• Develop a basic understanding of numerical algorithms used both professionally and academically in Economics, Business and Finance.
• Be able to utilize the computer to solve problem that humans cannot, and to exploit computational power to gain a deeper understanding of problems for which “pen-and-paper”-solutions are limited/opaque.
• Apply the outlined tools to deepen the knowledge of economic and statistical models concerning demand and supply, monopoly pricing decisions, as well as concepts such as the central limit theorem and endogeneity in estimation.
• Develop a basic understanding of machine learning techniques such as image recognition.

Perhaps you can solve the equation e^x=5, as it amounts simply to x=ln(5), which is about 1.61. But how about the simple extension e^x+x=5? No matter how hard you try, you will not find a solution. But the computer can! The answer is approximately 1.31. To obtain this answer I used the same method a simple calculator uses to compute the square root of a number. It is a very quick and easy way of solving problems that are beyond reach for the human.

The world is full of such problems -- equations being a part of them -- that humans cannot solve but that a computer can. In fact, most problem are only solvable by using the computer – problem ranging from the face-recognition used when unlocking your smartphone, to Google’s search algorithm, and to the Apollo space program. And with more computational power, with more available data, the world is becoming increasingly computational, and a basic knowledge of the underlying methodology is indispensable. This course aims to provide students with the knowledge of these methods and how they can be used in an economic context (including business and finance).

The course is extremely hands-on and focusses little on theory and much more on doing. To give a few examples: with the help of the computer there is no need to draw indifference curves and making them tangent to a budget constraint. The optimal choice can instead be computed directly. Moreover, and in the same context, we can easily calculate the income and substitution effects of price changes; the incidence of taxes on various parties; and the deadweight loss of monopoly power. Neither do we have to draw arrows or shift graphs, but we can instead let the computer do the work for us. And during the course we will learn do these things on our own. Moreover, we can simulate random data and get an idea of the consistency or unbiasedness of an estimator, and we can explore issues like endogeneity, small-sample bias, and confidence bounds. All at our finger tips, by using the computer instead of mathematical theory. In addition, the computer can even help us to understand theory better; a mysterious and magical concept like the central limit theorem – which underlies almost all significance testing in econometrics – can very be hard to digest. But the computer can help us to visualize its effect, providing us with a deeper understanding of a difficult mathematical result.

The world is becoming increasingly computationally intense by the minute. The reason is partly due to an increase in data availability, and partly to an increase in computational power. This course provides an insight into the most relevant methods used everywhere across the world, ranging from purchasing algorithms at Amazon, to research methods across a wide spectrum of disciplines.

The course’s development of personal competencies:

This is a very hands-on and general course that will provide a broad knowledge of computational algorithms. The methods are not limited to a specific software, but can be applied in a multitude of context. Thus, students can expect to leave the course with a transferable newfound knowledge of the digital revolution and how its associated tools can be used in an economic context. You will understand how to formulate a problem, how to use the computer to analyse it, and ultimately to solve it.

Textbook/ selected chapters will be provided