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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.
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 class is an introductory undergraduate course in machine learning. We will cover the most common machine learning techniques, discuss how to apply them in real-world- and business-related contexts and learn about common pitfalls and misconceptions. Basic understanding of probability theory, statistics and programming are expected. Code
templates will be provided in python, but other programming languages are accepted to solve the exercises.
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.
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.
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 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 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.
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 helps students to understand what a business model is, analyze an enterprise's own business model, transform the business model's improvement plan, and understand how to seek the best profit model of the enterprise through business model innovation and enhance the company's value. Understanding the relationship between technological innovation and business model innovation.