Programme Descriptions
The programme is divided into two majors: Information Management and Business Analysis. Students choose one of the majors according to their interests.
Programme Descriptions
The standard duration of the IMBA master's program is two years, using the credit system
Graduation Requirements: 6 compulsory courses+2 professional electives+4 electives=12 courses/36 credits
Information Management Direction
This direction cultivates cross-border professionals and business leaders who are proficient in both information technology and business management. Relevant courses include information management, system design, data analysis and computer science. Through a learning method that combines theory and practice, students learn to transform data into effective business information for organizational decision-making and enterprise digitalization.
Business Analysis Direction
This direction develops cross-border professionals and business leaders who are proficient in data analysis and business modeling. Relevant courses include data analysis, optimization and algorithms, machine learning and computer science. Through a learning method that combines theory and practice, students apply analytical methods to realize the commercial value of big data applications in important fields such as finance, technology, e-commerce, and health.
Compulsory Courses
Statistical Foundations for Analytics
This course provides a comprehensive and practical introduction to statistical data analysis. Topics cover discrete and continuous probability models, estimation and testing of hypotheses, simple and multiple linear regression analysis, and so on. Throughout the course, students will learn concepts and fundamentals of statistical inference and regression analysis by studying theory, developing intuition, and working through several practical examples.
Machine Learning for Business
The course offers a broad introduction to modern machine learning algorithms. It provides a foundational understanding of how machine learning and statistical algorithms work. A number of classic topics in supervised learning and unsupervised leaning will be covered, such as (1) linear and generalized linear models; (2) linear discriminant analysis; (3) support vector machines, boosting, and other regularized learning algorithm such as the LASSO and elastic net; (4) decision trees, k-nearest neighbours, factor analysis, principle components analysis, and naive Bayes; (5) leaning theory (bias variance trade-off). The course will also discuss recent applications of machine learning, such as to image, text and web data processing, and causal inference in business.
Programming for Business Intelligence
This course highlights the importance of information in businesses , providing students with a comprehensive understanding of information management, intelligence analysis, and data visualization techniques to enhance competitiveness. Through the exploration of various programming languages and tools, students will gain essential skills in business intelligence and analytics, as well as learn the fundamentals of effective data visualization and data processing.
Fundamentals of Database Management
This course introduces database management systems with emphasis on business applications. Topics covered include the different natures of data, selection and representation, use of suitable methods and tools for storing and accessing data, technical and administrative considerations in database implementation.
Economic Analytics
The course emphasizes on the applications of economic modelling and econometric analysis in operation and strategic issues. It introduces principles and tools derived from the studies of information economics, games, and industrial organization, as well as causal inference in econometrics. Students are expected to gain skills to make effective managerial decisions and strategic choices based on quantitative analysis of a firm's capabilities and the market, in which it operates.
System Analysis and Design
This course covers the development lifecycle of information systems. While the course introduces students to the whole systems development process, it focuses on the modelling of information systems requirements that enable identification of information problems and the prototyping of an efficient solution to those problems in an organization. Students will gain knowledge and experience in requirements modelling, systems analysis and feasibility assessment within a system development project aimed at developing a modern information system. They will also obtain practical experience in the use of a CASE tool to produce object and class definitions and to create models.
Statistical Foundations for Analytics
This course provides a comprehensive and practical introduction to statistical data analysis. Topics cover discrete and continuous probability models, estimation and testing of hypotheses, simple and multiple linear regression analysis, and so on. Throughout the course, students will learn concepts and fundamentals of statistical inference and regression analysis by studying theory, developing intuition, and working through several practical examples.
Machine Learning for Business
The course offers a broad introduction to modern machine learning algorithms. It provides a foundational understanding of how machine learning and statistical algorithms work. A number of classic topics in supervised learning and unsupervised leaning will be covered, such as (1) linear and generalized linear models; (2) linear discriminant analysis; (3) support vector machines, boosting, and other regularized learning algorithm such as the LASSO and elastic net; (4) decision trees, k-nearest neighbours, factor analysis, principle components analysis, and naive Bayes; (5) leaning theory (bias variance trade-off). The course will also discuss recent applications of machine learning, such as to image, text and web data processing, and causal inference in business.
Programming for Business Intelligence
This course highlights the importance of information in businesses , providing students with a comprehensive understanding of information management, intelligence analysis, and data visualization techniques to enhance competitiveness. Through the exploration of various programming languages and tools, students will gain essential skills in business intelligence and analytics, as well as learn the fundamentals of effective data visualization and data processing.
Operations Analytics
This course is an introduction to the principles and techniques of operations analytics. Topics covered include process analysis, inventory management, quality management, supply chain management. A set of quantitative and qualitative techniques will be covered to help students analyse and solve the operation problems.
Economic Analytics
The course emphasizes on the applications of economic modelling and econometric analysis in operation and strategic issues. It introduces principles and tools derived from the studies of information economics, games, and industrial organization, as well as causal inference in econometrics. Students are expected to gain skills to make effective managerial decisions and strategic choices based on quantitative analysis of a firm's capabilities and the market, in which it operates.
Quantitative Decision Models
This course presents modelling techniques in optimization that are known as linear programming, integer programming, and nonlinear programming. Applications to managerial decision problems in different industries including finance, accounting, human resources, and marketing will be discussed. Students are expected to model managerial decision problems using spreadsheet optimization, e.g., Excel and Excel Solver.
Major Electives
Internet Analytics and Business Intelligence
暂无简介
Blockchain Tech and Applications
Upon satisfactory completion of this course, students can expect to (1) understand the fundamental concepts in blockchain, such as cryptocurrency and consensus algorithms; (2) be able to read and argue about blockchain issues in a professional setting; (3) know the core concepts, methods, techniques, and tools for the development of blockchain solutions for various business contexts, such as finance, healthcare, and manufacturing; and (4) critically evaluate current trends in blockchain technology and their manifestation in various industrial sectors. This course requires students to work in group to develop a prototype system by using a blockchain framework and user-friendly tools. such as Hyperledger composer and fabric.
Simulation and Field Experiment
This course introduces simulation models to analyse business processes. A simulation package will be introduced and utilized to evaluate business process performance, and to facilitate the decision making on business process improvement. Knowledge learnt from the course can equip students with scientific competence and help them solve practical problems in various settings related to business process management.
Fintech and Applications
The objective of this course is to provide students with Fintech theory and practice. This course covers the applications of new technologies including big data, block chain, and artificial intelligence (AI) in financial services, the new forms of financial services, such as peer to peer lending and crowdfunding, cryptocurrencies, and Fintech regulations. Representatives from banks, hedge funds, and insurance companies will share recent Fintech development of their companies with the students.
Elective Courses
Elective courses offered by other Master programmes
暂无简介
Internship or Capstone Course
Big Data Analytics
This course examines the complexities of data mining and the tools and techniques currently used by companies to extract information from the data. Topics include Data Mining, Text and Web Mining, Social Network Analysis, Sentiment Analysis, Recommendation Systems, and Mobile and Location based Business Analytics. Examples of business problems to be solved analytically include customer relationship management, financial trading, social media marketing, search engine strategy, etc.
Supply Chain and Logistics Management
The course is designed to prepare students to apply business strategies, analytical methodologies and information technology in supply chain management. It conveys both the intuitions behind many key supply chain and logistics management concepts, and to provide simple techniques that can be used to analyze various aspects of the supply chain and logistics management. The systems approach to planning, analysis, design, development, and evaluation of supply chain management will be introduced.
Fintech Theory and Practice
The objective of this course is to provide students with Fintech theory and practice. This course covers the applications of new technologies like big data, block chain, and artificial intelligence (AI) in financial services, the new forms of financial services such as peer to peer lending and crowdfunding, cryptocurrencies, and Fintech regulations. Representatives from banks, hedge funds, and insurance companies will share the recent Fintech development in their companies with students.
Managing Service Operations
The focus of this course is to develop analytical thinking skills that will enable students contemplating careers in services to develop, evaluate and implement strategies for a wide range of organizations in the service sector. Topics include analyzing service processes using queueing models, improving service process with lean concepts, and analyzing customer behavior data and improving quality of service delivery.
Project Management
This course introduces simulation models to analyse business processes. A simulation package will be introduced and utilized to evaluate business process performance and to facilitate the decision making on business process improvement. The knowledge learnt from the course can equip students with scientific competence and help them solve practical problems in various settings related to business process management.
Artificial Intelligence and Internal Control
Blockchain Tech and Applications
Upon satisfactory completion of this course, students can expect to (1) understand the fundamental concepts in blockchain, such as cryptocurrency and consensus algorithms; (2) be able to read and argue about blockchain issues in a professional setting; (3) know the core concepts, methods, techniques, and tools for the development of blockchain solutions for various business contexts, such as finance, healthcare, and manufacturing; and (4) critically evaluate current trends in blockchain technology and their manifestation in various industrial sectors. This course requires students to work in group to develop a prototype system by using a blockchain framework and user-friendly tools. such as Hyperledger composer and fabric.
Data Communications and Networks
This course develops a managerial level of understanding about technical knowledge and terminology for data, voice, image, and video communications and computer networks to effectively communicate with technical, operational and management people in telecommunications. Students are expected to understand and apply various data communications concepts to situations encountered in industry; learn general concepts and techniques of data communications; understand the technology of the Internet; and understand the regulatory environment. Potential topics include telecommunication media, modulation techniques and multiplexing, network hardware, software, and services, privacy, security, and reliability considerations, LAN, MAN, and WAN and internetworking, telecommunications standards, policy and standards-making organizations.
Elective courses offered by other Master programmes
暂无简介
Internship or Capstone Course
Big Data Analytics
This course examines the complexities of data mining and the tools and techniques currently used by companies to extract information from the data. Topics include Data Mining, Text and Web Mining, Social Network Analysis, Sentiment Analysis, Recommendation Systems, and Mobile and Location based Business Analytics. Examples of business problems to be solved analytically include customer relationship management, financial trading, social media marketing, search engine strategy, etc.
Supply Chain and Logistics Management
The course is designed to prepare students to apply business strategies, analytical methodologies and information technology in supply chain management. It conveys both the intuitions behind many key supply chain and logistics management concepts, and to provide simple techniques that can be used to analyze various aspects of the supply chain and logistics management. The systems approach to planning, analysis, design, development, and evaluation of supply chain management will be introduced.
Blockchain Tech and Applications
Upon satisfactory completion of this course, students can expect to (1) understand the fundamental concepts in blockchain, such as cryptocurrency and consensus algorithms; (2) be able to read and argue about blockchain issues in a professional setting; (3) know the core concepts, methods, techniques, and tools for the development of blockchain solutions for various business contexts, such as finance, healthcare, and manufacturing; and (4) critically evaluate current trends in blockchain technology and their manifestation in various industrial sectors. This course requires students to work in group to develop a prototype system by using a blockchain framework and user-friendly tools. such as Hyperledger composer and fabric.
Managing Service Operations
The focus of this course is to develop analytical thinking skills that will enable students contemplating careers in services to develop, evaluate and implement strategies for a wide range of organizations in the service sector. Topics include analyzing service processes using queueing models, improving service process with lean concepts, and analyzing customer behavior data and improving quality of service delivery.
Stochastic Models and Their Business Applications
The focus of the course is about mathematical methods applied to economics and financial derivatives products. Probability and stochastic calculus will be studied before introducing the modeling theory for Options. It bridges the gap between the option pricing theory and practice with examples of popular structured products in the financial market. Topics include probability, stochastic calculus, risk-neutral modeling, black-scholes-merton model and applications. After the course, the students will be well prepared to work in financial industry as traders, structurers, sales and risk managers. Course grade will be based upon presence, homework or project and final exam.
Enterprise Process Analysis and Simulation
Marketing Analytics
This course is an introduction to the principles and techniques of operations analytics. Topics covered include process analysis, inventory management, quality management, supply chain management. A set of quantitative and qualitative techniques will be covered to help students analyse and solve the operation problems.
