Incoming students course catalogue

This course aims at learning advanced analytical decision tools to help managers 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.

Successful management requires the ability to recognize a decision situation, understand its essential features, and make a choice. However, many of these situations - particularly those involving uncertainty and/or complex interactions - may be too difficult to grasp intuitively, and the stakes may be too high to learn by experience. In these cases, we may benefit from using decision models - simplified representations of these situations that allow you to consider the different possible scenarios (i.e., ask "what if") and learn more about the problem. This course explores advanced tools based on decision trees and provides an introduction to the art of optimization modelling.

*Students interested in this course will need to prove prior knowledge: Strong Excel skills, linear algebra, calculus at undergraduate level.

Data and analytics are changing the world – and they bring ethical, social, economic, environmental, and legal challenges and opportunities. This course will explore how to use data 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 projects. This course is multidisciplinary and cross-cultural, and it uses various learning formats and methods.

In this class, we learn how to combine predictive machine learning methods with econometric approaches that aim to establish causality (see “Econometrics”). We start with reviewing the principles of Machine Learning. We then examine what distinguishes pure prediction problems from causal evaluation. The fundamental evaluation problem that makes causal analysis difficult is that we observe every unit of observation only in one state, either as treated or as non-treated. In Econometrics, you came across a range of methods to deal with this problem: randomized controlled trials, instrumental variables, difference-in-differences, and others. Common to these approaches is the goal to find an appropriate control group to define the counterfactual (“how would the treated look if they were not treated?”). Since we cannot observe this counterfactual, we have to predict it from the data at hand. This is where machine learning comes into play. Machine learning methods are useful to make accurate predictions based on observed data. We will get to know various methods to combine the two worlds of causal analysis and machine learning that are at the forefront of modern data science.

*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.

In this course, we will discuss models that allow you to generate a better understanding of the customer decision-making process. In particular, we will model consumer behaviour based on stated (e.g., gathered through experiments) and revealed preference (e.g., market) data. The models we discuss can be used to make more accurate predictions on consumer decisions. In addition, they enable the evaluation of how sensitive customers are to price changes and perceive advertising offers, product messages, or brand strategies and can assist marketers in identifying product features that have the potential to increase demand for the products. This course extends beyond the fundamental principles covered in the in Introduction to Machine Learning and Advanced Marketing Modeling courses. It explores advanced analytical techniques and modeling tools, vital for addressing complex issues in the modern, data-centric business environment. By the end of the course, students will be equipped with the knowledge and skills needed to decipher and predict consumer behaviour, enabling businesses to make data-driven decisions and gain a competitive edge in the market. For modeling purposes, we will use R software. Knowledge of statistical data analyses is necessary.

*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 necessary skills to manage an ever-increasing amount of data in various formats will be taught in this course. Students will become familiar with different database concepts (e.g., relational-mode (SQL) versus document-model (NoSQL)) and discuss their suitability for different business use cases. The computer exercises will enable students to apply their knowledge in both traditional server-based but also modern server-less database technologies.

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

Students will learn to transform raw datasets into compelling visual narratives. The Python component (using pandas, matplotlib, and seaborn) will begin with a practical introduction to the Python data-analysis workflow — covering how to import, clean, and wrangle data effectively using pandas before progressing to visualisation. Building on this foundation, students will then learn to create reproducible, programmatic visualisations that reveal patterns and insights in data. The Tableau component complements this by focusing on rapid, interactive dashboarding and storytelling, enabling students to communicate their analytical findings clearly to business audiences. By the end, students will be able to design, build, and present data-driven insights using both code and business-ready visual tools.

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, or GE, and ensures that new products and services are desirable and relevant for customers. This course will focus on this practical application of Design Thinking. Students will acquaint themselves with the fundamentals of the methodology, within the framework of effective ideation. They will learn by doing and repeating, getting real hands-on experience with how to apply the techniques to real product concept and ideation scenarios. Students will leave this course with a new framework for approaching problems and identifying the best course of action, with techniques that they can integrate into their everyday workflow and future teamwork.

This course provides a broad view of entrepreneurial finance. It aims to facilitate an understanding of how investors select startups, how entrepreneurs attract financing from investors and how financing contribute to a startup’s success. This course targets students who are interested in gaining a broader view of the financing landscape for entrepreneurial ventures. It further aims to prepare students who plan to get involved with entrepreneurial ventures in their careers -- as founders, advisors or investors.

The Global Corporate Strategy course focuses on multi-business firms operating on a global scale. It builds upon the knowledge acquired in Fundamentals of Strategic Management, where you learned the basics of Business and Corporate Strategy. This course goes further by deepening your understand-ing of how corporations formulate and implement corporate strategies in a global environment, ad-dressing the specific challenges that arise.

Private markets are not inherently good or bad. Extensive research across various disciplines, notably economics, has illuminated 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 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 encompasses a comprehensive understanding of competition policy and innovation, accompanied by 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.

This course dives into artificial intelligence spanning the foundational concepts to the current state of the art. We will explore AI's evolution from rule-based systems to today’s data-driven machine learning models, rooted in core mathematical principles like statistics and linear algebra. We will discuss AI’s transformative phases, from supervised learning and neural networks to reinforcement and deep learning. We will introduce the essential optimization techniques that drive model efficiency and stability.

*Students interested in this course will need to prove prior knowledge: Calculus and linear algebra at undergraduate level.

TBA

*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.

The role of marketing is gaining more importance in a rapidly changing business environment. The contemporary view of this role is to make sure that every aspect of the business is focused on delivering superior value to customers. Marketing management covers the science and art of how to meet customer needs profitably. We will start by discussing how to set a marketing strategy that achieves a long-term competitive advantage. Based on such a strategy, you will gain insight into key marketing decisions regarding the product and services, pricing, sales, and communication. Furthermore, you will collect hands-on experience by designing a marketing plan for a real company that combines all major decisions into one powerful guideline.

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. We focus on theories, tools and techniques that can be used to manage innovation successfully.

Agility is more than being fast and adaptable. Your speed and adaptability matter, but their realized value depends on your status, your power and how you can persuade others in an organizational context. Agile leadership, for us, requires defending your status and shaping that context. Consider, for instance, the demands facing a top collegiate basketball coach. In addition to helping her players become as fast and flexible as possible, she must ensure that power, status, and politics—within and beyond the team—don’t hinder their ability to recruit or destroy their esprit de corps. In addition, the coach must organize her players so they can innovate and implement—together, as a team. And she must achieve this in competitive arenas and political domains that are fraught with ambiguity. We’ll thus define agile leadership as empowering people to innovate and implement—together, fast, and flexibly—in the face of ambiguity. Guided by that definition, and grounded in social-science research, our learning journey will equip you to lead in today’s demanding business environment.

This course provides global managers with a clear conceptual understanding of the environment, in which international business operates. Senior managers face a diverse set of circumstances when they engage in cross-border business (mainly trade and foreign direct investment) as opposed to purely domestic business: e.g., enhanced globalization (& de-globalization) forces and cross-national heterogeneity in economic development, institutions and cultures. International institutions and agreements governing trade and economic order constitute another important layer of the global economy. Accordingly, we will attempt to use the perspective of economics (applied economics to be specific) as a tool to analyze these issues, make sense of the fundamental forces behind their dynamics, and understand their managerial implications.

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.

This course is built around the frameworks of factor investing and arbitrage. In factor investing, students will examine strategies such as momentum, value, and carry across multiple asset classes, focusing on their practical applications and theoretical underpinnings. The arbitrage component delves into strategies like fixed income arbitrage, merger arbitrage, and convertible bond arbitrage. Additionally, the course addresses critical topics such as portfolio optimization, risk management, and trade execution. Bridging academic research with real-world practice, the course equips students with the essential skills to develop, implement, and critically evaluate advanced trading and investment strategies.

The course covers fundamental concepts and methodologies for modeling and solving optimization problems found in prescriptive analytics. Students will gain expertise in formulating linear and mixed-integer linear programming models, implementing algorithms, and interpreting solutions using optimization software tools. These techniques enable students to tackle practical business challenges, such as optimizing resource allocation, production planning, supply chain management, workforce scheduling, portfolio optimization, facility location, and transportation problems. The course emphasizes practical applications through real-world examples and case studies, solidifying an understanding of optimization models across diverse business domains.

*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).

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, change management, and the development of a leadership approach that encourages organizational resilience and growth.

This course provides an introduction to 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. Alternative investments are broadly defined by four non-standard features. 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. And 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. 

The first part of the course focuses on hedge funds, their investment techniques, business models, the measurement of their performance, their risk-return characteristics and their impact on portfolio construction, and finally the types of hedge fund investors and their fund selection process. The course will cover the essential facts about this industry, combining theory, findings from the academic literature and empirical exercises, using actual hedge fund data to calculate alphas, measure risk and form portfolios from an investor’s perspective.

The second part of this course will first introduce private equity as an alternative asset class. What are the different models to invest in the private equity industry? What are the roles of limited partners and general partners? Next, it will focus on how a typical private equity investment - a buy-out - is structured. We will hereby develop a full buy-out model, focusing on the debt as well as on the equity side. In a first buy-out case, the focus is on the analysis of a proposed deal: is this deal expected to yield sufficient return for investors? In a second case, students will be asked to negotiate their own buy-out deal, including the price to be paid, the amount and type of debt to be raised, and the split of the equity between investors and management.

The third part of this course covers a wide range of investment topics relating to real estate and mortgage assets. The course is applied in nature in that the actual practices of real estate professionals are taught and discussed. Some of the topics to be covered are: real estate in the mixed-asset portfolio; real estate performance measurement, real estate equity securitization, and the surge and fall in mortgage-backed securities. The primary objectives are to link the real estate markets to the financial markets, giving full attention to the theoretical real estate investment issues involved.

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 Operations Management and Statistics/Econometrics background. 

The Data Visualization course covers the fundamentals of data visualization and exploratory data analysis. It equips students with the skills to effectively communicate data-driven findings, motivate analyses, and identify potential flaws, using powerful visual representations to reveal valuable insights and advance their careers. Use R + ggplot2, Tableau, and the principles of graphic design to create beautiful and truthful visualizations of data. Data rarely speaks for itself. On their own, raw data are difficult to understand, and in the absence of beauty and order, it is impossible to understand the truth that the data shows. In this class, you’ll learn how to use industry-standard graphic and data design techniques to create beautiful, understandable visualizations and uncover truth in data.

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

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. 

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

During this course, you will learn to use the cloud platform ecosystem to deploy machine learning models in production. You will get an introduction to the technical architecture of a machine learning pipeline, both for batch and streaming use cases. You will discover how to package the trained models and deploy them using Docker, execute jobs on a cloud platform, orchestrate the jobs to deliver model predictions on a regular schedule and learn about model serving through API endpoints. Finally, you will also touch upon observability to monitor the deployment of the models.

*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 in our lives. 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 little 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 left 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 prepare and conduct negotiations that achieve the best outcomes for you and others. To enable you to become a better negotiator, I will show you the theory behind negotiation and develop your understanding of the relevant processes in different contexts.

The course Open and Distributed Modes of Innovation equips the students with frameworks and tools to understand innovation ecosystems, engage with stakeholders, and adopt an effective open innovation approach. Participants will learn to leverage hackathons, accelerators, and more, fostering a culture of innovation to retain market share.

This course aims to build up student 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.

Entrepreneurs are problem solvers. In the 21st century, the world is facing severe social and environmental challenges that we urgently need to address to ensure long-term peace and prosperity. Social Entrepreneurship is a unique opportunity to build businesses that matter by combining purpose and profit to address development challenges.

The course is designed for students passionate about using their business skills to address complex challenges. We will work on developing concrete social business ideas based on the Sustainable Development Goals (SDGs), focusing on various aspects of launching a social business idea such as financial forecasting, impact measurement, and fundraising strategies. Through real-life case studies and direct interactions with social entrepreneurs from Europe and East Africa, we will gather valuable insights into best practices and experiences on how to set up successful social enterprises. Ultimately, the course will empower you to make a positive difference in the world by equipping you with the necessary tools to pitch and pursue your social business idea.

This course introduces the fundamentals of analysing and forecasting time-dependent data, combining statistical foundations with modern approaches such as machine learning and sentiment analysis. Students will gain hands-on experience in data acquisition, visualization, and exploratory analysis before applying methods to uncover trends, seasonality, and other dynamic patterns. Practical forecasting techniques will be implemented in R, with optional support in Python for students interested in extending their skills to modern machine learning and text-based applications.

*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 focuses on the valuation of companies (equity securities as well as entire firms). The tools and techniques include preparation of a full financial model, estimation and forecasting of free cash flows and other valuation attributes, application of valuation models (such as free cash flow, residual income and EVA), and understanding market-multiples valuation approaches (such as price-earnings ratios, EBITDA multiples, etc.). We use problems and cases developed from and around actual financial statements in various settings, such as regular “going concern” valuations, mergers and acquisitions (M&A), initial public offerings (IPOs), private equity settings, etc.

We also show how to practically incorporate various finance theories and concepts into the valuations, emphasizing that valuation is often context-specific in practice. In addition to viewing equity valuation from a fundamentals perspective, we will cover selected trading strategies, including technical strategies and accounting- and market-based trading rules.

The material is designed for students who have little or no professional background in analysis and valuation. 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, 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 place focus on the practical implementation of financial modeling and valuations, and we will run real-world trading strategies.

This workshop will introduce you to the psychology of conflict and equip you with key conflict resolution skills. Learn how to differentiate between constructive and destructive conflict, master the art of listening without necessarily agreeing, and understand how to adapt the other's perspective without passing judgement. The skills you are introduced to in this workshop are invaluable life tools that will aid you in pivotal moments throughout your career. The art and science of conflict resolution can improve all your relationships, work well in teams, and excel in your internships and in the early years of your career.

Certainly, you have all heard about agile, companies big and small like Amazon and Google are using agile to manage work. You may be forgiven if you wonder why agile is used more and more for managing work and projects. If you still think Agile is good for managing software projects only, you are making a big mistake. Not only all projects can and should use Agile, but organizations as a whole should also become Agile too. The main objective of this course is to get familiarized with fundamentals of Agile, Agile, Scrum & Kanban, as well as getting a hands-on experience of Agile practices via gamification.

This course is a complete introduction to cybersecurity for non-specialists. This course would be aimed at senior students who are not necessarily technical specialists and who do not have cybersecurity experience but are looking for a 'management-oriented' introduction to cybersecurity.

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.

In the seminar Impact Consulting, students will learn about assessing social impact and driving innovation. The course will cover the development of impact assessment approaches for leading consumer goods companies and international NGOs with a global presence, as well as collaboration with a German NGO to create digital innovations using artificial intelligence.

In fast-paced and demanding environments, the ability to manage stress and build resilience is not just a personal advantage but a professional necessity. This workshop explores the latest scientific insights into stress physiology, cognitive resilience, and their relationship with the optimization of performance. Through evidence-based techniques drawn from neuroscience and psychology, students will learn how to regulate stress responses, improve decision-making under pressure, and develop strength in demanding situations.

This course balances theory with application. Students will engage in discussions, self-assessments, and hands-on exercises to develop practical strategies for maintaining focus, adaptability, and composure in challenging settings. By the end of the workshop, participants will have a personalized toolkit for leveraging stress and sustaining resilience in competitive professional landscapes.

This interactive course introduces students to the fundamentals of Entrepreneurship through Acquisition (EtA) and the Search Fund model.

Through a blend of role-play simulations, lectures and case-based group work, students gain hands-on exposure to the entrepreneurial decision-making process, the structure of Search Funds and their growing relevance to SME succession challenges, particularly in the Mittelstand landscape.

The course enables students to reflect on EtA as an alternative career path to entrepreneurship, while building foundational skills in acquisition entrepreneurship.