Data Science and Big Data Technology with Contemporary Entrepreneurialism BEng (Hons): Taicang 2+1+1 programme

This module introduces students to the problems arising from databases, including concurrency in databases, information security considerations and how they are solved; the integration of heterogeneous sources of information and the use of semi-structured data; non-relational databases and the economic factors involved in their selection and to techniques for analysing large amounts of data, the security issues and commercial factors involved with them.

This module will provide students with an introduction to the machine learning. It will contain traditional machine learning algorithms, deep learning algorithms, and probabilistic graphical models. Both theoretical knowledge and practical skills will be offered.

This module extends earlier work on linear regression and analysis of variance, and then goes beyond these to generalised linear models. The module emphasises applications of statistical methods. Statistical software is used throughout as familiarity with its use is a valuable skill for those interested in a career in a statistical field.

This module addresses one of the most important topics in the field of entrepreneurship and innovation enabling students to respond to the challenges and opportunities posed by complex and uncertain business environments. The module is designed to equip students with the skills and attributes that individuals need to demonstrate in a corporate context. The objective of the module is to focus on management and leadership practices in a corporate context. It will approach the evaluation of corporate entrepreneurship (or intrapreneurship) by drawing attention to relevant theories/models and tools that can be applied in a broad range of business contexts including the private, public and third sector organisations. It provides an opportunity for students to develop and create new business solutions that can enhance the competitiveness and sustainability of organisations.

In today’s business environment, new and disruptive business models are emerging reflecting an in depth understanding of market needs, how new and emerging technologies could meet those needs, and of the resources and capabilities required to make the business a success. This module is designed to equip students with the skills and knowledge of how to better understand customers’ ‘real problem’ and build a product or a service as a solution to the problem identified. The module will take students through the start- up journey from developing a concept to engaging with the market and building a business model (using the Business Model Canvas as an entrepreneurial tool) as a platform to launch a new venture. The module will draw on relevant theories/models related to entrepreneurship and opportunity recognition, entrepreneurial leaderships, business modelling and innovation that can be applied in a range of business contexts including the private, public and third sector.

This module covers the concepts of distributed systems and the underlying principles of distributed computing and discusses the issues and various solutions proposed in the distributed computing community. Specifically, communication and broadcast, election algorithms, synchronization and concurrency, fault-tolerance and security related issues will be discussed in the lectures. Where applicable practical implementations of the concepts will be introduced.

This module introduces students to some basic algorithms and data structures. It gives some fundamental concepts of design and analysis of algorithms, and implementation of algorithms by choosing appropriate data structures.

​To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining.   Google search framework and IBM Watson QA system and various other industrial level data mining applications are discussed.