1. Foundation of Artificial Intelligence
Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, techniques, and application systems for simulating and extending human intelligence. AI techniques and models have been widely employed in various domain-specific applications due to their promising performance compared to conventional methods. This course focuses on fundamental concepts, techniques, and potential business applications of artificial intelligence. The course provides an overview of waves of AI, intelligent agents, problem-solving, planning, and reasoning. It includes topics about search, logic, genetic algorithms, and some potential business applications like expert systems, news analysis, and so on.
2. Business Data Management
This course is designed to describe the advanced concepts and principles of data management for business. Various types of databases will be discussed in this course, such as objected-oriented, relational, document-oriented, NoSQL, and New SQL. Popular database management systems such as Microsoft SQL Server and/or Oracle will be described. Topics include data models (ER, relational, and others); query language (Structure Queries Language); management of semi-structured and complex data; NoSQL databases. It also covers the essential concepts, options, and best practices for data administration, data protection, privacy control, user security and management, and system configurations. It addresses topics about the general concepts of data disaster recovery, planning, and procedures.
3. Principle of Data Analytics and Programming
This course provides students with the knowledge of the business data analytics process as well as the fundamental principles of programming for data collection, data preprocessing, data analysis, and data visualization. It introduces different concepts of Python programming, including the basic Python language syntax, variable declaration, basic operators, program flow and control, defining and using functions, classes, and file and operating system interface. Basic Python packages designed for data analytics will be introduced, such as Numpy, Scipy, Matplotlib, and Pandas. A number of data analytics applications in different business domains will be described.
4. CDS504: Business Data Analytics
This course is designed to introduce data analytics and its applications in e-Business. Databases are valuable treasures containing data and hidden precious knowledge. Nowadays, many organisations are performing their operations electronically and these organisations are capable of generating and collecting a huge amount of data in a relatively short period. The explosive growth of data requires a more efficient way to extract useful knowledge. Data analytics, which is an automated process of sifting the data to get the gold buried in databases, can fulfill this requirement. In this course, students will learn different data pre-processing, data mining, and analytic methods. They will learn how to apply appropriate methods to solve problems arise in different firms.
5. Machine Learning for Business
Machine learning is a branch and one of the most popular AI techniques in recent years. Machine learning models and techniques have been widely used in many fields, such as natural language understanding, machine vision, and pattern recognition. This course will discuss the concepts, techniques, and business applications of machine learning. It will introduce the supervised, semi-supervised, unsupervised, transfer, and reinforcement learning paradigms. The techniques include regression, clustering, logistic regression, neural networks, Q-learning, etc. will be described. Different business application examples will be discussed in this course.
6. Practical Application of Deep Learning
Deep learning is one of the bleeding-edge technologies of machine learning. It is a neural network used to establish and simulate the human brain for analytical learning and to interpret data by imitating the mechanism of the human brain. Deep learning is widely used in computer vision, speech recognition, natural language processing, and other fields. This course aims at providing an understanding and hands-on experience of the existing deep learning approaches. The topics will cover how to select deep neural networks, how to design deep neural networks, and how to train and optimise the neural networks for practical applications using state-of-the art software packages. The course will introduce different deep neural network models, including convolutional neural networks, recurrent neural networks, adversarial learning models, and training techniques including dropout, batch normalisation, selection of activation functions and so on. TensorFlow, Pytorch, or other state-of-the-art deep learning tools will be introduced and applied to solve different classes of problems with huge datasets in business domains.
Elective Courses (Any 12 credits from the eleven elective courses)
1. Marketing Analytics and Intelligence
Marketing analytics is the intersection of Marketing and Data Science, generating business insights and offering new opportunities for a competitive advantage. New digital technologies have fundamentally changed various aspects of marketing practice over the past years and have led to a dramatic shift in the quantity and quality of information we are able to access, analyse, and act. The course discusses the cutting-edge techniques used to unlock the predictive potential of data analysis to enhance marketing performance, strategic management, and operational efficiency and provides students with hand-on experience in the application of analytical tools and techniques, to real-life marketing problem. (This course will be lectured by an instructor from the Faculty of Business.)
2. Healthcare Analytics
Healthcare analytics transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalised. This course will introduce student the key technologies that support smart healthcare. It explains how to build the surveillance infrastructure and how the data is collected and transmitted back from various wearable sensors of multiple sources, by using the technologies of Internet of Things (IoT): Medium Access Control (MAC) protocols, routing protocols. This course will also describe data fusion of health and healthcare data, data models, data management, machine learning algorithms, and analytics techniques and tools for health risk prediction. Case studies and examples of application will be elaborated in this course. (This course will be lectured by an instructor from the Faculty of Business.)
3. Artificial Intelligence based Optimisation
In an optimisation problem, one seeks to minimise or maximise an objective function or a number of objective functions with real, integer, and/or discrete variables, subject to constraints on the variables. Optimisation refers to the study of these problems, their properties, the development and implementation of algorithms to solve these problems, and the application of these algorithms to real-world problems. In this course, advanced artificial intelligence algorithms such as multi/many objectives optimisation algorithms, genetic algorithms, evolution strategies, ant colony optimisation, particle swarm algorithms, firefly algorithms, differential evolution, and other meta-heuristic methods will be discussed. These methods are able to find optimal or near-optimal solutions for challenging optimisation problems. This course will also describe some real-world applications that use these algorithms to handle difficult business problems.
4. Location Intelligence
This course is about geographic foundations of location intelligence and geographical information system (GIS). The contents cover how GIS facilitate geospatial data analysis and communication to address complex geographic concepts or problems. Understanding how location analytics and technology could practically support business professional to analyse geospatial data from multiple sources and create location intelligence, to empower understanding, insight, intelligent decision making and prediction. Cutting edge topics and applications of location analytics in business will be introduced. The ethical, legal, and societal issues in the field will also be reviewed and addressed. This course combines classroom teaching and hands-on tutorial to learn GIS analytical skills by practice. (This course will be lectured by an instructor from the Science Unit.)
5. Big Data Analytics
This course provides an understanding of the concept and challenge of big data. The focus is on the data analytic techniques to tackle the V’s (volume, velocity, variety, veracity, valence, and value) in big data and how these impacts data collection, monitoring, storage, analysis and reporting. The following topics across the big data domain will be introduced: distributed file systems; big data analysis techniques; high-performance processing algorithms for big data; big data search and query technologies. An example (Apache Spark) of big data management system to manage and process large-scale data is introduced in the course. Big data analytics applications in business will also be elaborated. Students will actively participate in the delivery of this course through assignments, portfolio development, and projects.
Blockchain, as a decentralised open ledger, has proven to be a phenomenal success. This ground-breaking technique holds a huge promise in various fields, digital identification, data marketing, cryptocurrencies like bitcoin, etc. This course introduces students the fundamentals of blockchain, distributed ledger technology, alternative consensus, smart contracts and security, and cryptocurrencies. Case studies of cryptocurrencies and examples of application (e.g., Bitcoin) will be also elaborated. Students will understand the impact of blockchain technologies on financial services and other industries through assignments and projects.
7. CDS505: Mobile Technology and Applications in eBusiness
This course introduces the foundation of mobile technology and the basics for developing mobile applications. The course is also designed for managers to appreciate the business value of innovations in mobile technology as well as the relevant ethical issue.
8. CDS511: Project Management with Software
The principles of project management, largely developed and tested on engineering projects, are being successfully applied to projects of all sizes and types within the business world. Furthermore, the role of project management in a cross section of applications such as information technology, product development, and construction is now emphasised. This course addresses the fundamental principles of project management, and the tools and techniques at our disposal to help achieve our goals. Topics covered include: project definition and start up; project attribute estimation; planning and scheduling; resource selection and allocation, implementation; post-project evaluation; project management as a career; skills and knowledge required by professionals, including decision-making and resource allocation appropriate to project phases; integration with other disciplines, including accounting and finance. The Microsoft Project software tool will be introduced for project scheduling and management.
9. CDS510: Social Media for eBusiness
This course is about the fundamentals of social media for e-business and the steps involved in incorporating social technology into an e-business platform. It equips students with a comprehensive understanding of social media applications and their contribution to the formulation and corporate strategies.
10. CDS515: Business Decision Making with Software
Organisations often need to make decisions in their best interests in different situations, and Microsoft Excel is one of the most popular software that business people use to assist their decision making. This course introduces commonly used quantitative analysis techniques that facilitate scientific and systematic decision making. Students will learn how to employ appropriate decision making techniques to obtain the best solutions for a variety of business problems, and learn about the best-practices of spreadsheet modelling for clarity and communication. Through practicing these techniques and Excel functions, students will develop analytical and computer-based problem-solving skills, which can help them improve their performance at work or in daily life.
11. Project for Artificial Intelligence and Business Analytics
The integrated use of AI techniques and business analytics for solving the real-world problems is a critical ability. This course aims to provide an opportunity for students to integrate their knowledge obtained in other courses that involves the preparation, analysis, reflection and dissemination of data in a chosen research or application setting. The emphasis is on the management and execution of a well-defined project of a suitable scale. The projects may involve either real-world or experimental data and students may engage in such projects in groups. Some example of the projects will be “Convolutionary Neural Networks for Object Detection in Supply Chain Management”, “Financial News Analysis for Stock Market Prediction”, “User perceptions and Opinion Mining from Social Media Data”, “Personalised Recommendations from E-Commerce based on Matrix Factorisation” and so on.