科目概览

This course covers statistical techniques for inferential statistics, such as estimation and hypothesis testing for population parameters. The topics covered include point estimate and interval estimate of population parameters, comparing means, non-parametric techniques and the use of statistical software for data science. 

This course introduces students the fundamental concepts and principles of databases and data warehousing. Various types of databases will be covered in this course, such as objected-oriented, relational, document-oriented, graph, NoSQL, and New SQL. Popular database management systems such as Microsoft SQL Server and/or Oracle will be described and implemented. Topics include data models (ER, relational, and others); query language (Structured Query Language); implementation techniques of database management systems (index structures and query processing); management of semi-structured and complex data; distributed and NoSQL databases; the dimensional modelling technique for designing a data warehouse, and data warehouse architectures, OLAP and the project planning aspects in building a data warehouse.

This course teaches the core principles and ideas of data mining. It covers a range of data mining approaches used to extract knowledge from vast amounts of valuable databases in diverse fields such as business, finance, urban planning, and medicine. Additionally, students will develop quantitative analytical skills to interpret data mining models. The course also covers current IT skills that are essential for working with large databases, such as database management and programming. By the end of the course, students will have a comprehensive understanding of the theory and practice of data mining and will be equipped with the necessary skills to extract valuable insights from databases. 

This course aims at providing an intensive 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 optimize the neural networks for practical applications. The course will cover deep neural network models including convolutional neural networks, recurrent neural networks, long short-term memory networks, deep residual networks, generative adversarial networks, attention-based models, adversarial learning models and training techniques including dropout, batch normalization, 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. 

This course covers the concepts, techniques, and business applications of machine learning. It will cover the supervised, semi-supervised, unsupervised, transfer, and reinforcement learning paradigms. The techniques include regression, gradient descent, probability generative model, logistic regression, neural networks, support vector machine, backpropagation, principal component analysis, word embedding, q-learning, policy gradient and so on. The business application examples of machine learning will be included in this course.

This course is an advanced course tailored to meet the increasing demands of research postgraduate students from Lingnan University embarking on research journey in the realm of data science and interdisciplinary research. This course will delve into the foundational principles, methodologies, and tools used in data science research, providing students with the skills and knowledge needed to plan, design, execute, and evaluate data-driven research projects effectively. 

This course provides students the chance to demonstrate innovative abilities and initiatives in data science problems. Students will be required to carry out independent work on a major project, which can be theorical or practical, under the supervision of individual staff member and another co-supervisor from industry. The course develops the capability to integrate and apply data science knowledge and data analytical skills to different scenarios. The course also serves as a platform of presenting and sharing novel investigations of academic and/or industrial problems in real-world via data science knowledge.  

This course offers students the opportunity to conduct research-oriented study in an area of advanced data science under supervision of faculty members, allowing students to deeply engage with cutting-edge topics and contribute new knowledge to the field. Under the guidance of a faculty supervisor, students will identify a research question, design and implement a rigorous methodology, analyse results, and present findings in a comprehensive thesis. The course emphasizes the application of advanced data science techniques to solve complex, real-world problems or explore theoretical advancements. Throughout the course, students will develop crucial research skills, including literature review, experimental design, data collection and analysis, and academic writing. Regular progress presentations and peer reviews will hone students' ability to communicate complex ideas effectively. 

This course introduces fundamental concepts and design principles in cybersecurity as well as highlight different methodologies of protecting information and data in the cyber world. Topics include CIA (Confidentiality, Integrity, and Availability); introduction to security; cyber-attacks and threats; cryptographic algorithms and applications; network security and infrastructure. 

With the rapid development of cloud resources, majority of traditional data centres has been replaced by cloud platforms (such as Amazon Web Services, Microsoft Azure, Google Cloud Platform) due to the limited bandwidth of resource. A qualified data scientist needs to equip with cloud computing skills, learning to perform a series of tasks in the data pipeline on the cloud such as data acquisition, data cleansing, data transformation and data mining, as well as model training and testing. This course aims to examine the latest trends of cloud computing and provide students with the fundamental knowledge of cloud computing. Students will learn the best practices in deploying and implementing cloud computing applicable for unique business requirements. The course discusses the conceptual topics of cloud technologies and provide hands-on experience through projects utilizing public cloud infrastructures. Topics include cloud delivery models (SaaS, PaaS, and IaaS); Cloud computing overview; Public cloud infrastructure, On-demand self-service and resource pooling; rapid elasticity; measured service; cloud storage architecture (data distribution, durability, consistency and redundancy); data deduplication; cloud security issues; case studies of current cloud computing platforms. 

This course is an introduction to Natural Language Processing. It covers a brief overview of the field, including the cutting-edge text processing tasks (e.g., text summarization, named entity recognition, document classification, etc.), their computational problem setting and general thoughts of methodologies. State-of-the-art techniques will also be discussed, including generative sequence-to-sequence models, multimodal data modelling (e.g., image-to-text, video/audio-to-text), chatbot, question-answering system, topic modelling, etc. 

This course will introduce the basic techniques for image data processing and analysis. Topics include image processing and analysis in spatial and frequency domains, image restoration and compression, image segmentation and registration, morphological image processing, representation and description, feature description, face recognition, iris recognition, fingerprint recognition, image analysis topics, such as medical image analysis. 

This course introduces operations management in real-world situations (e.g., manufacturing and service industries). Students will learn how to design, operate, and improve processes to increase efficiency and effectiveness. The course covers key topics such as process design, capacity planning, inventory management, quality control, and supply chain management. Students will learn the importance of operations management, as well as the various techniques and strategies used to optimize processes and improve organizational performance. 

This course introduces students with data visualization techniques. It introduces visualization techniques for data in everyday life such as business, scientific computing, social media, medical imaging, etc. Such data can include CT/MRI data, graphs and networks, time-series data, text and documents, Twitter data, and spatio-temporal data, etc. 

This course introduces social analytics techniques such as (social) information network analysis and engineering. Students will learn both mathematical and programming knowledge for analysing the structures of typical social information networks (e.g. Facebook and Twitter). They will also learn how to use the social metrics to evaluate the quality and structure of social network. It will cover topics such as small world phenomenon; contagion tipping and influence in networks; models of network formation and evolution; the web graph and PageRank; social graphs and community detection; measuring centrality; greedy routing and navigations in networks; introduction to game theory and strategic behaviour; social engineering; and principles of computer system design. 

This course provides a comprehensive introduction of Mobile Edge Computing (MEC), a key enabler of 5G networks. The covered topics may include communication and computation basics for MEC, computation offloading, communication and computation resource management, edge caching, MEC application scenarios (e.g., Internet of Things, AR/VR), MEC hardware platforms and standardization. Edge AI is treated as a key application case, which is to support AI applications on resource-constrained devices with the help of MEC servers.

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, reasoning, and learning. It includes topics about search, logic, genetic algorithms, and computational learning and some potential business applications like expert systems, news analysis and so on. 

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. Apache Hadoop and Spark are examples of a big data management system to manage and process large-scale data. The course covers topics across the big data domain, such as distributed file systems; similarity search techniques and multimodal data processing. Big data analytics applications in data science will be elaborated. Students will actively participate in the delivery of this course through assignments, portfolio development, and projects. 

Blockchain, as a decentralized 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.  

This course aims to introduce students the power of healthcare analytics, which can enhance the traditional medical system in multiple ways by improving its efficiency, convenience, and personalization. Students will learn about key technologies that underpin smart healthcare, including the Internet of Things (IoT), which enables the collection and transmission of data from various wearable sensors. In addition, students will be exposed to data fusion techniques that integrate healthcare data from multiple sources. The course will delve into data models, data mining, and analytics techniques relevant to forecasting. Practical case studies and examples of application will be provided, helping students to understand how these technologies are being used in practice to improve healthcare.