Elective Courses

This course introduces the basic concepts in quantitative business analysis to help you gain a clear understanding of the key elements in the decision-making process. We discuss methods that are used extensively in business organizations. These methods provide you with the tools and the skills to approach, analyse, and solve problems of varying scales.

Furthermore, this course aims at improving a decision-maker’s overall problem-solving ability by stressing approaches to 1) understand and question assumptions, 2) consider a richer set of solution alternatives, and 3) consider diverse measures of performance.

Social media and digital advertising have become important sources for understanding the stakeholders of an organization, such as customers, suppliers, and pressure groups. Analysing data from these sources can reveal useful insights for strategic planning and achieving business objectives. This course is designed to help students develop strategies to create and extract value from these phenomena. 

Topics will cover strategic and operational issues pertaining to social media and digital marketing initiatives, metrics to capture and evaluate outcomes, and predictive analysis to link chatter to business performance. Techniques covered will include social network analytics, SEO/SEM, experimental design, text analytics, and sentiment analysis.

This course focuses on the fundamentals of forecasting and predictive models for business. This case-based, interactive course will analyse the applications of these models in various real-world business situations. Students will gain knowledge and hands-on experience in applying various statistical and data-mining techniques to make predictions by identifying patterns and making projections from historical data.

This course introduces simulation models to analyse business processes and solve practical problems. Students will gain basic knowledge such as Monte Carlo simulation and discrete event simulation first, and then be introduced to a simulation package that can be used to evaluate business process performance and, more importantly, facilitate decision making process.

The course is designed to prepare students to apply business strategies, analytical methodologies and information technology in supply chain management. Traditionally industries have focused on operation evaluation and performance improvement of manufacturing process; however, the deficiency of supply chain coordination results in severe downgrade of business competitiveness. 

With advent of information technology, computers not only improve manufacturing operation and management, but also enhance strategic decision making as well. This course focuses on the systems approach to planning, analysis, design, development, and evaluation of supply chain management.

The goal of this course is twofold. First, it aims to equip students with hands-on skills in using spreadsheet modeling and other analytical tools to solve investment and trading problems in finance. Excel spreadsheet and VBA will be used as the primary tool of developing analytics, e.g., building term structure models, pricing options by simulation, etc. The other objective is to introduce Financial Technology (“FinTech”)—one of the most promising sectors in financial industry. 

FinTech start-ups capitalize on technological innovations to provide better financial services with enhanced efficiency. We will have an overview on the active product areas in FinTech, including digital wealth management, payment solutions, and capital-raising. Digital currency and the closely related technology (blockchain) will also be highlighted.

This course offers an in-depth analysis of the internal management accounting systems and their use in firms’ decision-making, planning, and control. Major topics include the different methods of product costing, Differential analysis for decision-making, cost allocation among divisions, design of planning and budgeting systems, and performance measurement issues. Some management control for multi-segment corporations will be discussed. Corporate ethics are also incorporated into the discussions.

Modern business environment involves substantial uncertainty, and meanwhile generates vast amount of data. How can uncertainty be addressed properly and how can data be leveraged effectively in decision making? This course introduces modern optimization methodologies to facilitate decision making under uncertainty in a data-rich environment. Real-world examples will be presented to showcase that these key quantitative methods provide a significant competitive edge.

Application areas include revenue management, digital advertising, online marketing, logistics, and finance. 

Location, concerning the “where” aspect of all businesses, is important everywhere: from long-tradition industries such as, agriculture, banking, real estate, utility services, to modern e-retailing, healthcare, and location-based service platforms like ride-sharing and urban deliveries. Thanks to the overwhelming growth of online purchasing, the penetration of smartphones and locate-aware apps, and the looming prospect of Internet-Of-Things, “every single new 21^st century data source contains location (ESRI).” In such background, it is unsurprising that companies increasingly look for business analysts with geospatial mindset and toolbox to unlock opportunities of growth and better customer services. This course introduces the students to the basic knowledge of geospatial data, system, methods, and helps them develop mindset and toolbox to tackle challenging business problems related to location.

The ability to store, retrieve, and manipulate data using modern database systems is essential to the success of business analytics projects. Besides relational databases that have been widely used in transactional systems and dimensional databases that have been the core of data warehouses, non-relational databases (often referred to as NoSQL databases) have also gained much popularity in business analytics because of their flexibility to handle semi-structured and unstructured data. This course aims to introduce the fundamental principles of the design, development, and administration of various database models for students with no prior experience and knowledge in database management. In this course, students will learn the basic concepts involved in the design and management of relational databases, dimensional databases, and NoSQL databases for business analytics.

The newly emerging capability to rapidly deploy and iterate online controlled experiments to assist decision makings in organizations is one of the most significant innovations in today’s technology industry. As more and more social interactions, decisions, opinions, and transactions are mediated by online platforms, digital experiments are becoming increasingly crucial for firms to understand their user behaviors and make product decisions. This course will cover the most cutting-edge digital experimentation methods used in daily operations at large technology firms, such as Tencent, Alibaba, Facebook, and LinkedIn. We will also share the key lessons and pitfalls encountered in practice. Topics include the statistics behind experiments, experimental design, methods of analyzing experiments, networked experiments, quasi-experiments, A/B testing platform and culture in organizations, and recent development in digital experimentation. Students will also learn how to conduct and analyze online experiments using programming languages, such as python, in assignments and a course project.

Note: This course is not open to candidates who have taken or are taking MSBA7037 or MSMK7034.

Big data capabilities become more essential to the success of a modern enterprise. Business analytics and data engineering practitioners are seeking to help organizations harness their large-scale data for actionable insights. That warrants a development of analytics capacity under scalable environment, such as Apache Spark and cloud computing. This course aims at introducing fundamental concepts of big data and developing analytics skills on big data. Students will learn relevant cloud computing services and big data technologies to design and implement analytics initiatives, ranging over machine learning, network analytics, and text mining, through realistic business cases.

This course provides systematic training of machine learning, which is the foundation and core of data analytics. It covers topics such as nonlinear techniques, tree-based methods, boosting, ensemble learning, recommendation systems, anomaly detection, machine learning system design and interpretability. This course will be helpful for students who are interested in other elective classes such as MSBA7012, MSBA7013, MSBA7023, MSBA7026, MSBA7028, etc.

Deep learning being the core of artificial intelligence, has become more and more important. This course provides a broad introduction to deep learning, including fundamental concepts, state-of-the-art algorithms for data analysis, and their application to real cases.

Storytelling with data is an essential skill in today’s data-driven business world. This course demonstrates how to better understand your data, present clear evidence to your intended audience, and tell engaging data stories that highlight your business ideas visually and with logic. Through interactive lectures, demonstrations, and case studies, students are expected to learn the best practices of making persuasive arguments, critical presentation design principles, and data visualization skills.

The course will focus on innovations in both analytics and technology-driven business models. The course contents will cover contemporary topics on innovation strategies, business analytics, platform economics, business model development, disruptive technologies, digital experiments, cryptocurrency, information privacy, etc. In particular, students will learn how to identify innovation opportunities and manage innovation processes. They will also be able to master the ability to develop digital business models.

Quantitative Trading is an interdisciplinary subject involving a combination of techniques in statistical analysis, feature engineering, time series forecasting, machine learning and portfolio optimization. Through this course, students will get exposure to basic asset pricing for financial markets and how state-of-the-art quantitative techniques may be applied in real trading decisions.

This course provides a comprehensive introduction to generative artificial intelligence (AI) and its applications in business contexts. Students will learn about the evolution and background of generative AI, in the context of decision-making, explore the various applications of generative AI in different business contexts, and examine the risks and opportunities associated with its use.

This course is designed to provide students an opportunity to acquire first-hand knowledge of international business environment. Experiential learning and knowledge exchange will be applied throughout the study field trip to develop students’ understanding of international business environment in a different cultural context and help them construct a global perspective. Firm visits and cultural related activities will be arranged during the study field trip to arouse students’ awareness in cultural diversity and enhance their understanding of how culture can impact international business strategy.
Note: This course is not open to candidates who have taken or are taking MACC7025, ECON6100, MFIN7057, PMGM7016 or MSMK7020.

This Applied Large Language Models course offers students a comprehensive introduction to the latest advancements in LLMs, along with hands-on experience using state-of-the-art tools and techniques. Upon10 completing this course, students will be proficient in implementing LLMs across various applications, enhancing their professional skills and increasing their desirability in the job market.

This course is designed to explore the ethical implications of AI technologies in real-world applications. Students will study the intersection of AI and ethics for social good, learning philosophical foundations, fairness, bias, civility in communication, misinformation, privacy, and green AI. Through hands-on experience and assignments, students will develop skills in analyzing large-scale data sets, applying machine learning libraries, and building ethical AI systems.

This course offers an in-depth exploration of cutting-edge A/B testing techniques used in the daily operations of major technology firms, driving product innovation. We will also delve into key insights and common challenges encountered in practical scenarios. Topics covered include the statistical underpinnings of experiments, experimental design, methods for analyzing experimental data, the integration of A/B testing platforms and culture in organizations, recent advancements in A/B testing, and the principles of observational causal studies. Given the role of A/B testing as a pivotal tool in product management, this course emphasizes its application in this significant context. Participants will gain hands-on experience in conducting and analyzing online experiments using programming languages such as Python, complemented by assignments and an extensive course project.

Note: This course is not open to candidates who have taken or are taking MSBA7025 or MSMK7034.

You can take up to two electives from the Master of Accounting, Master of  Economics, Master of Finance, Master of Finance in Financial Technology, Master of Global Management or Master of Science in Marketing programme at HKU. Enrollment in electives from other programmes is subject to seat availability and approval by the Programme Directors concerned based on your profile, capabilities and performance in the MScBA programme.

Since enrollment in other taught postgraduate electives is not guaranteed, you should always choose five MScBA electives during the course enrollment in our programme. Course enrollment results of other programmes may only be confirmed after that course has started. If your enrollment is successful, you can drop the MScBA elective(s) and enroll in the other taught postgraudate elective(s).

It is your responsibility to make sure you obtain 60 credits to fulfill the graduation requirements and there is no overlapping of classes and exams in courses from different programmes.

*The list of available electives from other programmes may have prerequisite requirement(s) and is subject to change for future intakes.