This course addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course, which builds on the strength of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the data mining and decision sciences theme to the broader agenda of business intelligence.
You will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, through the use of applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You will also gain a greater understanding of the impact technological advances have on nature and practices adopted within the business intelligence and analytics practices, and know how to adapt to these changes.
You are expected to already have quantitative skills, with an interest in developing these further to support postgraduate activity in analysing, evaluating and reporting on a range of real-world data-intensive problems. You will have a suitable Honours degree from a UK university (or equivalent qualification) in a scientific or engineering discipline with some exposure to the use of IT, or in an area of computer science or IT with a strong interest in quantitative analysis. If you do not have a formal qualification, but you are already in employment, you may be considered if your role involves the data mining and decision support techniques and technologies deployed in the course. If your first language is not English, you will need an IELTS score of 6.5 or equivalent.
BIG DATA THEORY AND PRACTICE
The module discusses how to manage the volume, velocity and variety of Big Data, SQL and noSQL databases, and it touches on issues related to data governance and data quality.
This is a self–contained module in applied statistics and operational research (OR) for decision making that lays the foundations for more advanced modules in data mining, optimisation and simulation modelling. It covers the essential of descriptive, predictive, and prescriptive analytics in an application driven manner and makes use of appropriate software tools such as EXCEL and R to derive meaningful solutions.
DATA MINING AND MACHINE LEARNING
This module will provide an overview of modern techniques in Machine Learning and Data Mining that are particularly customised for Data Science applications. Students will work with select data sets, related to the specific public sector or businesses application domains. Students will work through exercises that provide opportunities to explore the features and strengths of different machine learning and data mining methodologies. A range of toolkits will be introduced, such as R and Python.
RESEARCH METHODS AND PROFESSIONAL PRACTICE
The module strengthens student’s skills for the research and industry needs in the area of their studies, their final project, and their professional development. It guides the students’ personal development plan towards the professional requirements of the discipline, and covers methods of critical evaluation, gathering and analysing information, and preparing and planning a project proposal.
BUSINESS SYSTEMS POSTGRADUATE PROJECT
The project module plays a unifying role and it aims to encourage and reward individual inventiveness and application of effort. The scope of the project is not only to complete a well-defined piece of work in a professional manner, but also to place the work into the context of the current state of the art in business intelligence and/or analytics.
ADVANCED BIG DATA ANALYTICS
The module teaches students how to use Big Data Analytics in enterprises considering both the latest research achievements and technology trends. It gives an overview of the underlying concepts and technologies of Big Data Analytics, such as Hadoop, MapReduce, Hive, etc. It covers the whole data lifecycle from creating to processing data and from publishing and to preserving data.
The module provides an in-depth analysis of advanced topics in operational research such as discrete optimisation, multiple criteria optimisation and modern heuristic approaches.
DATA VISUALISATION AND DASHBOARDING
This module covers the theoretical and practical aspects of data visualisation including graphical perception, dynamic dashboard visualisations, and static data ‘infographics’. Tools such as R and Tableau are used. The aim is to prepare students for becoming a data visualisation specialist.
DATA WAREHOUSING AND OLAP
This module teaches students how to build Data Warehouses by understanding their structures and the concept of multi-dimensional modelling. The focus is on Data Warehouse design, multi-dimensional modelling, the integration of multi-source data and analysis, cloud-based data warehousing, NOSQL OLAP, aiming to support better business decision making.
DATA REPOSITORIES PRINCIPLES AND TOOLS
An introductory module that covers theoretical & practical issues related to technologies employed in persistent storage of data. It evaluates underlying technologies & approaches used in capturing, maintaining & modelling persistent data; reviews the evolution of DBMSs their components & functionality, along with some of the predominant & emerging data models; addresses practical issues related to conceptual data modelling, practical & current trends in database design; it also discusses in detail the features and constructs of the SQL, the de-facto database language for the definition and manipulation of relational data constructs.
SIMULATION MODELLING: RISK, PROCESSES, AND SYSTEMS
The module focuses on the choice and use of appropriate simulation modelling approaches to treat real–world problems, developing solution(s) using powerful simulation software and explaining the business and industrial implications thereof. Relevant applications to problems such as stock control, reliability, project management, and service redesign will be considered in domains such as healthcare, supply-chain, and transport.
WEB AND SOCIAL MEDIA ANALYTICS
This module explores the use of modelling to analyse and measure both online presence and impact using web and social media data. During the module students will learn how to listen to social media conversations taking place and how such data can be transformed into actionable insight for a brand or organisation. Furthermore, we will study ways in which the effectiveness of modern websites are often judged and how online web metrics can be used to drive performance. The overriding aim of the module is to equip students with the necessary technical skills and industrial knowledge for a career in the area of web or social media marketing.
You may take instead another postgraduate module in the Department, at the course leader’s discretion.
Graduates can expect to find employment as consultants, decision modelling or advanced data analyst, and members of technical and analytics teams supporting management decision making in diverse organisations. Typical employers include local authorities, PLCs (such as GlaxoSmithKline, Prudential, Santander and Unilever), public sector organisations (such as the NHS and primarily care trusts), retail head offices, the BBC, the Civil Service and the host of banks, brokers and regulators that makeup the city, along with all the specialist support consultancies in IT and market research and forecasting, all of the whom us data for the full range of decision making.
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