筑波大学 University of Tsukuba 国際経営プロフェッショナル専攻 Graduate School of Business Sciences MBA Program in international Business

Applied Information

Applied Information Area Index

Business Mathematics

The mathematics are needed for the study of economics and business. The objective of business mathematics is to introduce or review some basic mathematical concepts and methods for students to learn quantitative methods in business, which includes topics such as functions and graphs, matrix algebra, probability and statistics, and differentiation. The topics will be taught by different instructors from the corresponding fields.

Business Simulation

The main purpose is to encourage students to find various styles such as information gathering, data analysis to make more effective decisions on management through gaming simulation. The number of participants is limited, since this class will be offered in the Tokyo Satellite (PC room). Therefore the a priori submission (by email) is required in advance. The questionnaire items and more detail information are described in the syllabus. Students who have not earned credit for “business game” of GSSM can register for this class.

Introductory Data Analysis : Invitation to Quantitative Analysis

This course provides basic topics regarding quantitative methods, which include probability, probability distributions and descriptive statistics, sampling and estimation, hypothesis testing and so on. Students will learn how to summarize data and how to make appropriate decisions based on data.

Data Analysis 1: Introduction to Data Analysis

Data analysis is an indispensable tool for empirical analysis and data-oriented decision making in the fields of natural science, humanities, and social sciences. This class introduces basic concepts of descriptive statistical methods, linear regression for prediction and its residual analysis with statistical software R through a series of the group works on financial data analyses of all the listed companies in Japan.

Data Analysis 2: Principle of Quantitative Research

This course covers fundamentals on quantitative analysis, including a design of data collection, data analysis strategy, and summarization of the quantitative results. Some exercises are included to apply the statistical tools, such as design of experiments, regression analysis and so forth.

Data Analysis 3: Data Mining

This class is designed to enhance understanding of key techniques of Data Mining which are applied in various fields such as marketing research, medical information analysis etc. Another aim is to acquaint students with basic mathematical descriptions in order to enhance the understanding of professional articles.

Operations Management 1: Operations Management

Operations management is primarily involved with activities of developing, producing and delivering goods and services. It applies the underlying methodologies of management science to deal with the operations. The focus is on how to combine concepts, models, and methods to help managers develop better systems and make better decisions concerning operations. This course covers five operations management and management science topics, which are PERT/CPM, Linear Programming, Analytic Hierarchy Process, Decision Analysis and Inventory Management Models. The fundamental concepts, models and principles associated with each topic and their applications in operations will be taught by different instructors from the corresponding fields.

Operations Management 2: Decision Analysis

Decision analysis provides powerful tools for dealing with complex decisions that involve multiple objectives and/or uncertainty. In this course, we will learn a useful decision process to identify and overcome the challenges of decision making. We will introduce some fundamental concepts, models and methods for decision analysis in various situations such as decision with multiple objectives, decisions under uncertainty and decisions with different decision makers and different/conflict decision objectives, namely game problems. We will make practices to solve some real-world decision problems through group works.

Operations Management 3: Risk Analysis

Risk analysis is defined as a systematic process to describe risk, i.e. to present an informative risk picture. Risk analysis is incorporated primarily in risk management and risk-based decision making. The objective of this course is to learn the fundamental concepts of risk analysis and a variety of models and methods to deal with risk identification, risk assessment and risk management problems. A risk filtering, ranking and management (RFRM) process will be introduced and applied to solve some practical risk management problems through group works.

Operations Mamangement 4: Project Management

In order to accomplish a project successfully, it is important to carry out systematized management processes, such as requirements definition, planning, executing tasks, and monitoring and control. This course provides the fundamental knowledge of project management. For instance, WBS(Work Breakdown Structure), Scheduling techniques, EVM(Earned Value Management), Cost Estimation and Contract, Risk Management, Quality Assurance and so on.

Operations Management 6: Systems Design Theory

Understanding behaviors of social systems is one of key factors for success on business and our life. Diagraming techniques, for example, Flow chart, ER Diagram (Entity Relationship Diagram), State chart and UML (Unified Modeling Language) are useful to visualize/design our social systems. Additionally, natural languages, for example, Japanese, English, Spanish and other languages are useful when we will design social models. In this class we will learn text analysis, diagraming techniques, and systems design.

Operations Management 7: Principles of Artificial Intelligence and Its Social Implementation for Business Applications

The third boom of artificial intelligence (AI) is still continuing.
The first one occurred from the early 1960's to the beginning of the 1970's.
At that time, their main topics include to develop solvers of puzzles and block worlds, which were seemingly complex, however, well-defined small problems. This boom had suddenly ended with the failure of machine translation systems. The main topics of the second boom include so called “expert system” or knowledge-based systems, which started with the fifth generation computer project in Japan in the 1980's. The second boom had also ended in the beginning of 1990’s, with the economic bubble bursting and the closes of central laboratories at major firms. Compared with these previous two booms, the current AI era is different, because there are so many practical results superior to human intelligent activities such as Go and Shogi, automatic driving. These results are attracting people because of their ease of understanding. Recent machine learning techniques via artificial neural networks have an important roles in the development. However, from the viewpoint of system development, implementation, and management, we believe, there remain the same difficulties as the previous two booms. This might cause the next collapse of the boom. In this lecture, we will discuss methodologies to make use of artificial intelligence technology as system management. Referring to our recent research results as examples, we will explain centric issues on artificial intelligence and its social implementation. Then, we will give our future perspective of advanced information and communication technologies.