The goal of this course is to prepare you to be an effective leader, group member, and manager of others regardless of your career path and to be a good analyst of how best to organize people. The course will accomplish these goals by focusing on many different knowledge bases and skill sets. These include: What principles can you draw on to analyze and improve performance in organizations? How can you exert influence for positive results at any level of an organization? How can you maintain high ethical standards? We will examine principles for designing incentive systems, motivating employees, running effective teams, making good decisions, negotiating, harnessing diversity, and organizing the distribution of work.

Marketing is one of the foundation courses of business education. From a student's point of view, a comprehensive, broad-based interdisciplinary training is consistent with the development of higher education, which will also benefit students. Students who have a basic understanding of marketing will be more competitive in the future job market. This course will study marketing theory systematically, help students to construct an overall knowledge framework of marketing, and use relevant theories to reveal the principles behind daily business activities. This course will use textbooks, business cases, experimental surveys and secondary data analysis.

This course mainly introduces the development status and application of management information system, including Introduction to management information systems, globalization of e-commerce, business models, database management, information systems and corporate strategies, information system ethics issues, emerging information technologies, business intelligence, information system security.

The course is designed to provide the student with the essential knowledge needed to analyze, evaluate, design, implement, and administer the business database. The topics include the terminology and fundamental concepts associated with the relational and object-oriented database processing; The design and implementation of a relational database; and the application of SQL using PostgesSQL.

The objective of this course is to introduces the fundamental concepts of data structures in business and the algorithms that proceed from them. Although this course has a greater focus on theory than application, the assignments, examples, and cases introduced throughout the course help to bridge the gap between theoretical concepts and real-world problem solving. We will enhance the understanding of the operations and functions of the data structures and algorithms explored throughout the course by visualizing examples of data structures and algorithms. Key topics within this course include recursion, fundamental data structures (including stacks, queues, linked lists, trees, and graphs), and the basics of algorithmic analysis.

The aim of this course is to introduce management science philosophy, techniques, models and application for informed managerial decision-making. Topics covered include: linear programming, distribution and network models, project management, inventory models, queueing models, simulations, forecasting. This course is designed as an introduction to the field of Operations Management/Operations Research (OM/OR). The focus will be on the application of the scientific approach to decision-making. Small case studies will be covered. It is not our intent to train OR theoreticians in this course. Rather, we seek to convey an appreciation for what an OR analyst does and why it is important. Thus, students who successfully complete this course are expected to have built the capability for modeling business related problems and prepare to get solutions by OR techniques.

This course will provide an overview of data governance and business model. It will cover building a governance infrastructure based on business model, agents' roles and responsibilities, stewardship, governance communications, regulatory compliance, privacy concerns, data ethics, and risk management. This course will address data quality as a continuous issue in data management. It will emphasize the challenges of data quality in the context of big data as volumes of data increase and the uses for data expand. This course will utilize case studies, trends, techniques, and best practices as it examines the topics of data governance and business model.

The challenge of data mining is to transform raw data into useful information and actionable knowledge. Data mining is the computational process of discovering patterns in data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and data management. This course will introduce key concepts and techniques in data mining, including specific algorithms and techniques for classification, regression, association analysis, clustering, outlier detection, etc. By taking this course, you will be given a broad view of the general data mining algorithms as well as their applications in business. At a practical level you will have the chance to explore an assortment of data mining techniques which you will apply to problems involving real-world data.

This course is designed to instruct the theoretical foundations as well as applications about deep learning algorithms for high grade undergraduate students. From the theoretical perspective: the course will deliver: 1) fundamental machine learning theories related to deep learning algorithms, and 2) underlying theoretical mechanism of deep learning models. Also, from the application perspective: the course will guide the students to master: 1) How to use and apply deep learning models, and 2) How to design and develop models in real life settings

This course starts from individual's and business' needs for Big Data analysis. We will then introduce the basic concepts, theories, and methods of Econometrics and Big Data Statistics such as statistical analysis, econometric analysis, data modeling, model selection, and optimal decision making. Besides learning about the underlying theoretical models and tools, a lot of emphasis will be placed on real world applications and economic interpretations. Students will learn how to apply R to perform big data analysis on real-world economic data.

This course introduces students to data-driven analysis, modeling and decision making for complex real systems based on discrete-event simulation. We mainly focus on problems that have no closed-form solutions but with abundant data resources. The course provides a solid mathematical/statistical grounding in simulation and some tools to solve actual problems. It will cover data collection and input data analysis, modeling techniques, random number generators, discrete-event simulation approaches, simulated output data analysis, simulation variance reduction techniques and state-of-the-art simulation software.

During the course, we will share knowledge and examples related to innovation and entrepreneurship, discuss about innovation and entrepreneurship opportunities and procedures that are suitable under the current environment of technology and business development in Shenzhen, the Greater Bay Area, and China, and analyze advantages and disadvantages for college students to participate in innovative and entrepreneurial activities. This course will help students to know about innovation and entrepreneurship, and create their own innovative and entrepreneurial ideas, which will be useful for their participation in innovative and entrepreneurial competitions, design of final year projects, and career development, in the future.

A significant portion of modern commercial activity is dependent on electronic commerce. In this course: 1: The student will gain familiarity with common e-commerce business models and get an understanding of how and when they are used. 2: We shall cover important enabling technologies, including basics of internet communication, security, clouds, as well as low level technology enabling functions such as localization and tracking. 3: Several important applications in various sectors of industry, including visualization and analysis as well as ELogistics will be introduced.

This course is designed for students to acquire core conceptual frameworks, strategies, and tools in social media and digital marketing, including introducing the background and the fundamentals of social media and digital marketing, discussing the typical examples or cases in social media and digital marketing, learning the basics of digital marketing analytics. Upon completing this course, students will learn the background on what is known about social media and digital marketing, including the business implications of social media such as blogs, micro blogs and product reviews, social networking platforms, viral marketing, search engine advertising and optimization, digital advertising, mobile marketing, influencer marketing, live stream marketing, leveraging the wisdom of the crowds such as open innovation, crowdsourcing.

Negotiation is the art and science of securing agreements between two or more interdependent parties. This course will help you to understand the theory and processes of negotiation as practiced in diverse settings, recognize the components of an effective negotiation, and analyse your own behaviour in negotiations. The course will be largely experiential, giving you an opportunity to develop your skills by participating in numerous negotiation exercises and integrating your experiences with the principles presented in the readings and the class discussions.

This course is mainly devoted to the methodology and application of behavioral and experimental methods in economics and management. Given the recent growth of interest in real behavioral considerations, experiments are increasingly used in economics to study human behavior. With their help we now have a much better understanding of individual and market behavior. Students will overview some of the most important existing behavioral and experimental work and learn how to design their own experiments and prepare to run them. They will also master randomized control trial (RCT), the golden rule of quantitative economic, management, and social science studies, policy evaluation, and causal inference. The course also briefly introduces behavioral finance, i.e., how individuals and firms make financial decisions in a way that deviates from those predicted by traditional financial or economic theory, and how this affects various financial practices.

This course introduces advanced topics related to the operations of an organization (e.g., companies, non-profit organizations and government). The objective of this course is to provide students with the ability of modeling, mathematically analytical skills and managerial insights to critically understand/analyze/optimize an organization's operations decisions and practices. Such training prepares students in dealing with various problems in production and operations management. Also, such knowledge is important for careers in a variety of areas, including general management, entrepreneurship, investment banking (e.g. business restructurings, mergers and acquisitions), venture capital (e.g. evaluating new business plans) and management consulting (business restructuring improvement).

This course introduces basic stochastic models and processes including Poisson processes, renewal theory, discrete- and continuous-time Markov chains, queueing theory, etc., and the application of these concepts to problem solving in business and management.

Starting from the principles of ERP system, this course discovers the development of ERP and the role of each module, and on this basis, it uses specific ERP system to train students on the application of real system.

The course provides theoretical views, research methods, and empirical findings in the field of consumer behavior. This course originates from research in behavioral economics, social psychology, and marketing, and intends to introduce a broad scope of consumer behavior. Through this course, students will not only learn theories in consumer behavior, but also understand the rationale of experiment design and causal relationship.

This course studies the nature, form and scope of international business. It covers both the macro (contextual environment) and micro (business functions) aspects of the topic. In terms of the context in which international business operates, we will discuss the political, economic, and cultural environment. The emphasis then shifts to how firms operate within this global environment. We will discuss the impact of FDI on host countries' economies, and how host governments evaluate the effects of foreign direct investment (FDI). We will also discuss how firms carry out their production and financial management activities.