The goal of this course is to teach existent and potential managers or entrepreneurs to think scientifically when they make innovation decisions, such as decisions about new investments, new products, the adoption of new technologies and practices, or the launch of business ideas. We will see that good decisions require the use of logic, probabilities, and analyses of data. However, the course requires no technical background. Its goal is to help managers and entrepreneurs to embrace the use of logic, tools, and data, even if they have no technical background. The motivation of the course is that many innovations and new firms fail not because of technological problems, but because of wrong managerial decisions. This often depends on the inability to frame problems and to predict the implications of actions. How can we start thinking about this issue? For quite some time now, we have been able to observe managers and entrepreneurs who think scientifically. They were able to overcome difficulties in making decisions. In addition, by teaching how to use and interpret data, we show how to put to use a resource that is becoming increasingly easy to find and use for managerial decisions. We recognize that there are other similar approaches. For example, Agile development, design thinking, discovery by design, and the lean startup method. However, the combination of theory and data and the framework that explains why this framework helps entrepreneurs is unique to our approach. Also, there is often an emphasis on entrepreneurship as actions and gut feelings. We recognize that this view has merits. However, entrepreneurship may have gone too much in this direction, and we need some rebalance to include more structural thinking. The course emphasizes practice, providing students with practical skills and expertise on how to apply the approach to concrete managerial or entrepreneurial problems. The course is articulated in five parts. Part one presents the big picture. It starts with an introduction to the scientific approach to decision making, what it does and how, and what are its implications. We present innovation as problem-solving before and discuss the building blocks of the scientific approach to innovation decisions: from how to formulate the problem to how to formulate the hypotheses and the theory, and how to test them. The whole discussion is framed and applied to concrete managerial problems including a discussion of specific managerial tools. In part two, we move from the general framework to the details of the scientific approach, and explain how to use probabilistic thinking. As we said, the course is meant for students who have no background on these matters. What we will do is that we will then bring any student up to speed on the logic of these concepts. In doing so, we want to satisfy the increasingly common requirement that today's managers need to understand probabilities to make decisions. We will then be able to explain the implications of different approaches to decision-making, and particularly the scientific approach. This will help to understand how and why certain decisions lead to some outcomes instead of others, and how to make better decisions. We'll also provide concrete examples and specific tools that can be used to formulate and test hypotheses, and we will show you how to design and run experiments. Part three makes a deep dive into data analysis. The goal of this part is to teach the basics of data analysis. It starts with the distinction between correlation and causality in the analysis of data. An important takeaway of the course is that correlation enables decision makers to make predictions, but only causal analysis enables them to indicate an action to be taken. Topics in this part include regression analyses and practical applications of the scientific approach with examples, cases, and tools. Part four is a more advanced part in which we discuss causality and provide you with some broad exposure to Big Data and machine learning, and we discussed how they can inform managerial decisions. We will wrap up with a discussion on when the scientific approach is most appropriate or has limitations. This helps to see when to apply this approach or when to apply other approaches including our own gut feelings. The final part, part five, consists of a final project in which we ask you to apply what you learned during the course to real-world issues through a final assignment. By the end of the course, students will be able to correctly frame problems, articulate hypotheses, and test quickly with rigorous experiments or statistical tests. Are there steps you can follow? Yes. With this course, we laid out a process that will guide you in the innovation journey. We chose to include several practical examples, interviews, cases, and tools that simplify the process of decision-making while creating new products or services. All in all, we had lots of fun in preparing this course, and we believe that - in so doing - we also offer some opportunities to learn how to improve decision-making in firms or startups. We really hope you will enjoy the course too, and learn as much as we did in creating it. We all look forward to welcoming you to the course.
