and ultimately drawing insights for business decisions linked to both distribution and supply chain interactions. Students develop highly marketable skills in spatial data analytics that are transferable across a broad range of industries and sectors. These skills include the ability to generate a range of outputs, including decision support systems, maps and visualisations that effectively communicate complex information to support strategic, tactical and operational decision making. This unit utilises a widely-used spatial software package and introduces Geographic Information Systems (GIS), spatial databases and structured query language (SQL).

With fast growth of the modern digital economy, multi-quintillion bytes of data is generated every day. This provides an enormous opportunity and a significant challenge for marketers to extract marketing insights because of not only the size of the data, but also the structure of the data. A growing proportion of the data is unstructured, such as customer emails and texts, mobile data, social media UGCs, C2C data on two-sided platforms in the sharing economy. Traditional marketing research methods cannot be used to solve these problems. This unit introduces state of the art machine learning methods to help marketers extract consumers insights from big data including structured and unstructured data and make better informed business decisions.

There have been two fundamental shifts in the focus of business and marketing strategy. On the one hand, companies have become more focused on managing relationships with their customers over an extended period of time. On the other hand, more than any time in history companies' decisions become more data-driven due to the exponential increase in the volume of data on customers, competitors and markets. To obtain, retain and grow a customer base, it is crucial to know how to obtain customer information and how to make sense of it. This unit introduces students to fundamental concepts of customer relationship management and state-of-art analytics and how to apply these to real-world business problems. The unit covers topics including understanding customer relationships, implementing strategic customer relationship management, handling and analysing customer-related databases, increasing customer profitability based on actionable insights gained from customer data, and giving more value to data through visualisation. Students also gain statistical skills, however, no prior knowledge of statistics is required.

Business operations are the activities that businesses carry out to create value. This unit provides the models needed to analyse business operations of a company or organisation and make management decisions on operational issues. It covers business operations in both manufacturing and service industries, looking at processes, supply chains and quality issues. Topics covered may include the modelling of manufacturing operations and related group technologies, the modelling of financial service operations (e.g. brokerage operations), and the operations implications of internet technologies.

It is now common for businesses to have access to very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.

This unit provides the basic knowledge and tools needed to understand and manage risk. It includes business cases to illustrate the nature of risk and risk management strategies. The main focus is on quantitative approaches to analysing risk through understanding the probability distributions involved. Topics covered include: Value at Risk calculations; Utility theory for decisions; Prospect theory for decisions under risk; Extreme value theory; Monte-Carlo simulation; Stochastic optimization; Robust optimization; Credit scoring; Real options.

Time series and statistical modelling is a fundamental component of the theory and practice of modern financial asset pricing as well as financial risk measurement and management. Further, forecasting is a required component of financial and investment decision making. This unit provides an introduction to the time series models used for the analysis of data arising in financial markets. It then considers methods for forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of financial return data; the Capital Asset Pricing Model (CAPM); financial return factor models, with known and unknown factors, in panel data settings; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting market risk measures such as Value at Risk. Emphasis is placed on applications involving the analysis of many real market datasets. Students are encouraged to undertake hands-on analysis using an appropriate computing package.

To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this unit, students gain skills required to succeed in today's highly analytical and data-driven economy. The unit introduces the basics of data management, business forecasting, decision trees, logistic regression, and predictive modelling. The unit features corporate case studies and hands-on exercises to demonstrate the concepts presented.

Machine Learning is a fundamental aspect of data analytics that automates analytical model building in modern business. In the big data era, managers are able to use very large and rich data sources and to make business decisions based on quantitative data analysis. Machine Learning covers a range of state-of-the-art methods/algorithms that iteratively learn from data, allowing computers to find hidden patterns and relationships in such data so as to support business decisions. This unit introduces modern machine learning techniques and builds skills in using data for everyday business decision making. Topics include: Machine Learning Foundation; Modern Regression Methods; Advanced Classification Techniques; Latent Variable Models; Support Vector Machines (SVM) and Kernel Methods; Artificial Neural Networks; Deep Learning; and Machine Learning for Big Data. Emphasis is placed on applications involving the analysis of business data. Students will practise applying machine learning algorithms to real-world datasets by using an appropriate computing package.

Accurate and effective analysis of data is a crucial skill in today's data-rich business environment. Visual Data Analytics (VDA) is an indispensable scientific tool for analysing all sorts of business-related data and, in particular, complex high-dimensional data. Applications include the visualisation of financial statements, capital market data, marketing data, supply chain data and many others. VDA has the ability to encode vast amounts of information into a small space that can be then intuitively interpreted for decision-making. This unit draws upon statistics, computer science, behavioural psychology and information design for visualising numerical and text data. It presents statistical and data analysis methods that are necessary for description, exploration, inference and diagnosis using data reduction, visual mining, smoothing, clustering and validation techniques. Upon completion of the unit, students should be proficient in producing high integrity visuals that enable fast and precise business decision-making. Students will also learn about the limitations of visual perception and how to design powerful visuals that can tap into our natural cognitive predisposition in favouring visual types of information.

Behavioral Data Science is a new growing field combining techniques from the behavioral sciences, such as psychology, economics, sociology, and business, with computational approaches from computer science, statistics, data-centric engineering, information systems research and mathematics, all in order to better model, understand and predict human, algorithmic, and systems behaviour. Behavioral Data Science lies at the interface of all these disciplines (and a growing list of others) - all interested in combining deep knowledge about the questions underlying human, algorithmic, and systems behavior with increasing quantities of data. How can people's well-being be measured and improved using behavioural data science? How can people be nudged at scale to engage in more pro-social and environmentally friendly behaviour? How can companies provide personalized services to customers and deliver them in responsible way without compromising people's privacy? This unit of study explains how these and many other questions can be answered, providing detailed methodology and concrete examples. Behavioral data science approaches these questions by looking at how human, algorithmic, and systems behavior can be better understood, analysed, and optimised using large amounts of data available through digital technologies and by employing innovative modelling approaches at the intersection of behavioral science and data science. The purpose of this unit is to provide a comprehensive overview of these modelling approaches, applications, and tools which are used by contemporary behavioral data scientists at the leading edge of their field.