Faculty: Prof. Haipeng Shen

Associate Dean (Executive Education), Patrick S C Poon Professor in Analytics and Innovation, Faculty of Business and Economics, HKU

Learning outcomes:
Firms are creating and consuming vast amounts of information, leaving behind a trail of digitized (big) data. To be successful, firms need to use the data to drive their decision-making, often under uncertainty due to incomplete information. However, much of the promise from the information-rich digital recording of firm activities has failed to materialize as managers often find it difficult to translate (big) data into actionable policies, and generate business value. This module offers discussions at both strategical and operational levels. We start with introduction of big data and artificial intelligence through examples. We then discuss the analytics triangle of Question – Data – Analytics, and the right balance among them needed for effective data-driven decision making. We demonstrate how to ask “crunchy” questions with cases and problem sharing from the participants, and then introduce analytics techniques using examples from operations analytics and healthcare analytics. We also hear from guest speakers/past programme participants, and conclude with an outlook of the future modules.

• Strategic Considerations of Data Analytics
• The Analytics Triangle: Question, Data, Analytics
• How to Ask Crunching Questions
• Pitfalls of Big Data
• Analytics Techniques
• Operations Analytics
• Predictive Analytics
• Healthcare Analytics/Precision Medicine
• Problem Workshop Discussion

Pre-requisites: basic knowledge of data analysis (such as linear regression)
Software used in class: JMP

Faculty: Prof. Xin (Shane) Wang

Assistant Professor, Marketing and Statistics, Ivey Business School

Learning Outcomes:
The first decade of twenty-first century is the era of business analytics. The trends and innovations that have shaped the technology industry over the past several years. For example, Cloud computing has gone mainstream for many enterprises, and the Internet of Things (IoT) is changing how both industrial and consumer-oriented companies do business. This course will give your insights on latest trends in technology development locally, regional, and globally and the implications to the business world. The most important, you will see the technology innovation in property industry. Machine learning has become something of a buzzword over the past few years and it is already starting to be integrated within several property platforms. For example, using machine learning for classification, genuine property listings as opposed to common ‘bait and switch’ properties can be identified and removed from online listing sites. The topics contain machine learning, artificial intelligence, image recognition, visual information processing, and Blockchain. Finally, you'll have a chance to put your knowledge to work in simulated business settings.

Web and Search Engine

• Search engine optimization
• Online advertising (Pay-per-click advertising)
• Measurement and prediction of ad effectiveness

Online Social Networks

• Structure of social networks
• Leverage online social networks
• Engagement on social networks

Online Reputation

• Managing and analyzing social interactions
• Online word-of-mouth
• Platform strategy

Mobile Strategy

• Leverage mobile internet
• Location-based targeting
• Omni-channel marketing

Pre-requisites: basic knowledge of spreadsheet software (e.g., Microsoft Excel)

Faculty: Prof. Michael Chau

Associate Professor, Faculty of Business and Economics, HKU

Learning Outcomes:
Massive volumes of data are available to organizations for analysis to gain managerial and strategic insights. Such data include online transactions, mobile applications, sensors, video-capturing systems, and most notably, social media. It is important for organizations to know the various state-of-the-art technologies that can manage and analyze these “big data”, and know the power and limitations of such technologies. In this module we will look at what platforms are necessary for handling massive data and how data mining and text mining technologies have been applied in various business settings for gaining competitive advantages. We will also have a hands-on session on data visualization and business intelligence dashboards.

• Technologies and platforms for processing big data
• Data analytics and mining
• Customer segmentation using data classification and clustering
• Basket analysis (association rule mining)
• Other example applications of data analytics
• Text analytics and natural language processing
• Sentiment analysis on online opinions
• Monitoring business performance through BI dashboards
• Visualizing data using Tableau

Pre-requisites: basic knowledge of spreadsheet software (e.g., Microsoft Excel)
Software used in class: Tableau

Faculty: Prof. Ning Su

Associate Professor, General Management, Strategy & Information Systems, Ivey Business School

Learning outcomes:
This course is designed to offer an executive perspective on big data and related emerging technologies, and their impact on both private and public sectors. The course encompasses key concepts, analytical frameworks, real-life case studies, and future trends related to big data and digital technologies, with a focus on managerial decision-making in an increasingly digitized and globalized business environment. Specifically, we will examine the complexity of building technology platform for big data, analyze examples of leveraging big data in a competitive industry environment, discuss models and practices for acquiring big data capabilities, explore emerging markets and emerging technologies, and investigate the impact of big data on the future of global business, economy, and society.

Building technology infrastructure for big data

• Analyzing the complexity of building large-scale data infrastructure

Acquiring big data capabilities

• Examining how to utilize big data to improve performance in changing industry environments
• Sourcing big data capabilities
• Examining how to obtain big data capabilities through strategic sourcing

Developing a big data program

• Applying learning to creatively utilize big data for value creation

Exploring emerging markets

• Exploring China’s emerging “big data” economy and its impact

Managing data and technology projects

• Simulation: Experiencing rapid prototyping
• Understanding managerial issues in data and technology projects

Exploring emerging technologies

• The future of big data