Business Administration

The course analyses accounting practices and processes from the point of view of investors. The course examines revenue recognition, tangible and intangible assets, the reporting of financial instruments, off-balance-sheet accounting, stock-based compensation, as well as, issues related to the differential approaches to measurement including historical cost and fair values. However, the exact composition of the topics may vary from year to year driven by the latest developments in financial reporting, standard-setting and related debates. The course enhances students’ understanding of contemporary issues in financial accounting. Throughout the course, taken-for-granted “wisdoms” are evaluated and challenged. 

This course focuses on the food industry and studies the success and failure cases of marketing and information management in fields such as agriculture, food service, bio industry, and distribution industry, and discusses how to apply them for the development of our food industry. Through case analysis, students will acquire various practical knowledge on how business activities in these industries are developed from the perspective of marketing and information management, and how to solve problems using methods and frameworks. Students who successfully complete this course will have basic skills as management consultants in the field of food and bio business. 

This course covers financial engineering theories including fixed-income securities, interest rate risks, modern portfolio theory, capital asset pricing model, and derivatives. Students explore and build hands-on experience for application of Al techniques such as dimension reduction, supervised/unsupervised learning, natural language processing, and deep reinforcement learning. 

This course covers programming using data programming language on an introductory yet rigorous basis for highly motivated students with little or no prior experience in programming. The course focuses on collecting and analyzing data, as well as the grammar of the data programming language and provides an in-depth look at big data analysis.  

Modern scientific, engineering, and business applications are increasingly dependent on data, yet traditional data analysis technologies were not designed for the complexity of big data. Big data analysis has emerged as a new, exciting, and fast-paced discipline that explores novel statistical and implementation challenges that emerge in collecting, processing, storing, and extracting knowledge from big data.  

Students learn how to collect, process, and analyze large amounts of data by combining data analysis technology and artificial intelligence technology.  Students will use Python as a powerful tool for data management and analysis and analyze structured and unstructured data using LLMs for natural language processing, text analysis, and graph-based multi-agent systems.  In addition, data management and system design techniques using RAG (Retrieval-Augmented Page 1 of 9 Generation) and APIs that support prompt engineering techniques and AI-based decision-making will also be covered. 

This is a useful course for students who want to combine and utilize data analysis and artificial intelligence technology in various fields such as information technology (IT), business analytics, marketing, and strategic planning. 

Prerequisites:  Basic knowledge of the system configuration of the operating system (Windows, macOS, etc.) (e.g., setting environment variables, etc.).  Basic knowledge of the Python programming language 

The course teaches quantitative techniques that guide evidence-based managerial decision-making. Students examine whether the predictions of managerial, social, or economic theory are supported by empirical evidence. Particular emphasis is on (a) the many ways in which evidence is abused in the academic or managerial debate, and (b) the causality in the relationship between variables. The approach is both formal, as the course makes extensive use of econometric theorems and techniques, and solidly grounded in intuition, as it provides numerous examples of tests of real-life relations. Many of these examples are illustrated using the STATA software package, and students learn the basics of data manipulation and regression techniques.

The course covers analyzing market demand; factors affecting firms; cost, profit analysis, pricing, competition in various kinds of market structure, strategic behavior, firm growth (mergers and acquisitions); the impact of governments on company policies; interpreting economic data; and the macroeconomic environment.  In analyzing all these topics, the course relies heavily on, and where practical, current examples and case studies, rather than mathematical modelling. 

In this course students learn how to evaluate a brand strategy and how to use defined models and analytical tools to improve upon it. It covers the complete process, from consumer research, competitor analysis and positioning, to bringing the brand to life through design and activations. The course is based on the latest academic insights and infused with examples from our daily lives. It helps you prepare for a future as a marketeer, brand strategist, or entrepreneur.

This course covers leading-edge concepts and tools related to strategic and financial analysis. Students develop experience in discovering, diagnosing, and solving business- and corporate-level problems such as formulating business and corporate strategies. Topics include Strategic thinking and analysis, Corporate vision, Environmental analysis, Competitive strategy, and Corporate strategy. 

This course focuses on accounting data used by managers to plan and control the operations of an organization. Students examine the design methodologies of management accounting systems that enhance the quality of management decision-making related to each function in the corporate value chain, namely research & development, design, manufacturing, marketing, distribution and customer service. Topics include cost structure analysis, various cost concepts, design methods of various costing systems, strategic decision makings using cost information, and performance measurement systems. This course provides students with contemporary management accounting techniques including ABC, Target Costing, Quality Costing, Lifecycle Costing, Balanced Scorecard, etc.  

The course covers methods for data mining and business analytics and their usage in making strategic business decisions. It concentrates on the modelling aspects of data mining and provides tools for better understanding key methods of data exploration, visualization, classification, prediction, and clustering. The course starts with data visualization and getting to know features hidden in the data. Traditional regression models and hypothesis testing are practiced using real data. This introduction to traditional approaches then leads to the discussion of more advanced methods such as, discriminant analysis, classification and clustering methods, which are useful in finding patterns hidden in the data. The course deals with various types of data such as categorical data, time series, text data, and network data, among others. The fundamentals of building suitable models are discussed. Illustrations are carried out using the statistical package R.