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 Intelligence (BI), increasingly known as Business Analytics, 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 management skills. Taking a business rather than technology perspective, this unit covers all aspects of the enterprise BI ecosystem in the context of strategic and operational BI, including all five stages of BI evolution. Topics include assessment and management of organisational data quality, multidimensional data modelling and integration, management of structured and unstructured data (including those created by social media), business aspects of data warehousing, innovation through advanced analytics, BI driven performance management, business process intelligence, active enterprise intelligence, and management of complex BI projects. Access is provided to the largest world-wide community of BI academics and industry practitioners called TUN (www.TeradataUniversityNetwork.com). The hands-on experience in using a commercial BI platform, combined with in-depth analytical skills, will enable students completing the unit to help any organization (regardless of its size and industry domain) to derive more intelligence from its data and compete on analytics. This unit does not require programming experience; it is suitable for both current and aspiring BI practitioners as well as general business practitioners from any functional area interested to learn how to start and lead BI-related initiatives.

Data visualisation, story-telling, and scenario development have been identified as the most prominent analytical practices of tomorrow. This unit seeks to equip students with necessary knowledge and data visualisation skills, acquired though real-life project inspired by the leading industry practices. Students will also develop a 'holistic' view of data visualisation in practice and will acquire 'thinking tools' to deal with its 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.

The world is increasingly filled with systems, devices and sensors collecting large amounts of data on a continual basis. Most of these data are associated with locations that represent everything from the movement of individuals travelling between activities to the flow of goods or transactions along a supply chain and from the location of companies to those of their current and future customers. Taking this spatial context into account transforms analyses, problem solving and provides a powerful method of visualising the world. This is the essence of Geographic Information Systems (GIS) and this unit. This unit starts by introducing students to the 'building blocks' of GIS systems, including data structures, relational databases, spatial queries and analysis. The focus then moves on to sources of spatial data including Global Positioning System (GPS), operational systems such as smartcard ticketing and transaction data along with web-based sources highlighting both the potential and challenges associated with integrating each data source within a GIS environment. The unit is hands-on involving learning how to use the latest GIS software to analyse several problems of interest using real 'big data' sources and to communicate the results in a powerful and effective way. These include identifying potential demand for new services or infrastructure, creating a delivery and scheduling plan for a delivery firm or examining the behaviour of travellers or consumers over time and locations. This unit is aimed at students interested in the spatial impact of decision-making and on the potential for using large spatial datasets for in-depth multi-faceted analytics.

This unit provides an introduction to marketing research and an overview of the industry. The major components of marketing research projects are discussed and students gain an insight into understanding and structuring research problems. The unit also gives an overview of primary, secondary and internal sources of data as well as advanced methods and techniques of research.

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.

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.

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 decision 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 decision. 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 enables 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.