This course is Layer 1 — “Self” — of the AI leadership journey: the leader’s personal competency with AI. Generative AI produces confident, specific answers in seconds. The hard part is no longer getting an answer — it is telling an insight from a hallucination, and owning what happens next.

That judgment cannot be delegated. Across five hands-on sessions, students direct AI agents (Claude Code) to build real, working business artifacts — a board deck, a private knowledge base, a BI dashboard, a DCF valuation, and a packaged AI agent — and learn the managerial judgment calls the AI cannot make. Every session pairs a real business task with hands-on practice and a debate in which students take a position.

As Mary Callahan Erdoes, head of JPMorgan Asset Management, said, “Coding is not for just tech people, it is for anyone who wants to run a competitive company in the 21st century.” That's truer than ever in the age of AI: large language models and AI coding assistants can write and debug code, but only for people who already understand what "good code" looks like and how to direct, verify, and correct machine-generated output. This pre-course teaches you to code and apply it to basic data analytics, with an explicit eye toward working alongside AI tools rather than being replaced by them. The course is designed for beginners with no or little coding experience who want to build the programming foundation required to succeed in data analytics in an AI-augmented workplace. It covers Python, the language of choice for data scientists and the language most AI coding assistants are trained to support. Students learn core programming concepts, then apply them using libraries such as pandas and numpy to work with real data. The course will heavily emphasize hands-on experience in Python programming. A large proportion of time in class will involve coding exercises.

The course will provide students with a remarkably useful and essential set of business math concepts and methods for understanding and analyzing individuals’ and firms’ economic decisions. We will study several key concepts in the microeconomic foundation of managerial decision-making. Topics covered include (i) demand, supply, and market equilibrium; (ii) optimal pricing strategies under competition; (iii) utility function and risk attitude; (iv) information asymmetry and problems of adverse selection and moral hazard; and (v) game theory and strategic decision-making. As such, this course serves as a “tools” course for making analytical business decisions. The course will be presented through lectures, exercises, and student-led discussions.

Harvard Business Review called “Data Scientist: The Sexiest Job of the 21st Century”. In the past decade, the growing accessibility of large-scale data has paved the way for the field of data science to become in-demand professions. The course will introduce a number of useful data science-related libraries in the Python ecosystem such as pandas, numpy, matplotlib, and seaborn. Students will further advance their programming and analytical skills in Python using Pandas and numpy libraries. Through case studies and hands-on exercises, students will gain a deep understanding of how to solve data analytics problems in real-world situations.

This course will introduce key measures and indicators that managers and investors use to evaluate not just specific projects, but also the state of the corporation. A clear understanding of these measures is essential for analytics professionals as they seek to make the company more competitive and maximize shareholder value. Topics covered will include financial statement and market-based metrics, fundamentals of capital budgeting, project evaluation, and firm value.

Operations Management (OM) concerns the activities that organizations do to produce goods and services for their customers. More succinctly, OM studies how organizations match supply with demand, reduce frictions and delays, and how they get things done better over time. The course is built on a balance of quantitative and qualitative materials, and it discusses OM concepts using contemporary examples from the industry.

This course will cover fundamental statistical concepts and tools that are used to understand business data and make business decisions. For example, we can utilize these statistical concepts and tools to describe distributional characteristics of a product’s past sales data, to predict the future customer demand for the same product, and to test new product ideas and decide on the future product offerings. More specifically, we will study key concepts such as probability distributions, linear regression and hypothesis testing. The course materials will be useful at various levels of decision-making and for different roles (such as managers, strategists, analysts, economists, scientists, etc.) in a wide range of industry settings.

Business forecasting is an essential function for any organization seeking to make data driven decisions. Using machine learning and traditional statistical modelling, businesses can gain insights into future market trends, customer behavior, and optimal resource allocation. This course provides an in-depth overview of classic and modern forecasting techniques with a focus on practical applications. Students will learn to think deeply about conceptual issues in modelling, and be required to implement forecasting and evaluation techniques using statistical software.

As corporate data is growing exponentially, understanding the foundations of how to manage and work with large databases is an essential starting point of business analytics. This course will provide you with a solid understanding of how to use and design databases in real-world situations. You will learn the fundamentals of Database Management Systems (DBMS) and the SQL language for data analysis. Using a robust SQL database system allows you to work with big data and access information quickly and efficiently. SQL is also an excellent adjunct to programming languages used in data science, such as R and Python. The course will also cover a brief introduction to the No-SQL database for working with large sets of distributed data.

This course is for beginners who want to perform data analysis with SQL and build database design skills. More specifically, students will develop the necessary skills in building powerful databases and writing for data cleaning, transformation, and storytelling. The classes will be lecture-based, with lots of hands-on exercises using real data sets you will likely encounter in the real world.

The purpose of this short module is to familiarize you with the strategic marketing decisions companies need to make while building a truly global brand. This module asks you to reflect on how modern firms create a value proposition for their customers, and how they capture the value they have created. Starting with the unique positioning of a firm’s product or service, we will discuss how firms integrate the 4Ps marketing mix elements (product, promotion, place, and price) into a coherent marketing strategy. We will examine why some brands are merely good while others are great, how to align positioning strategy with your tactical decisions, how firms can achieve competitive advantage by creating unique value for their customers, and how firms can achieve profitability by creatively capturing the value they have created.

Accounting is the “language of business” and thus essential for virtually any business and personal transactions. This is why “accounting” is a core course in business school programs. When making management decisions (i.e., planning and implementing business strategies, and evaluating performances, etc.), managers use various kinds of financial (and non-financial) information. In this course, our focus is on how they do (and should do) that in a variety of decision-making contexts. We will also study how managers use accounting information to motivate and evaluate management performance, i.e., for the purpose of control. At the end of the course, we will briefly explain financial statements, produced by firms for external reporting (i.e., financial accounting).

Although based on economic common sense, it is easy to misuse accounting information for managerial decisions, which could have disastrous consequences for you and your company, as we’ll see. Making right decisions will boost your productivity and effectiveness, and give you an advantage over your competitors. Main topics to be covered include cost concepts, cost-volume-profit (CVP) analysis, decision making using relevant cost and revenue (including opportunity costs), cost allocation, decentralization and responsibility accounting, and performance evaluation.

Simply speaking, accounting is much like playing golf or tennis. While it may be enjoyable to watch someone else play the game, there is no way to learn how to play the game unless you do it yourself and actively participate in the entire learning process. This means that you need to do readings before and after class, participate in class discussion, and work on problems and cases to strengthen your understanding. Remember that you have invested a huge amount of financial and non-financial resources in this program. A small amount of your incremental investment will give you a large return.

This course is designed to learn the fundamental concept of AI&ML methods and how to apply AI and ChatGPT to real business problems through hands-on projects. Rather than focusing heavily on mathematical theory, the course emphasizes:

• using ChatGPT as a daily analytical partner
• learning the core concepts of machine learning and LLMs
• building practical AI tools and prototypes
• translating AI results into business decisions and executive communication

The instructor introduces the core concepts briefly, while most class time is spent on project-based practice using ChatGPT + Python + Colab.

Customers’ digital footprints have become a significant asset for marketers seeking to gain a competitive edge. We will concentrate on how to use cutting-edge data analysis tools, how to communicate the findings, and how to develop insights and enhance marketing decision-making throughout the course. The course will heavily emphasize conceptual understanding of the underlying algorithms and logic of the analytic tools. AI tools such as Gemini, ChatGPT and Claude make execution cheaper and easier — therefore understanding fundamentals matters more, not less.

In the domain of marketing, data analytics are applied to understand the basic questions in marketing management: 1) what are the customer’s needs and wants? 2) which groups of customers should the company target? 3) how to evaluate the effectiveness of the marketing mixes? 4) who are the valuable customers whom the company should develop long-term relationship with? The goal of the course is to equip students with essential concepts, modeling techniques and tools in conducting data analysis in marketing. In the course, we will focus on how to apply the state-of-art data analytic tools, how to communicate the results, and how to generate insights and improve marketing decision-making.

The goal of this Data Driven Communication course is to provide you with the skills needed to communicate a specific message in a memorable and actionable manner.

In this course you will:

• Be able to construct powerful narratives through effective data analysis.
• Be able to design support materials such as slides that enhance audiences’ ability to comprehend both the data presented, and the overarching meaning.
• Be able to connect with stakeholders through combination of rational presentation and empathetic persuasion.
• Present significant amounts of data coherently and with confidence.

This course is designed to train students to become more knowledgeable and effective data scientists by mastering Deep Learning techniques for real-world business applications. By learning hands-on implementation of high-level Deep Learning software such as TensorFlow and Keras, students will develop practical skills in building and optimizing deep learning models. This course emphasizes intuition and application over rigorous mathematics, so students are expected to have a basic understanding of Python, Linear Algebra, Calculus, and Probability. Students will explore fundamental models such as Perceptrons, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). After learning these models, students will apply them to business challenges such as product image classification and fraud detection. By the end of this course, students will have a strong understanding of the deep learning toolbox, which will enable them to analyze complex business data and effectively apply these concepts in their professional careers.

This course explores the ethical questions and legal challenges that arise from the rapid advancement of technology and the evolving ways in which individuals’ data is collected, analyzed, used, and sold. With our increased reliance on mobile and internet platforms—including social media, generative AI, search engines, navigation apps, and other tools that enhance our efficiency and well-being—it becomes essential to examine how our personal and private data is being gathered, used and disseminated at an increasingly alarming rate.

From location-tracking apps and dating sites that sell intimate information, to facial recognition technologies used in schools and government-run databases of biometric data, modern technologies pose serious concerns about privacy and autonomy. Laws have historically struggled to keep pace with technological change, and today’s global legal landscape is a patchwork of outdated and inconsistent regulations that often fail to provide adequate protection.

In this course, we will examine how individuals are losing control over their personal data—and whether it might be reclaimed. We will analyze the legal frameworks of Korea, the European Union, and the United States, as these jurisdictions have the most significant impact on companies operating both in Korea and globally.

Topics will include the evolving concept of “consent,” the classification and treatment of sensitive data, jurisdictional gaps in legal protection, the principle of “privacy by design” (PbD), algorithmic bias and discrimination, and the unique legal and ethical issues related to collecting data from minors. Students will participate in group exercises designed to deepen their understanding and raise awareness of the complex ethical and legal challenges associated with data collection and privacy in today’s digital world.

As the complexity of problems facing marketing manager increases, so does the need for information. Information for managerial decision making should be collected, analyzed and interpreted through a systematic approach of marketing research. This course introduces you to the fundamental concepts, methodologies, and applications of marketing research. Both qualitative and quantitative (survey) research methods and analytic approaches will be covered with a focus on marketing decisions such as segmentation (cluster analysis), positioning (factor analysis), product/price decisions (conjoint analysis), and promotion decisions (A/B testing).

Technologies are intricately woven into the fabric of human lives. Consequently, firms must navigate the landscape of emerging and evolving technologies both within and outside their operations. This course will delve into the opportunities and challenges presented by technological innovation. We will explore various sources, types, and patterns of innovation and discuss the formulation and implementation of innovation strategies. The course will be discussion-intensive, with class participation serving as a significant evaluation component. Other components include individual case reports, a team project, and a final exam. This course requires students to integrate the concepts, knowledge, and skills learned from prior strategy courses and critically assess their applicability to the management of technological innovation. While key concepts and frameworks will be delivered through lectures, active involvement in in-class discussions is crucial for the successful completion of this course.

This workshop provides students with a hands-on introduction to the application of artificial intelligence and business analytics in real-world business contexts. Students will explore how AI technologies, data, and analytical methods can be integrated to identify business opportunities, solve complex problems, and support data-driven decision-making. Through practical exercises, case studies, and team-based projects, students will work with business data and AI tools to analyze problems, generate insights, and develop actionable solutions. The workshop emphasizes the practical use of AI and analytics rather than technical theory, enabling students to effectively communicate with data and AI professionals and apply emerging technologies to their own business functions and organizations.

Business intelligence (BI) turns business data into shared understanding and better decisions. Traditionally, this has meant preparing data, defining metrics, and building interactive reports and dashboards. Those capabilities remain essential, but modern BI also includes semantic layers, natural-language interfaces, large language models (LLMs), and AI agents that can query data and support decision workflows.

The course focuses on the foundations of enterprise analytics using Microsoft Power BI: data preparation, relational modelling, measures, visualization design, and dashboard storytelling. The course concludes by, LLM-enabled approaches using low-code tools such as Metabase, potentially connected through an MCP server. This forward-looking session introduces students to conversational interfaces and custom AI workflows, helping them evaluate how these emerging solutions can complement governed dashboards in a business context.

While most of a firm’s marketing activities (such as product design, distribution, and advertising) create value for the customer, pricing is the only marketing activity that creates value for the firm. Pricing is a far more powerful lever to improve firm’s profits than activities such as cost reduction and sales improvement. Practicing managers have found that a 1% improvement in price can typically increase profits by over 11%. Despite the significance of pricing for a firm’s profits, many managers lack the quantitative and strategic skills to set prices, and often make mistakes in their pricing decisions. Such errors can result in pricing that is too cost-oriented, failing to account for consumer response or competitive reaction. Additionally, pricing may be inconsistent with product position or unresponsive to market changes, and new product pricing may encourage cannibalization of an existing product line. Knowledge of pricing would help in avoiding these types of mistakes. Hence, this course is relevant to those pursuing careers in marketing, finance and general management.

Social media not only provides marketers with a means of communicating with their customers, but also a way to better understand their customers. Viewing consumers’ social media activity as the “voice of the consumer,” this course exposes participants to the analytic methods that can be used to convert social media data to marketing insights. Participants will be exposed to both the benefits and limitations of relying on social media data compared to traditional methods of marketing research.

In addition to providing firms with a means to gain insights about market structure and consumers’ perceptions of the brand, information in social media can have immediate impact on firms’ daily activity and performance. Despite this significance, many firms are not equipped with the skills to effectively manage, analyze, and draw insights from the social media data. In a 2018 survey of businesses, it was found that only 29% had effective social media marketing programs. Another recent survey of consumers found that 92% of consumers trust recommendations from other people over brand content, 70% found consumer reviews to be their second most trusted source, 47% read blogs developed by influencers and experts to discover new trends and new ideas, and 35% used blogs to discover new products and services. Also, 20% of women who used social media considered products promoted by bloggers they knew. Today, businesses and consumers use social media to make their purchase decisions.

This course will provide a detailed step-by-step guide to access social media data using Python. You will gain an understanding of how Application Programming Interfaces (API) work and how to use them. Furthermore, this course will also introduce you to scraping the web data, using a simple chrome extension tool. You will also learn how to analyze and visualize the collected data using R. Lastly and perhaps most importantly, this course will discuss business implications of social media through a number of case studies.

In the rapidly evolving digital landscape, Generative AI has emerged as a transformative force for businesses across industries. This course, "Generative AI for Business," is designed to equip professionals with the knowledge and skills to leverage Generative AI technologies to solve complex business problems, enhance creativity, and drive innovation. By focusing on practical applications of Generative AI, including prompt engineering, semantic search, image generation, and Retrieval-Augmented Generation (RAG), Agentic AI, participants will learn how to effectively implement these tools to gain a competitive edge.

This course is ideal for business professionals, entrepreneurs, marketing specialists, product managers, and anyone interested in harnessing the power of Generative AI to solve business challenges. Whether you are looking to improve customer experiences, streamline operations, or foster innovation within your organization, this course will provide you with the tools and insights needed to succeed.

With the rise of massive data and easy access to computational power, companies are under increasing pressure to adopt computer-supported decision-making. However, there is a significant shortage of skilled data analysts capable of transforming data into actionable insights that lead to better business decisions. Industry experts often summarize the situation like this:

DATA COLLECTION ✅ (decent) ⟹ ANALYSIS: inadequate ⟹ ACTION ❌ (not happening)

Many firms struggle to fully leverage their data, with most challenges arising in the analysis phase. Either the analysis does not adequately address key managerial questions or fails to effectively communicate insights into actionable recommendations. This course will help you bridge that gap by teaching you how to develop small yet effective optimization and Monte Carlo models using tools like MS Excel—empowering you to make data-driven decisions with readily accessible software.

The digital transformation of business and the newly dominant role of platforms, big data, and AI have fundamentally transformed the modern competitive environment. In this course, we strive to understand the unique challenges and opportunities this transformation creates for both established and new companies.

After a preliminary analysis of the traditional business model and competitive environment, we will explore how companies currently find themselves under immense pressure to adapt to radically different circumstances. The dominance of network effects, the increasingly unclear boundaries between organizations and their environments, the complex definition of suppliers and buyers that can often coincide, and new opportunities to outsource firm functions and risks through crowdsourcing and open innovation create urgent challenges and unprecedented opportunities.

We will explore these concepts and their implications for modern business through cases of companies that tried, either successfully or unsuccessfully, to engage in digital transformation to fully or partially adapt their business models to a vastly different competitive environment. We will also examine the cases of new ventures that emerged specifically to take advantage of a digitally transformed business environment, often creating entirely new industries and/or business models.

The AI Capstone Project provides students with an opportunity to integrate the knowledge and skills acquired throughout the AI MBA program to address real-world business challenges. Working individually or in teams, students will identify a meaningful business problem, analyze relevant data and business contexts, and design an AI-driven solution that creates measurable business value. Students will apply AI, business analytics, and strategic frameworks to develop practical solutions while considering issues such as organizational readiness, implementation feasibility, and responsible AI adoption. The project culminates in a final presentation in which students communicate their findings, proposed solution, and expected business impact to an audience of business and industry professionals. Through this experience, students will strengthen their ability to translate AI capabilities into actionable business strategies and lead AI-driven innovation within their organizations.

The Applied Business Projects course provides students with an opportunity to apply the knowledge and skills developed throughout the AI MBA program to real-world business challenges. Students will work on practical projects that address problems such as business process improvement, customer experience, data-driven decision-making, and AI-enabled innovation. Through project-based learning, students will define business problems, analyze data and organizational contexts, develop and evaluate potential solutions, and translate their findings into actionable recommendations. Emphasis is placed on bridging business strategy, analytics, and AI to create practical and measurable business value. By completing an applied project, students will strengthen their problem-solving, collaboration, and communication skills while gaining hands-on experience in driving business innovation through data and AI.

This Global Field study is an intensive, immersive experience designed to provide you with an understanding of the relationship between technology, innovation, and business in the context of Taiwan's highly dynamic and competitive environment. Through a curated selection of site visits, including top-tier universities, leading tech companies, government institutions, and cultural heritage sites, students will explore firsthand the strategies and operations that have propelled the country to the forefront of the global technology sector. The program aims to offer insights into sustainable business practices, the semiconductor industry, advanced manufacturing, and digital transformation initiatives that are shaping the future of global business landscapes.