Incoming students course catalogue

This course aims at learning advanced analytical decision tools to help manager make sound business choices. We will explore how to model complex decision problems under uncertainty using these techniques and leverage the resulting analysis to make insightful business recommendations. This course is quite intensive and requires students to be very well prepared before each class.

*Students interested in this course will need to prove prior knowledge: Strong Excel skills, linear algebra, calculus at undergraduate level as well as intermediate level of Python

Data and analytics in general and AI in particular are changing the world – and they bring ethical, social, economic, environmental, and legal challenges and opportunities. This course will explore how to use data, analytics and AI responsibly and effectively for the benefit of society. We will investigate and discuss different aspects of the societal impact of analytics and AI, such as human control, biases, abuse, ownership, unemployment, and surveillance. You will also learn about ethical principles for data-driven systems, such as fairness, utility, transparency, understanding, responsibility, and ethics. You will apply your learning to real-world problems or scenarios through class discussions, a debate club, group presentations, and assignments.

In this class, we learn how to combine the predictive power of machine learning with experimental and quasi-experimental approaches to estimate causal effects.

*Students interested in this course will need to prove prior knowledge: have taken Econometrics and Introduction to Machine Learning + Intermediate proficiency in programming with R AND Python 

The exponential growth of data presents an important opportunity but also a challenge for firms. The skills required to manage increasing volumes of data in multiple formats—while ensuring quality, security, and usability for analytics and AI—will be taught in this course. Students become familiar with key database and data platform concepts (e.g., relational model (SQL) vs document model (NoSQL)) and discuss their suitability for different business use cases.

*Students interested in this course will need to prove prior knowledge: Intermediate proficiency in programming with R AND Python 

In today's data-driven world, visual communication is a core competency for any data professional. This 5-session course teaches students how to turn raw data into insightful visualisations that drive understanding and decision-making. Through a combination of design theory and hands-on labs, students will develop practical skills in two leading platforms: Python, for its power and reproducibility, and Tableau for rapid, interactive dashboarding. Upon completion, students will be able to select the appropriate visualisation methods for their data and audience, creating graphics that are not only beautiful but also clear and truthful.

Design Thinking is a popular toolkit to enhance customer centricity and creativity in the early stages of an innovation process. It has been popularized by companies like Ideo, SAP, AirBnB, Deutsche Telekom or General Electric, and ensures that new products and services are desirable and relevant for customers

This course provides a broad view of entrepreneurial finance. It aims to facilitate an understanding of how entrepreneurs raise financing from investors, how investors select startups, and how financing contribute to a startup’s success.

The Global Corporate Strategy course focuses on multi-business firms operating on a global scale. We will re-visit the basics of Corporate Strategy and go further by deepening your understanding of how corporations formulate and implement corporate strategies in a global environment, addressing the specific challenges that arise.

Private markets are neither inherently good nor inherently bad. Extensive research across various disciplines, notably economics, has shed light on both the merits and shortcomings of market systems. On one hand, they have served as the catalyst for the world's most prosperous societies, leveraging private innovation and enterprise. On the other hand, markets have been implicated in a spectrum of societal issues, including exacerbating the market power of firms, income inequality, and environmental degradation.

It's crucial to acknowledge that markets are not isolated entities; they are intricately intertwined with state and social institutions. For instance, government oversight ensures fair trade by regulating weights, and measures, and prohibiting fraud. This course primarily focuses on a common form of market regulation: competition (or antitrust) law and regulation. It acquaints students with the pivotal role competition law and regulation play in shaping capitalism and the innovation patterns it brings forward. The curriculum provides a comprehensive understanding of competition policy and innovation, drawing on insights from economics, societal perspectives, political considerations, and contemporary challenges within data-driven economies.

This comprehensive introduction to artificial intelligence course covers the evolution from foundational concepts to cutting-edge developments in artificial intelligence. It explores core mathematical principles, machine learning models, neural networks, and optimization techniques. The curriculum includes the Neural Network Revolution's impact on image recognition and NLP, generative AI models, and addresses key challenges like bias and deepfakes. The course concludes with practical applications and future developments, preparing students to understand AI's technical foundations and business applications.

*Students interested in this course will need to prove prior knowledge: Intro to Machine Learning, Mathematics (college level), Intermediate proficiency in programming with R AND Python

The Machine Learning Models for Market Research course aims to give Masters students a comprehensive understanding of the role of analytics in marketing, decision-making, and to equip them with the skills to develop effective customer strategies from data insights. This course builds upon the foundational concepts discussed in Introduction to Machine Learning, and delves further into analytical techniques and modelling tools essential for tackling marketing challenges in the contemporary data-rich business landscape.

*Students interested in this course will need to prove prior knowledge: have taken Econometrics and Introduction to Machine Learning + Intermediate proficiency in programming with R AND Python 

Welcome to ‘Managing Organizations’! Many aspects of our lives are lived in or through organizations; organizations are where we are born, where we spend most of our working lives, and where we most likely breathe our last breath. As such, organizations are a defining feature of modern life. These might include employers, schools, universities, hospitals, governments, religious institutions, etc. Since organizations are such a pervasive part of our lives, we should spend some time to understand them!

This course aims to provide a bird’s eye view of the key internal components of organizations (e.g., structure, culture, processes) and how to harness their power. This will help you understand not only how the internal dynamics of organizations may affect you and your career but also how you can influence organizations effectively. We will focus on organizational dynamics that take place within the boundaries of an organization, not on competitive strategy or inter-organizational relations. Throughout this course, we will also examine how traditional organizational concepts (structure, culture, processes) continue to apply in today's environment of hybrid work, digital collaboration tools, and globally distributed teams.

People often define Marketing as advertising or sales – highly visible activities by which organizations try to persuade consumers to buy products and services. However, marketing is more than advertising and sales – even the most skillful marketing cannot make consumers buy things that they don’t want. Rather, marketing involves identifying consumer needs, and then satisfying these needs with the right product, available through the best distribution channels, promoted in ways that motivate purchase as much as possible, and priced at the right level.

These decisions – product, place (distribution), promotion, and price – comprise the “Marketing Mix.” Together with rigorous and complete analysis of the environment, customers, and competitors, they are the key activities of marketing management, and they are crucial ones: failure to find the right combination of the “Marketing Mix” may result in product (or service) failure. The objective of this course is to introduce you to the concepts, analyses, and activities that comprise marketing management and to provide practice in assessing and solving marketing problems.

The course is designed to further develop your critical thinking skills. Ultimately, you should develop improved decision-making skills, including the ability to analyze problems, evaluate alternatives, and make better decisions. If this course is successful, you will feel more prepared to approach new situations, make decisions and develop strategies based on careful analysis. The skills should extend beyond marketing and apply to all areas of business.

Operations Management (OM) concerns the activities that organizations do to produce goods and services for their customers. In essence, these activities are business processes that take source inputs (e.g. raw materials), utilize resources (e.g. labor, capital) to add value and process them into final products or services, and finally deliver these to match customer demand. In this course, we will study core strategies and tools to analyze and improve these business processes. We will also introduce theories and discuss strategies to integrate the processes within the organization and to coordinate them across the value (supply) chain.

Innovation is often a prerequisite for companies to stay abreast of competition, however it is seldom a smooth and frictionless process. In fact, it is mostly a balancing act between competing demands: stability and change, fluidity and structure, autonomy and control. This course addresses selected management challenges and opportunities presented by innovation: How can organizations break free from doing what they know best? How can organizations tap the expertise and resources of external partners? What is the role of leadership & culture when organizing for innovation? Can organizations be truly Agile? We start by mapping the challenges of corporate innovation, discuss how and why innovations may fail and the activities firms can adopt when they seek to innovate. In particular, we focus on theories, tools and techniques that can be used to manage innovation successfully.

The Sustainability Bootcamp at Vali immerses students from the Master in Innovation & Entrepreneurship (MIE) program in the ideation and early-stage development process of creating value-driven, sustainable ventures. This hands-on course is designed to inspire students to ideate and generate impactful solutions by exploring real-world challenges, by researching tech-driven industry trends, and identifying opportunities that the market demands. Through team-building exercises and challenge-based learning, students will form interdisciplinary teams to brainstorm solutions to these pressing challenges, leveraging their unique skills and understanding what is needed to make a team effective to found a company.

Students will dive deeper into defining their solution and target audience using tools like the Value Proposition Canvas, exploring customer pains and gains, and conducting essential market research, including competitor analysis. By the end of the Sustainability Bootcamp, students will have a solid foundation in the process of opportunity identification and problem-solution fit, which they can then use to iterate their ideas and build ventures. Pitching skills will also be part of the course, for students to showcase their solutions in their final presentations.

This class provides an introduction to the principles of data science and machine learning. We work on the skills of collecting, handling, cleaning, manipulating, storing, and analyzing large data sets. We will explore principles of effectively communicating and visualizing data. The course provides an introduction into state-of-the-art techniques to analyze data.

In today's data-driven world, visual communication is a core competency for any data professional. This 5-session course teaches students how to turn raw data into insightful visualisations that drive understanding and decision-making. Through a combination of design theory and hands-on labs, students will develop practical skills in two leading platforms: R (with ggplot2) for its power and reproducibility, and Tableau for rapid, interactive dashboarding. Upon completion, students will be able to select the appropriate visualisation methods for their data and audience, creating graphics that are not only beautiful but also clear and truthful.

*Students interested in this course will need to prove prior knowledge: Intermediate proficiency in programming with R AND Python

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For long-term survival, a company needs to be both sustainable and profitable. Both established companies and start-ups need to consider this balance, and even in a world of security concerns and economic downturns, it remains a key item on the corporate agenda. This course provides a comprehensive understanding of why sustainability matters to companies and how it can be effectively integrated into business strategies and operations. The course aims to foster an understanding of the economic, environmental, and social dimensions of sustainability, enabling you to drive impactful change within various industries. Through case studies, guest talks, discussions, and practical exercises, you will gain the knowledge and skills needed to develop and implement sustainable business practices that lead to both profitability and sustainability.

This course presents a modern, risk-based framework for investment management, emphasizing allocation to diversified sources of return. Students learn portfolio constrution techniques and gain a curated overview of trading and investment strategies used by hedge funds and institutional asset managers. Through real-world examples and practical applications, the course builds the skills needed to operate effectively in today’s financial markets.

The ability to make “intelligent” decisions is critical for both managers and firms. In today’s hypercompetitive business environment, problems are too complex to rely simply on intuition and common sense. Quantitative decision tools developed in management science and statistics allow practitioners to base decisions on data-driven and scientific methods. This course prepares students to understand and analyze business data, use data to build analytical models, and apply these models to make better business decisions, emphasizing applications in operations research. Students also develop skills to infer operational performance and business model aspects from available data.

*Students interested in this course will need to prove prior knowledge: Calculus and linear algebra at undergraduate level, as well as to have taken Advanced Decision Analysis (or equivalent), as well as intermediate level of Python

This course is designed to equip you with the knowledge and skills needed to excel in a dynamic environment, where adaptability, innovation, and responsiveness are essential. We will delve into the principles of agile leadership, including agile methodologies, ambidextrous organizations and the development of a leadership approach that encourages organizational resilience and growth.

This course introduces various alternative investments, i.e. asset classes other than standard equity or fixed income. We focus specifically on hedge funds, private equity and real estate. Four non-standard features broadly define alternative investments. First, they are typically subject to limited regulation and formal disclosure requirements. Second, information hurdles and search costs for investors are high. A third distinctive feature is their illiquidity. Fourth, they have typically low correlations with traditional investments such as stocks and bonds. Overall, these non-standard features make them highly attractive investments given their potential diversification benefits, which explains their rapid growth in recent years.

This course explores the transformative impact of AI and analytics on healthcare, demonstrating how predictive, prescriptive, and causal analytics are shaping patient care and healthcare delivery. Over five sessions, students will engage with real-world applications, from optimizing patient outcomes to designing field experiments that evaluate the impact of innovative interventions. The course concludes with an exploration of future trends in healthcare, highlighting emerging technologies and ethical considerations. Overall, this course offers a practical, real-world overview of analytics in action, emphasizing the implications for patient care and system efficiency.

*Students interested in this course will need to prove prior knowledge of: Operations Management and Statistics/Econometrics background

In this course we aim to delve into the applications of data driven methods, like analytics and artificial intelligence (AI) in tackling climate challenges related to adaptation, mitigation and resilience. Students will acquire both knowledge and practical skills to prepare them for environmental sustainability and climate science.

Through the utilization of analytics and AI this course focuses on addressing weather and climate related issues. The application of data driven techniques will involve analyzing climate data predicting emerging patterns and formulating strategies for adaptation and mitigation. Students will learn how to interpret climate datasets, evaluate risks and propose solutions that bolster resilience against the effects of climate change by leveraging cutting edge technologies such as machine learning, predictive modeling and data visualization. By engaging in real world case studies and projects students will understand how to apply analytics and AI to some of the environmental challenges of our time.

*Students interested in this course will need to prove prior knowledge: Intermediate proficiency in programming with R AND Python 

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*Students interested in this course will need to prove prior knowledge: Intermediate proficiency with R AND Python, Introduction to Machine Learning, Data Visualization.

Whether we appreciate it or not, we are all negotiators.

Sometimes, the occasion is obvious; we know that we are entering a negotiation that will have large ramifications for our future. When negotiating a salary, contract, or other deal, the outcomes of the negotiation will clearly determine important things for us. But other less obvious negotiations pepper our days. Whether it's negotiating over a workload with a colleague, our needs with a service provider, or our joint life with a significant other, these negotiations dictate the shape our lives will take.

Despite negotiation being such a pervasive, high-stakes activity, we tend to know litle about the strategy and psychology that make for successful negotiations. Why are we sometimes successful in coming to a rewarding agreement, yet at other times, we are let with the feeling that we have lost?

In this course, I will teach you about the art and science of negotiation. We will work on understanding the interdependent nature of agreements and the skills and tools you can use to conduct negotiations that achieve the best outcomes for you and others. To enable you to become a beter negotiator, I will show you the theory behind negotiation and develop your understanding of some important processes in different contexts.

By developing a foundational understanding of negotiation through theory and practice, you will become a beter-equipped negotiator, no mater the occasion.

This course aims to build up students’ skills in advanced risk analysis and spreadsheet modelling. It introduces Visual Basic for Applications (VBA) as a tool for developing user-defined functions, user interfaces, and integrated data analytics applications. By the end of the course, students with the guidance of the instructor will develop an Excel add-in that contains the core Monte-Carlo functionality that can be found in commercial products such as @RISK. This add-in, called XLRISK, will be an open-source application published in the Web, that can be freely used by students, researchers, and data analysts. In terms of applications, the course has a financial focus, covering topics such as Real Options, Value at Risk, and stock price simulation.

Social entrepreneurship is about turning purpose into action — using business as a tool to solve some of the world’s most complex problems. This course invites you to think like a changemaker and an entrepreneur simultaneously. Together, we will explore how innovative business models can drive social and environmental progress while staying financially sustainable.

You will learn by doing: working in teams to design and test your own social business idea. Along the way, you will build skills in financial forecasting, impact measurement, and pitching— and you will hear directly from social entrepreneurs in Europe and East Africa who are already putting these ideas into practice.

By the end of the course, you will have developed a concrete social business concept and financial plan, and learned how to pitch it confidently to investors and stakeholders. This course is not just about understanding social entrepreneurship, but also about doing it.

Time-dependent data are central to decision-making in business, finance, economics, and policy. From financial markets and macroeconomic indicators to demand forecasting and operational planning, understanding how data evolve over time is a critical analytical skill.

This 5-session course introduces students to the principles and practice of time series analysis, with a strong emphasis on applied forecasting and interpretation rather than mathematical derivation. Students will learn how to explore, model, and forecast time series data using R, combining classical statistical approaches with modern, data-driven techniques.

Through a combination of conceptual teaching and hands-on workshops, the course equips students with the ability to select appropriate models, evaluate forecast performance, and translate quantitative results into clear, actionable insights for decision-makers.

*Students interested in this course will need to prove prior knowledge: Intermediate proficiency in programming with R AND Python  + Introduction to machine learning as well as Econometrics

The course is designed for students who have limited or no background in financial analysis and valuation.  We will, however, go to an advanced level in some modules.  I assume a basic understanding of financial accounting, finance, and regression analysis. I also expect you to be able to manipulate Excel spreadsheets and we will learn how to collect data from various databases.  The course is beneficial for students planning careers in investment banking, portfolio management, corporate finance, financial consulting and security analysis.  It is also valuable to students who sit “on the other side” and wish to attract investors, where knowledge about financial modeling and valuation techniques is important.

Note that the course is of the “get your hands dirty” variety.  That is, we will focus on the practical implementation of financial modeling and valuations, and we will run real-world trading strategies.

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Where there are people, there are teams. And where there are teams, there is conflict. The good news? You can turn both into opportunities.

Your success and happiness don't just depend on what you know, they hinge on your relationships with people. Whether you're collaborating on a high-stakes project or navigating a tough disagreement, people will shape your trajectory. They'll give you opportunities, champion your promotions, and influence your daily satisfaction. They'll also challenge you, compete with you, and sometimes drive you up the wall. The question is: how well can you work together and resolve the conflicts that inevitably arise?

This workshop will introduce you to the psychology of teamwork and conflict, equipping you with skills that will improve every relationship in your life: at work, at home, with family, clients, colleagues, and friends. Throughout your career, you'll join countless teams and face recurring challenges. If you keep running into the same roadblocks, it might be time to examine how you approach working with others. This course focuses on understanding both others and yourself, because sustainable success over your career requires both.

I look forward to welcoming you to Group Dynamics and Teamwork.

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What kind of future do you want to shape or contribute to? What kind of leader do you aspire to be?

In this interactive course, we will use LEGO® Serious Play® - a powerful tool for visualizing and communicating ideas - to help you develop, articulate, and communicate your leadership vision and mission engagingly and memorably.

A vision provides direction, focus to the leader, and inspires those they lead to follow it. Defining a leadership vision involves creating a clear and compelling picture of the future state that a leader envisions for themselves, their organization, as well as for their team or community. A vision focuses on the “why” people should follow, a mission describes the “what” and “how,” it is a roadmap, a strategic plan to work towards the vision.

Through at-home preparation and guided exercises, students will reflect about:

• The type of work or environment that aligns with their values and goals.

• The activities and roles that bring the most meaning to their professional life.

• Practical strategies to achieve and communicate their vision effectively.

By the end of the course, participants will:

• Build self-awareness through metaphorical and tactile learning.

• Explore personal values and leadership aspirations.

• Develop a clear, personally meaningful leadership vision.

• Create a concrete leadership mission.

• Develop a strategic personal leadership development plan.

The course provides a detailed overview how to support corporates in identifying, assessing, and measuring its positive and negative impacts on society. The course will introduce frameworks to holistically identify the impact across the entire value covering all ESG dimensions. The students will learn how apply those tools in client workshops and how to facilitate those.

In fast-paced and demanding environments, the ability to manage stress and build resilience is essential for sustaining performance and well-being. This course examines how stress affects the body and brain, and how cognitive resilience supports effective performance under pressure. Using evidence-based tools from neuroscience and psychology, students will learn and practice techniques such as breathing for stress-regulation, cognitive reframing, mindfulness and habit-building to improve decision-making and maintain effectiveness in demanding situations.

The course balances theory with hands-on application. Students will take part in discussions, guided self-assessments, and practical exercises to develop personalized strategies for maintaining focus, adaptability, and composure during challenging circumstances, both in their professional and personal lives.

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There are two ways in which (micro) economics is useful to business students. The first is that economics helps you understand how markets work. Wherever you are headed after business school, you will almost surely spend a lot of time thinking about markets: markets for consumer goods, longterm contractual markets for input supplies, markets for stocks and other financial instruments, labor markets, markets for new goods, and so on. Even if you go into the nonprofit or public sector, you will have to think about how your activities affect and are affected by related market activities.

This aspect of economics is called the positive aspect, meaning that it is descriptive or predictive. It describes what outcomes to expect given a set of economic conditions, and how changes in those conditions change the expected outcomes. For a business student, this aspect of economics is the tool that helps you understand your context and environment.

A second aspect of economics addresses what the best way is to achieve a particular goal. In a business context, the goal is usually maximizing profits. Examples of questions economics can help answer are: How much should a firm produce? What prices to set? What is the right way to consider costs when calculating the profitability of different actions? How should a firm respond to competitors’ strategies?

This aspect of economics is called the normative aspect. This is the aspect of economics that advises what one should do, given what positive economics has to say about what is then likely to happen as a result. As such, understanding positive economics is necessary to being able to make the normative decisions that managers, strategists, marketers, financiers, and entrepreneurs face every day.

Both sets of tools are necessary for making good business decisions. The aim of this course is to equip you with both. While the course will cover specific issues, the main emphasis of the course is developing an approach to thinking about economic problems. Like learning to ride a bicycle, learning to think like an economist takes practice. Merely observing how problems are approached in lectures or readings will not enable you to solve similar problems yourself. The only way to become proficient at solving economic problems is to do them yourself. You will have many such opportunities through problem sets, quizzes, simulations, online experiments and in-class exercises and discussions.

If you have prior economics training, there is also plenty for you on the course. Some (foundational) concepts may not be new to you, but many will be. Importantly, our approach differs from standard programs and emphasizes intuition, application, teamwork, and discussion. This allows you to apply, extend and develop your own abilities as well as benefit your classmates with less prior experience, which bolsters your own understanding, builds your network, and fosters better results for everyone.
 

This course aims at understanding some of the theoretical foundations of decision-making and provides the corresponding analytical and psychological frameworks to help make sound choices.

This course introduces basic concepts and techniques of modern finance. The focus will be on how one could apply these concepts to real-world situations, and help investors and corporate managers to make meaningful capital allocation decisions. The takeaways from the course will have important implications for environmental, social and governance (ESG) practices of firms as well as investors’ and managers’ contribution to entrepreneurs, innovative startups and technological progress with impact on people’s lives and well-being.

This course brings together organizational data sources with analytical techniques to answer key issues facing people in organizations today, such as employee selection, well-being, retention, coordination across an organization’s structure, or the measurement of employee feedback and organizational culture.

*Students interested in this course will need to prove prior knowledge: Statistics at undergraduate level, Intermediate proficiency in programming with R and Python.

In today's data-driven world, visual communication is a core competency for any data professional. This 5-session course teaches students how to turn raw data into insightful visualisations that drive understanding and decision-making. Through a combination of design theory and hands-on labs, students will develop practical skills in two leading platforms: R (with ggplot2) for its power and reproducibility, and Tableau for rapid, interactive dashboarding. Upon completion, students will be able to select the appropriate visualisation methods for their data and audience, creating graphics that are not only beautiful but also clear and truthful.

*Students interested in this course will need to prove prior knowledge: Intermediate proficiency in programming with R and Python.

Machine learning (ML) encompasses theories, methods, and practices for extracting patterns from observational data. The prevalence of data (“big data”) has led to a surge in interest in ML to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers’ mindsets and ground, to a large extent, on ML. Familiarity with ML is a core competency of managers in the digital economy.

Covering the foundations of ML, the overarching objective of the course is to enable students to judge the adequacy of ML approaches for business and management use cases. We aim to build a solid understanding of ML algorithms to appreciate their capabilities, potential, and limitations. Beyond examining these aspects through a methodological lens, it is crucial to discuss the business impact of ML and derive implications for management. To that end, the course addresses emerging challenges arising from the increasing deployment of ML/AI in organizations, including fairness and the risks of discrimination associated with autonomous algorithmic decision-making systems.

*Students interested in this course will need to prove prior knowledge: Calculus, Linear Algebra, Statistics, and Probability Theory at undergraduate level, Intermediate proficiency in programming with R and Python.

In order to make the best decisions, data is vital. Having extensive and detailed data available enables sophisticated analyses and allows to make more accurate predictions. Projects in the real world rarely require a single tool or skill and all the required data is rarely in your database. A Data Analyst therefore needs to be able to not only work with internal data but also with data from external sources. They need to be able to access different data storages such as other databases or APIs, import, clean and prepare the data and ultimately make it available to relevant stakeholders. 

Applying our knowledge of Python to data gathering allows us to access data in the whole internet programmatically while using Python to manipulate and store these data.

This course will equip students with techniques to collect data from a variety of sources on the internet and read this data from a variety of formats into a single Python data frame for further analysis. 

*Students interested in this course will need to prove prior knowledge: Intermediate proficiency in programming with R and Python.

This is an introductory course in econometrics. It will acquaint you with a set of statistical techniques that are used and developed by economists to analyse data and are applicable to a wide range of applications in economics and business.

*Students interested in this course will need to prove prior knowledge: Calculus, Linear Algebra, Statistics, and Probability Theory at undergraduate level, Intermediate proficiency in programming with R and Python.

Business decisions are often too complex to be made by intuition alone. We need to communicate the structure of our reasoning, defend it to adversarial challenge and deliver presentations that show we have done a thorough analysis. We also need to understand and make use of various sources of data, organise the inputs of experts and colleagues, and use software to provide analytical support to our reasoning. The overall objective of this course is to equip you with analytical thinking and powerful tools that help you be more effective in these tasks. The goal is to teach you how to perform data analysis to support decision making, build simple but powerful models that test your intuitive reasoning, develop managerial thinking and facilitate the communication of your recommendations.

Specifically, we will cover the most frequently used statistical methods for analysing data and solving decision problems. Moreover, we will use these methods to solve a number of business cases from a variety of contexts. Through a combination of lectures, real-life case studies, computer workshops and discussions you will develop specific skills in the areas of data analysis, regression modelling and business forecasting. The course is multi-disciplinary in nature and adds to the analytical dimension of other areas, including finance, marketing and accounting.

The study of strategy revolves around a fundamental question: why are some firms more successful than others? 

In this course, you will develop the knowledge and skills necessary to understand, evaluate, and craft strategies that will help develop and sustain firms’ competitive advantage.

This course is not designed to turn you into an accountant. Rather, it is designed to provide you with the ability to be a knowledgeable, confident consumer of accounting information provided in the financial statements and related disclosure.
 

Can Entrepreneurship be taught? What are key challenges in the entrepreneurial process and how can entrepreneurs overcome them? How can entrepreneurs use strategic thinking to develop more successful businesses? While these questions are more difficult to answer than one may think, we will build the foundation together.

The course is focused on merging entrepreneurial and strategic thinking together with actual handson practice. During the course you will not only piece together the key blocks of going from idea to business, but you will be able to apply those in real time, with real startups.

This course is intensive and dynamic. It is designed to address the needs of those who are either actively evaluating specific venture opportunities, plan to start entrepreneurial ventures, or who want to pursue careers where the ability to evaluate new ventures is important (e.g., venture capital, consulting, M&A). Also, this course will be of interest to those of you who consider venture opportunities inside existing organizations, as incumbent firms increasingly face startups as competitors or as potential partners.

Technological change is the process of invention, innovation, and diffusion of novel technologies or processes. Individuals and organizations contribute to technological change by developing new and/or continuously improving existing technologies, and they can benefit if they successfully commercialize the results of their development efforts. Technological change is a multi-layered process that involves multiple stakeholders including producers and adopters of new technology but also regulators and governments setting the general context in which technological change can take place.

Mastering technological change is a prerequisite for firms, be it start-ups or established organizations, to differentiate vis-a-vis competitors, penetrate new markets, and achieve higher margins. Technological change, however, often takes the form of a competetive race and, to succeed, firms must run this race with a clear strategy, speed, and precision.

The main goal of this course is to equip you with tools and frameworks that will enable you to understand paterns of technological change to ultimately develop strategies that maximize the chances to benefit from it. In this regard, this course provides some key concepts that can be directly applied by analysts and managers to solve real-world challenges that can arise from continuous technological changes inside and outside firms. These concepts are discussed using examples from firms that are active in various industries.

One fundamental trend is that globalization is less determined by economic and efficiency considerations, but increasingly by political, national security and foreign policy interests. As a result, geopolitics and how to deal with it will be even more at the forefront of business leaders.

This course provides global managers with a deeper understanding of the recent geo-political challenges for international business. Senior managers face a diverse set of circumstances when they engage in cross-border business as opposed to purely domestic business: e.g., enhanced deglobalization forces, cross-national heterogeneity in economic development, and strong interference of political actors and leaders. All this is embedded in multilateral institutions (such as the WTO or the G20), which are increasingly challenged in a multipolar world.

This course we will provide a holistic approach to global geopolitical by combining economic trends with the political and institutional realities, in order to better understand the fundamental forces behind those trends. 

This comprehensive introduction to artificial intelligence course covers the evolution from foundational concepts to cutting-edge developments in artificial intelligence. It explores core principleslike machine learning models, neural networks, and optimization techniques. The curriculum includes the Neural Network evolution's impact on image recognition and NLP, generative AI models and addresses key challenges like bias and deepfakes. The course concludes with practical applications and future developments, preparing students to understand AI's technical foundations and business applications

This class provides an introduction to the principles of data science and machine learning. We work on the skills of collecting, handling, cleaning, manipulating, storing, and analyzing large data sets. We will explore principles of effectively communicating and visualizing data. The course provides an introduction into state-of-the-art techniques to analyze data.

The course provides students with a comprehensive understanding of the key concepts and strategies necessary for successful sales and marketing in the entrepreneurial context. The course covers a wide range of topics and focuses on equipping students with the knowledge and skills required to build and manage effective sales and marketing operations in startup ventures. Learn how to tackle sales and marketing challenges from the perspective of a B2B entrepreneur.

In the rapidly evolving AI revolution, the question is no longer "Should we use AI to build a startup?" but "How do we build a startup that is AI-native from Day 1?" This intensive, 5-session course bridges the gap between strategy and technical execution. Designed for aspiring entrepreneurs and corporate innovators, students will move beyond the hype to identify high-value use cases and build prototypes themselves. Through a mix of strategic theory augmented by guest speakers and hands-on group work, participants will leverage the latest tools to design, prototype, and pitch a defensible AI startup.
The course mirrors the lifecycle of a modern AI startup - from theoretical grounding to technical execution to fundraising. It begins by deconstructing the current AI landscape and identifying high-value use cases that are ripe for AI disruption. Students then transition into strategic design by applying a specialized AI Business Model Canvas to validate their niche and defensive moats. The middle of the course pivots to a "builder" mindset, where teams use state-of-the-art tools to transform their concepts into functional, interactive prototypes. This technical proof-of-concept is then paired with a rigorous financial and narrative framework culminating in a professional venture pitch. The course concludes with a forward-looking analysis of where the market is evolving including the importance of efficiency at scale, ensuring students leave with both a tangible MVP and a long-term strategic compass for the AI-native economy.

This course delves into the intricate world of debt markets and debt instruments - both as a pivotal asset class known as "fixed income", and as an intricately structured network of funding flows underpinning the global financial and economic systems.

This course addresses financial management issues confronting multinational corporations and corporations with extensive international business. Cross-border transactions and multinational operations force managers to consider the effects of exchange rate fluctuations, legal regimes, institutional rigidities and, more generally, country risk. This course provides managers with an analytic look at major decisions undertaken by firms in an international setting. The subjects addressed include currency exposure and hedging, corporate funding, international capital budgeting and international portfolio management. Students thus have the opportunity to translate the foundations of finance from previous courses to this richer cross-border setting.

This course offers a concise yet comprehensive introduction to the world of Investment Banking, with particular focus on Corporate Finance. Investment Banking plays an important role as intermediary between corporations and capital markets, thereby facilitating optimal capital allocation and a well-functioning market for corporate control across the economy. Typical projects include mergers and acquisitions (M&A), equity capital raisings including initial public offerings (IPOs), debt financings, or corporate restructurings. Through a mix of conceptual input, case discussions and an interactive “sandbox exercise”, the course will explore the scope and conceptual foundations of Investment Banking and provide insight into the transaction processes and decision making frameworks that shape modern finance as well as entire industries.

The objective of this course is to provide the ESMT MSc participants with an opportunity to put their managerial knowledge and skills into practice.

Since management is fundamentally a ‘discipline’ devoted to decision-making and action, we have decided to put MSc participants in a situation as close as possible to what managers experience every day. This will be done with a business simulation (sometimes also called business game) in which the participants in teams have to manage a virtual company for five quarters.

The business simulation is called “MARGA” (for Market Game). This simulation allows participants to make decisions in all corporate functions typically found in a real company; furthermore, companies’ results are based on what other companies do thus inducing participants to compete among teams.

Team members make real decisions in the areas of R&D, marketing, production, personnel, controlling, and finance, among others. In this way, they learn how the different areas work together in the context of the company as a whole. The overall goal for the team, in direct competition, is to align its product and service spectrum with market demand. Supported by effective controlling tools, teams are able to manage their companies according to the principles of value-based management and to maximize their company value.

Visual data, such as images and video, constitutes a massive and rapidly growing portion of the digital information generated today. Organizations across various industries, from healthcare and autonomous manufacturing to retail and agriculture, are increasingly relying on Computer Vision to automate processes, enhance quality control, and extract actionable business insights from visual inputs. Familiarity with how machines interpret and process visual data is becoming a crucial asset for data professionals and modern managers.
This course is designed to equip students with a foundational and practical understanding of Computer Vision technologies and their real-world applications. Through a combination of theoretical lectures and hands-on programming exercises, the course will enable students to build, train, and evaluate computer vision models. Beyond the methodological lens, the course emphasizes bridging the gap between technical execution and business value, empowering students to identify, design, and assess viable use cases where Computer Vision can drive innovation and operational efficiency.

*Students interested in this course will need to prove prior knowledge: Econometrics, Intermediate proficiency in Python programming, and Basic linear algebra and probability knowledge

The aim of this course is to provide the theoretical background and foundations of investment theory. Particular attention will be paid to the investment in stocks and bonds. The applicability of the theoretical models and their empirical relevance will be discussed as well as how these are used in academic literature to determine how and when to invest financial markets.

This course aims to equip you with the knowledge, skills, and techniques necessary to design effective and reliable survey studies. It ensures that you understand the steps and complexities involved in creating and evaluating surveys that collect relevant data, provide valuable insights, and achieve the specific objectives of your project. The course adopts an interactive methodology, allowing you to leverage your own experiences and interests while integrating theory and practice in the sessions.

The transition to a more sustainable global economy is reshaping how firms define their purpose, allocate capital, design financial contracts, and communicate with stakeholders. Capital markets are responding:
sustainability-linked financial products have proliferated, regulators in different jurisdictions have introduced mandatory reporting regimes, and asset managers increasingly demand credible, comparable sustainability disclosures. Alongside these developments, a critical infrastructure of third-party ratings, impact measurement methodologies, and watchdog tools has emerged — with significant variation in quality, coverage, and incentive alignment.
This course equips participants with the conceptual frameworks, empirical evidence, and practical analytical tools needed to navigate this landscape. Rather than treating sustainability as a normative exercise, the course approaches it as an information and contracting problem: How do firms translate sustainability commitments into verifiable claims? How do markets price sustainability-related risk and performance? What are the limits of existing measurement and verification regimes, and what are the implications for investors, lenders, regulators, and civil society?

The landscape of data science continues to evolve rapidly, with a growing number of tools. This course is designed to provide students with a comprehensive understanding of a modern tool stack essential for building and integrating machine learning models into business operations. Participants will gain insights into code management tools and learn how to harness large language models as coding assistants. Additionally, students will explore how to effectively present results to business users.

*Students interested in this course will need to prove prior knowledge: Intermediate proficiency in Programming with R AND Python, GitHub, and Data Scraping

Sustainability challenges, from climate change to biodiversity loss to human capital development,  are among the most pressing problems of our time. In this course, students learn how data and evidence can be used to understand these challenges and evaluate potential solutions. Through hands-on work with real data from research-practice partnerships in India, Rwanda, and Peru, students develop practical skills in data acquisition, analysis, and communication while grappling with questions that matter: How does extreme heat affect the livelihoods of smallholder farmers? Can low-cost behavioral interventions improve teacher training at scale? What drives illegal gold mining in the Amazon, and what is its environmental toll? The course combines structured data exercises with an original team-based project, giving students experience with the full arc of applied analytics: from messy data to actionable insight. No prior subject-matter expertise is required; just knowledge of econometrics and a willingness to engage with real-world problems.

*Students interested in this course will need to prove prior knowledge: Econometrics, Intermediate proficiency in programming with R and Python

A 15-hour, local-first master class on practical NLP for business. Participants learn to build RAG systems, sentiment and text classifiers, structured extraction pipelines, and topic models using free tools that run on a standard laptop. The course combines short lectures, notebooks, financial and legal datasets, quizzes, and a capstone project focused on realistic business deliverables.

*Students interested in this course will need to prove prior knowledge: Basic/intermediate level of Python (functions, loops, data structures, basic OOP)m pandas (load/filter/groupby/merge), Jupyter (run cells, restart kernel, read errors), Command Line Interface (cd, ls, run scripts, edit .env), Machine Learning (train/test split, accuracy, precision, recall), GitHub (clone, pull)

This 3-day course will introduce students to data analytics using the python programming language. We will start at the beginning of every analytics pipeline with importing and cleaning data. As we progress, we will focus on essential data science skills such as data manipulation & transformation, summary & inferential statistics, and visualisation techniques.

Students will use NumPy as the primary tool for working with numerical data in Python and pandas, for cleaning, transforming, manipulating and structuring data. For visualisation, we will employ Matplotlib and Seaborn to create insightful charts and plots. More advanced topics include inferential statistics, where we will learn to draw conclusions from sample data. For this we will employ SciPy & Statsmodels allowing us to perform statistical tests, quantify relationships and assess uncertainty.

Throughout the course, participants will gain hands-on experience with real-world datasets, learning best practices for efficient and organized Python programming. By the end, students will have a solid foundation in Python, enabling them to perform data analysis tasks and continue their learning journey in data science.

*Students interested in this course will need to prove prior knowledge: Beginner to intermediate proficiency in Programming with R and Python, Statistics at an undergraduate level.

A strong professional network is critical for success in your job, career development, and achieving your professional goals. Research shows that more than your skills or knowledge, the type of professional network you develop at work drives your performance. But how do you build an effective professional network? While you most likely have an intuitive understanding of what is required to build one, intuition is often wrong and can lead you to develop an inefficient professional network. In this workshop, we will examine the characteristics of your professional network and identify network profiles and their associated performance implications. You will gain insights into the critical components of a successful professional network, understand common misconceptions about how to build a professional network and how to avoid them, and identify your current network profile with its strengths and weaknesses. Participants who complete the survey will receive a personalized report of their professional network, which will help develop a tailored action plan to optimize their network. Moreover, a well-structured network can enhance your resilience and empower you in your career. We will use the Martha Rinaldi case to illustrate issues new recruits may encounter when they join an organization and how to manage them using their professional networks. 

Strong ideas and solid analyses only create impact if they are communicated clearly and are easy to understand, remember, and act upon. Yet, many presentations unintentionally overload their audience — turning meetings into “read-alongs” instead of decision moments.

This course introduces the first part of the Communication Clinic, focusing on the presentation: how to turn complex content into clear, structured, and engaging communication. Participants learn how to reduce complexity, define a strong core message, and design presentations that are easy to follow and hard to forget. The course combines practical methods (e.g., core message, grouping, narrative structure) with hands-on application on real presentation material. 

Where there are people, there are teams. And where there are teams, there is conflict. The good news? You can turn both into opportunities.

Your success and happiness don't just depend on what you know, they hinge on your relationships with people. Whether you're collaborating on a high-stakes project or navigating a tough disagreement, people will shape your trajectory. They'll give you opportunities, champion your promotions, and influence your daily satisfaction. They'll also challenge you, compete with you, and sometimes drive you up the wall. The question is: how well can you work together and resolve the conflicts that inevitably arise?

This workshop will introduce you to the psychology of teamwork and conflict, equipping you with skills that will improve every relationship in your life: at work, at home, with family, clients, colleagues, and friends. Throughout your career, you'll join countless teams and face recurring challenges. If you keep running into the same roadblocks, it might be time to examine how you approach working with others. This course focuses on understanding both others and yourself, because sustainable success over your career requires both.
 

The course aims to raise awareness among Masters business students of the critical role human rights play in sustainable business and responsible leadership. Beyond the Sustainable Development Goals, 'ESG' is becoming more mainstream and legal requirements on companies to undertake mandatory corporate sustainability due diligence are growing. Meanwhile, companies are grappling with the need for a rapid and just transition to net zero against a backdrop of increasingly challenging operating environments as geopolitical tensions spread globally.

The seminar blends an overview of the business case for human rights with practical small group work and input from senior corporate sustainability executives from leading multinational companies such as Unilever, IKEA, ABB, Ørsted and Equinor who are constantly tackling human rights challenges and dilemmas in their operations and supply chains. Many of the course participants are future business leaders who will need a fundamental understanding of what the 'S' in ESG really means, how companies are impacting human rights across their business and value chains, and what steps can be taken to mitigate risks and exercise responsible leadership.

In this course, we’ll explore how data risks - such as poor data quality, weak governance and regulatory expectations - can impact,  be controlled and overseen by different parts of an organization- from front line teams to risk management and audit functions. Participants will also learn core risk management practices, including how to identify, control, measure, and monitor data risks. 

This course is designed to help participants strengthen their presentation skills by focusing on clarity and structure. Framed as a storytelling sprint, it guides participants through a practical process of turning ideas into compelling presentations that resonate with an audience.

The course follows a clear progression: applying simplicity methods to structure content and using memorability techniques to make messages stick. Participants will have opportunities to streamline their preparation process, apply storytelling techniques creatively, and refine their content through rehearsal, feedback, and iteration.

The overall aim is to provide a hands-on environment where key principles of presentation design come to life — through collaboration, iteration, and real-time practice.
 

This course equips students with a proven, consultant-style approach to solving complex business problems. This will award students a tool-set to run practice projects efficiently and tell the story of the solution convincingly. Through a combination of online pre-work, caselets, and interactive discussions, students are exposed to real-world problem-solving scenarios. Group work and in-class presentations provide opportunities to apply frameworks collaboratively, test assumptions, and refine recommendations. Quizzes are used to reinforce core concepts and ensure learning progress throughout the course. By the end, students will be able to approach ambiguous challenges systematically and present their solutions with clarity and confidence.

Meetings can be an amazing tool for strengthening relationships, building alignment within and across teams and unleashing our collective intelligence and potential. That said, they can also be a painful waste of time.

With this short course, we provide answers to key questions that any professional will face; these include: Should we (even) meet? Who should be there? How should I prepare? How should I lead the meeting?, and How should I follow up?

We will link these questions to key theoretical and practical frameworks and tools, which will equip you to better get things done in our modern, social, work environment.

Machine learning and artificial intelligence are now widely applied to all fields in business. Machine learning provides algorithms and software tools for extracting insights from large sets of data. Modern managers need algorithms, but these algorithms need managers, too. The main goals of this class are to provide insights into how machine learning algorithms work and to develop the analytical skills to use them to solve business problems. This will allow a better understanding of how to derive information and value from increasing amounts of data. At the same time, the class aims to get an understanding of the limitations of machine learning approaches.

The main goal of this course is to equip you with analytical skills and tools that will enable you to derive information from increasingly large amounts of customer data, in order to make data-driven marketing decisions. You will learn how to translate several types of common marketing problems into analytical questions, execute the analysis, and interpret the results to produce actionable insights.

The ability to recognize a decision situation and understand its essential features involve potentially complex interactions between decisions over time that may be difficult to grasp intuitively. In these cases, we may benefit from using decision models – simplified representations of these situations that allow us to consider the different possible scenarios (i.e., ask "what if") and learn more about the problem. This course introduces techniques for formally representing and analyzing decision problems. The skills learned in this course are applicable in almost all aspects of business and should be helpful in future courses.

Innovation is often a prerequisite for companies to stay abreast of competition, however it is seldom a smooth and frictionless process. In fact, it is mostly a balancing act between competing demands: stability and change, fluidity and structure, autonomy and control. This course addresses selected management challenges and opportunities presented by innovation: How can organizations break free from doing what they know best? How can organizations tap the expertise and resources of external partners? What is the role of leadership & culture when organizing for innovation? Can organizations be truly agile? We start by mapping the innovation landscape, discuss how and why innovations may fail and the activities firms can adopt when they seek to innovate. In particular, we focus on theories, tools and techniques that can be used to manage innovation successfully.

The course is designed for students who have limited or no background in entrepreneurial finance and valuation. I assume a basic understanding of financial accounting and finance (we will have a refresher module available). I also expect you to be able to manipulate Excel spreadsheets and we will learn how to collect data from various databases. The course is beneficial for students planning careers as entrepreneurs as well as in venture capital and other institutions providing financing to early stage companies. It is also valuable to students interested more broadly in private equity, investment banking, portfolio management, corporate finance, financial consulting and security analysis. Note that the course is of the “get your hands dirty” variety. That is, we will focus on the practical implementation of financial modeling and valuations, with special focus on early stage companies.

Most innovation courses still behave as if organizations can solve hard problems by adding more: more budget, more people, more technology, more consultants, more platforms, more projects, more everything. This course starts from the opposite reality. Most managers do not innovate from abundance. They innovate from constraint. In this course, students work on one existing organization and identify one human-facing 
moment where value is currently lost, blocked, wasted, delayed, or underused. Not a  fictional startup. Not a blank sheet fantasy. Not a transformation roadmap dressed up with  adjectives. The task is to design and defend one zero resource strategic move that creates  new value by recombining what the organization already has. Zero resource does not mean zero effort. It means no hidden dependency on new budgets, new hires, new suppliers, new technology, or major infrastructure. Students must work with  existing means: capabilities, relationships, spaces, routines, channels, knowledge, behaviors, touchpoints, processes, and positions of control. The discipline is simple and difficult: create value without adding resources. The course follows a tight logic chain. We begin with effectuation, not prediction, to identify what the organization already controls. We then move from existing means to hidden tensions, focusing on one visible human experience that can be observed, described, and defended without confidential data. Students lock one concrete problem situation, use Blue Ocean and ERRC logic to reallocate value, compress the result into one strategic lever, and stress test whether the move is operationally credible within six months. 

The point of the course is not to generate lots of ideas. The point is to make better decisions under constraint. By the end, students should be able to see hidden value inside existing systems, make explicit tradeoffs, defend one focused innovation move, and explain why it creates value without falling back on the lazy reflex of asking for more resources. One human moment. One constraint. One strategic lever.

Every business is exposed to environmental and social risks, whether through its supply chain, cost structure, regulatory environment, or market access. These risks are often discussed in terms of compliance or corporate values, but they are, at their core, economics problems. They involve externalities, information failures, missing property rights, fragile contracts, and regulatory incentives; the same forces that shape pricing, competition, and market design. This course uses economics to analyse sustainability challenges. Each session introduces an 
economic concept, applies it to a real business problem through a case study or exercise, and then examines the empirical evidence. Over six sessions, you will build a toolkit of six diagnostic frameworks for identifying, evaluating, and responding to sustainability risks.

The course will provide students with an understanding of: 
• basics of strategy and of going abroad  
• current developments in global B2B markets, 
• strategic options of industrial companies to succeed,  
• challenges of introducing new strategies and 
• approaches to overcome these challenges - based on theory as well as on practice. 
• Students should apply learning to concrete cases from practice and develop concepts for managing challenges of industrial companies in global markets.

This course equips participants with knowledge and intuition to make strategic, evidence based decisions in digital marketing. The course covers: platforms, search, AI-mediated discovery, networks, data, automated ad markets, creators, (digitally mediated) social influence, and customer communities. Participants learn to connect classical marketing logic (4Ps, STP, the funnel, and customer value) with digital execution choices such as SEO/SEA, targeting, content, influencer collaboration, media budgeting, personalized pricing, CRM, and risk control.

Most of us do not wake up thinking, “Today is the day I want to address that lingering tension with my colleague,” or “I can’t wait to have a difficult conversation with my direct report about their poor progress.” And yet, many of the most important moments in organizations — and in life — involve exactly those situations. Many organizational failures emerge not from too much conflict, but from too little: people staying silent, avoiding difficult conversations, withholding disagreement, or failing to challenge decisions until it is too late. 

Disagreement is socially and emotionally costly. We like people who agree with us. We trust them more. Conflict threatens belonging, identity, status, and relationships, which is part of why even intelligent and well-intentioned people often avoid it or handle it poorly. 

This course examines where conflict comes from, why it persists, and how we can navigate disagreement more productively. We will explore why intelligent and well intentioned people often see the same situation completely differently, why trust can be difficult to repair once damaged, and why disagreements so easily become personal. We will also examine how identity, fairness, culture, belonging, and self interest shape conflict in ways we often fail to recognize. The course will also examine how, when managed well, disagreement can improve decision-making, surface hidden problems, and deepen understanding. Whether you are managing teams, navigating cultural differences, giving difficult feedback, resolving tensions between colleagues, or trying to make good decisions under pressure, the ability to manage conflict constructively is a critical management and leadership skill. 

The course is highly interactive and experiential. Through simulations, role plays, case discussions, exercises, and structured reflection, you will experience the dynamics discussed in class firsthand, including misunderstanding, mistrust, coalition formation, fairness disputes, mediation challenges, and cooperation breakdowns.

In today’s volatile markets, your effectiveness as a leader demands more than speed and flexibility. Although these qualities are essential, their true value rests on two stabilizing pillars: the informal status you hold and the formal structure you craft. Consider the demands faced by the Team Principal of a Formula One team. Beyond helping drivers, engineers, and pit crew maximize speed and adaptability, this leader must manage status dynamics—both within the team and across the sport—to attract top talent and maintain cohesion. At the same time, the Team Principal must design a structure that allows the team to innovate and execute seamlessly under pressure. These efforts unfold not only in the competitive arena of the racetrack but also across the uncertain, politically charged world of F1, where decisions often carry high-stakes implications. In this course, we will explore leadership as the art of empowering people to innovate and implement—together, fast, and flexibly—in the face of ambiguity. Grounded in research on network-based status and organizational structure, our learning journey will equip you to lead more effectively in today’s demanding business environment. 

Artificial Intelligence is no longer a future topic: it is already reshaping how companies serve customers, run operations, and compete. But there's a wide gap between AI hype and what actually works in practice. This course aims to close that gap. Over three intensive days, you will explore how leading organizations create real value with AI: the wins, the failures, and the patterns that separate the two. You'll go beyond use cases into the frontier of Agentic AI, the next-generation paradigm where AI systems act as orchestrated teammates, not isolated tools. In addition, you'll design and build your own agentic solution in a team project that runs across all three days. 

This is a highly applied course. Expect real implementation examples, hands-on prototyping, guest perspectives from practitioners on the frontier, and a final pitch where you present what you've built. 

Experience the intersection of technology and finance in this course on deep-tech venture capital. Students will explore the nuances of evaluating and investing in innovative startups that are shaping the future. Through interactive case studies, expert panels, and hands-on deal diligence exercises, you'll gain invaluable insights into the venture capital landscape. Collaborate with peers to analyse real-world investment opportunities and present your findings to an investment committee. This course not only equips you with the skills to assess promising ventures but also immerses you in the strategic decision-making processes of successful investors.

Systems thinking focuses on understanding how complex systems consistently generate the outcomes we observe in organisations and society. Rather than looking at isolated events or symptoms, systems thinking directs attention to patterns over time, underlying structures, feedback loops, and the mental models that shape behaviour. This perspective helps decision-makers to better assess the potential impacts of change and to design interventions that are robust in dynamic and uncertain environments. 

This course provides an introductory, practice-oriented exploration of systems thinking and systems mapping. Participants are introduced to core concepts such as system dynamics, feedback loops and leverage points, and they learn how to visualise complex situations using both analogue and digital mapping tools. Building on a human-centred approach, the course uses a real-life business challenge as the basis for analysis, systems mapping and reflection. The course is closely aligned with the programme’s emphasis on rigorous analytical problem-solving (LG2). Students learn to decompose complex, sometimes ambiguous situations into understandable system structures, to synthesise different perspectives into shared system maps, and to explore how interventions might create change in a system over time. The course also supports participants in connecting analytical insight with ethical and sustainability considerations by highlighting unintended consequences and system-wide 
impacts of decisions. 
Across five modules (12 sessions), participants progressively move from understanding basic systems thinking concepts to applying methods such as systems mapping, causal loop diagrams and system dynamics to their own organisational context. Between sessions, teams work on their own system maps and conduct qualitative stakeholder interviews to enrich their understanding of the system they analyse. The course culminates in an individual or tandem assignment in which students synthesise what they have learnt by applying the methods and process to a self-selected challenge and creating a digital system map and accompanying analysis.

German Course. Students has to have min. B1 Level. 

*Core-Leadership Course: Only for those requiring an odd number of ECTS.

The course will focus on two related phenomena, strategic change and the role of organizational culture for strategic management of organizations. In our discussions, we will thus strongly focus on what has been called the “organizational software” of organizations, their people and culture. We will analyze several cases where organizations have formulated strategic plans and either failed or succeeded. This will allow us to develop a mature understanding of both organizational members’ motivations to support, challenge, or block strategic change and the nature and role of emerged organizationally shared norms and behaviors (what we often call “culture”). Expect no silver bullets! There are no simple answers to bridge the chasm between strategy formulation and strategy implementation. But there is much to be gained from a better understanding and anticipation of challenges associated with strategy execution.

In this optional, for-credit, elective module, all participants in the Global Online MBA will have the opportunity to meet at ESMT’s campus in Berlin. Participants will experience highly interactive sessions focused on key developments in the German and Global Economy, with a particular focus on Industry 4.0, Germany’s Hidden Champions, and the Berlin startup scene. The week will comprise a mixture of academic sessions, company visits, team-building events, and a social program to help participants develop an understanding on the Germany Economy with key take-aways for their own businesses.

Mastering technological change is a prerequisite for firms, be it start-ups or established organizations, to differentiate vis-a-vis competitors, to penetrate new markets and achieve higher margins. Technological change, however, often takes the form of a competitive race and, to succeed, firms must run this race with a clear strategy, speed and precision.

The main goal of this course is to equip you with tools and frameworks that will enable you to understand patterns of technological change in order to ultimately develop strategies that maximize the chances to benefit from it. In this regard, this course provides some key concepts that can be directly applied by analysts and managers to solve real world challenges that can arise from continuous technological changes inside and outside firms. These concepts are discussed using examples from firms that are active in various industries.

Leadership is one of the most common and valued qualities referenced in organizations. Companies seek effective leaders, invest heavily in leadership development, and recognize that leadership is essential to organizational functioning and success. As a result, there is no shortage of advice about how to lead, what traits effective leaders possess, what skills they should develop, and what styles they should adopt.

But before we can answer those questions, we first need to ask something more fundamental: What function do leaders actually serve? Because if we do not understand the function of leadership, how can we know whether someone is leading effectively or poorly, or how we ourselves can lead effectively?

This course answers these foundational questions. The central idea of the course is that effective leadership is about aligning people around a direction or objective. Leaders are effective to the extent that they are able to get others to align their actions around that direction. While that core function remains relatively stable, the way leaders create alignment varies considerably across situations. Sometimes leaders create alignment through meaning, identity, and belonging. Sometimes through authority, urgency, and constraint. Sometimes through communication, trust, perspective getting, and deep listening. Leaders may all be trying to create alignment, but the mechanisms through
which alignment occurs differ dramatically depending on the situation.

This helps explain why leadership advice often feels contradictory. Effective leaders are described as both participatory and decisive, humble and commanding, visionary and pragmatic. This course argues that these are not actually contradictions. Different situations create different alignment problems, and different alignment problems require different leadership mechanisms. 

Throughout the course, you will learn how different alignment mechanisms work, why they work, when they are likely to succeed, and where they create risks or fail. Most importantly, you will develop a diagnostic lens for leadership. Rather than simply asking, “What kind of leader should I be?” you will learn to ask, “What kind of alignment problem am I facing?” That shift helps leaders recognize when a situation requires decision-making, certainty and authority, when it requires identity and vision, and when it requires participation, trust-building, and deep listening.

The goal of the course is not simply to teach leadership traits or styles, but to give you a framework for understanding why people align, why they resist or defect, and how leaders become effective under different conditions so that you can more effectively evaluate leadership and become a more effective leader yourself.

*Only for students with MA Track or no Track

We start by defining innovation and mapping the innovation landscape and will then move to the internal and external activities firms can adopt when they seek to innovate. We will then focus on the role of users in the innovation process, on how to open the innovation process and use new forms of innovation. In particular, we focus on theories, tools and techniques that can be used to manage innovation successfully.

Throughout the course you will receive guidance on how to assess possibilities for innovation within your company and how to bring them to life through a structured plan.