Data at Scale: Management, Processing and Visualisation
This module introduces the fundamental concepts and technologies that are used by modern international businesses to store, fuse, manipulate and visualise mass datasets.
Key concepts include:
- core database and cloud technologies
- data acquisition and cleansing
- how to manipulate mass datasets (focusing on SQL, Hadoop)
- effective solutions to common data challenges (for example, missing data)
- handling geospatial and open data
- visualisation technologies (Tableau, PowerMap, QGIS, CartoDB)
- web visualisation (HTML5)
All content is based around real-world business examples.
Foundational Business Analytics
This module introduces fundamental statistical concepts and key descriptive modelling techniques in data science, while laying a foundation for the general programming skills required by any top modern business analyst (for example, Python/R).
A range of descriptive modelling concepts will be covered (such as feature engineering, clustering techniques, rule mining, topic modelling and dimensionality reduction) against a background of real world datasets (predominantly based on consumer data).
You will learn not only how to successfully implement foundational descriptive techniques, but also how to evaluate and communicate results in order to make them effective in actual business environments.
Consumer Behaviour and Consumption
The module interrogates the concept of 'the consumer' and 'consumption'. It examines behaviour across the consumption cycle (through production, acquisition, use and disposal) addressing individual and contextual factors that shape behaviour at micro and macro levels.
It reviews the roots of research into consumer behaviour and consumption, covers particular theories and bodies of literature (for example, decision making, learning, habits, socio-cultural processes). It provides opportunities to apply theory to consumer behaviour and consumption in a variety of context and to assess the implications for commercial and non-profit organisations, public policy and consumers themselves.
Management Science for Decision Support
The emphasis in this module is on formulating (modelling) and solving models with spreadsheets. The topics covered include:
- modelling principles
- optimisation and linear programming
- network models
- introduction to integer programming
- key concepts of probability and uncertainty
- decision theory
- queuing systems
Supply Chain Planning and Management
Module content is divided into three major parts:
- Fundamental supply chain concepts; The importance of supply chain management and logistics; Classification approaches; Buyer-supplier relationships and sourcing decisions.
- Supply chain management processes; Forecasting for supply chain and production management: qualitative and quantitative approaches (regression and causal modelling, time series methods); Inventory: forms, functions, decisions, and models. Inventory positioning. Supply chain dynamics and the bullwhip effect.
- Planning and control approaches, Aggregate planning. Hierarchical planning and control. MRP-based planning and control. Capacity management strategies. Enterprise Resource Planning (ERP) systems. Just-In-Time (JIT) and Theory of Constraints (TOC). Methods and techniques for planning and control.
Analytics Specialisations and Applications
Offering an in-depth look at specialised analytical techniques, this module highlights the significant opportunities they offer within business environments in extracting actionable insights. You will develop an understanding of how these advanced skills can bring competitive advantage in real-world business applications.
Leading Big Data Business Projects
This module explicitly focuses on technologies, planning and managerial issues associated with big data projects in business. Key concepts revolve around:
- understanding requirements
- coordination of a business analytics team
- the data lifecycle
- using noSQL solutions (for example, Hadoop, Pig, Hive, MongoDB)
- linked data
- taking solutions from evaluation to implementation (for example, A/B testing)
- presenting persuasive results (for example, d3, advanced python and HTML5)
- extending to real-time/stream business analytics
Machine Learning and Predictive Analytics
This module covers core predictive models and their motivation within business use cases. Introduces the key techniques that underpin this increasingly relevant field along with their evaluation (for example, measures of success, cross validation, A-B testing).
You will establish knowledge of state-of-the-art prediction techniques including classification models (for example, decision trees/forests, SVMs), Nearest Neighbour models, Bayesian methods, Ensembles and Deep Learning. Practical exercises will be set against a range of real world datasets and time series data.
This module is jointly taught using SPSS modeller and Python, giving you practical experience in industrial workflow data modelling environments where applicable.
Advanced Operations Analysis
This module covers:
- more advanced forecasting techniques (including more advanced time series and causal models)
- inventory modelling (quantity discount models; joint replenishment; reorder point , lot size systems; periodic review models; news vendor model; (S-1, S) model; multi-warehouse situations)
- push/pull/conwip and TOC production systems
- shop floor control; buffer management and line balancing
- operations scheduling and sequencing
- managing variety and variability; postponement; mass customisation
- managing MRP based planning and control systems
- organising the planning and control function, sales and operations planning and operations planning and control software
The module provides an overview of the changing international business environment and its challenges for international firms. It critically reviews contemporary issues in international marketing and provides insights into the development of competitive strategies and international marketing management within the context of consumer goods, industrial goods and services.
The module is designed to develop an appreciation of the special requirements for successfully conducting international marketing activities. The module aims to provide you with an in-depth understanding of current issues in international marketing theory and practice.
Quantitative Risk Management
The module covers:
- the development of quantitative risk management and its use by firms to measure and manage their risk
- measures of risk: value at risk, expected shortfall, and other risk measures
- uses of quantitative risk measures: estimating risk measures - historical simulation approaches, parametric approaches and Monte Carlo simulation approaches
- volatility forecasting
- estimating liquidity risks
- backtesting risk models
- risk measurement using Extreme Value Theory
- model and parameter risk
Supply Chain and Operations Strategy and Practice
Module content is divided into three major parts:
- Strategy, context of supply chain and operations strategy. The range of strategic decisions within and between operations. The impact of competition on operations and supply chains. Supply chain strategies for coordination and integration. Collaborative forecasting, planning and replenishment. Strategic perspectives of operations in the virtual and extended enterprises.
- Performance measurement, concept and importance of performance measurement within supply chain and operations management. Financial, non-financial, single and multi-factor measures. Approaches and techniques for developing customer facing and internal measures including Balanced Scorecard and Supply Chain Operations Reference (SCOR) model. Selection and application of key performance indicators. Importance of intra and inter-organisational benchmarking and approaches to target setting.
- Current practice, current supply chain management, logistics, and operations management approaches in specific industries/sectors. Pertinent issues and drivers of change in practicing supply chain and operations management.
Data Driven Dissertation Project in Business Analytics
Representing the culmination of the programme, you will design, execute and report a research project based on the analysis of real-world or simulated data. This includes an 8,000-word dissertation, exhibits and data visualisations, and will need to satisfy scholarly objectives consistent with the execution of quality applied research in a business or social context.
The modules we offer are inspired by the research interests of our staff and as a result may change for reasons of, for example, research developments or legislation changes. This list is an example of typical modules we offer, not a definitive list.