- One year (Full-time)
- Two years (Part-time)
Medium of Instruction
This module aims at introducing the concepts, technologies and tools for dealing with big data, including the key characteristics of big data and its applications, common frameworks for processing big data as well as fundamental techniques in the data science process. Students will have hands-on programming experience in data preparation, exploration, analysis and visualization. In addition, the societal and ethical issues in big data will also be discussed.
This module covers data management theories and systems. In addition to traditional data management concepts using relational databases, Big Data features extremely large volume of unstructured data that traditional databases are not able to handle. The emphasis of the module is on computing techniques to cater Big Data, including distributed storage, database-as-a-service paradigm and NoSQL. Students are also trained with hands-on skills with state-of-the-art systems, e.g., MongoDB.
This module aims to provide students with the data mining techniques for solving practical problems. Students will learn a set of tools for data visualization and apply data mining techniques such as classification, association rules and cluster analysis to analyse real-life problems. Students are required to work effectively in a team to complete a project.
This module introduces modern methods for constructing and evaluating statistical models and their implementation using computing software. It will cover the underlying principles of statistical modeling approaches.
This module targets at recent advances in AI and machine Learning techniques, and their business applications. The module covers AI concepts, machine learning concepts and techniques including supervised learning, unsupervised learning, and reinforcement learning. It also covers deep learning concepts and the TensorFlow open source framework. Students will apply AI and ML algorithms/tools in various business applications (e.g., object recognition).
Select three major elective modules from the following
The objective of the module is to help student to understand what machine learning is and how it can be used in marketing analytics. The module will first introduce to students the major machine learning algorithms that are commonly used in marketing and sales. It will also discuss real examples of using machine learning in marketing scenarios, such as personalizing offers to customers or improving an online customer experience. Students will also learn about the theory, techniques and how to choose the machine learning algorithm that best fit a particular marketing problem in industry.
This module offers an overview of current distributed computing technologies, and distributed algorithms, that underlie today’s cloud computing applications. The concepts and models covered in the module includes: cloud service models (SaaS, PaaS, and IaaS), virtualization techniques, cloud infrastructure and networking, cloud and distributed storage (e.g., key-value/NoSQL stores), distributed algorithms (e.g., leader election, and failure recovery), etc. We will also look at some industry frameworks such as Hadoop, Map/Reduce and Spark.
The module examines the emerging trends and issues in security and privacy of big data applications, such as web and mobile services. Big data analysis could be beneficial to businesses and the society, but in general it involves collection of sensitive or even confidential personal data, which could result in devastating consequences to the person, businesses, and the society if the data are mishandled. Hence, the module focuses on identifying selected aspects of security and privacy issues and challenges in such big data applications. It explores existing approaches and available tools to enforce security and preserve privacy, and equips students with the ability to evaluate the security and privacy of a solution to a security and/or privacy issue.
This module aims to let students learn the state-of-the-art knowledge in the area of data science and artificial intelligence. It covers selected contemporary topics in related fields. The topics vary depending on the latest trends and the expertise of the module coordinator. It also emphasizes on practical knowledge, such as real-life examples and hands-on skills.
This course teaches a range of quantitative methods for decision analytics with emphasis on supply chain management issues. Because of the tremendous amount of information available on the Internet (primarily via social media), it paves the way for the use of analytics to obtain vital information for the planning and control of supply chains. There are two kinds of analytics: data analytics and decision analytics. Data analytics are information methodology that can be used to process data, transforming data into domain knowledge (parameters of the supply chain). Within a supply chain context, such domain knowledge can be further processed by decision analytics: descriptive analytics (supply chain control techniques), prescriptive analytics (supply chain optimisation methodology) and predictive analytics (demand forecasting and resources planning models). In this course, we discuss these two kinds of analytics and show that a proper integration of these two kinds of analytics is required for the successful planning and control of supply chains within a big data context.
Complete and obtain a Grade D or above in 8 modules (24 credits), including five core modules and three major elective modules; and obtain a minimum cumulative GPA of 2.0