Financial Engineering (MSc, Executive) at HECTOR School

We see modern Financial Engineering as the science of data-driven decision-making in business environments. Building more accurate models reduces uncertainty around future events and paves the way to better decision-making. Learning from data and using classical statistical concepts and novel concepts from machine learning help businesses across industries and geography solve predictive data analytics and valuation problems. Today’s predictive learning schemes perform tasks that were previously only solvable by a limited group of experts. Advances in predictive analytics and learning will affect all business models and industries. Financial tasks in particular will be transformed at an astonishingly fast pace. Vast amounts of data, paired with the individual's and institution's desire to plan ahead to meet future obligations and investments, make financial decision-making in its broadest sense an especially appealing application of predictive analytics and learning schemes. Our Master’s programme in Financial Engineering with a special focus on Data Science, Artificial Intelligence (Machine Learning) and Predictive Analytics prepares decision-makers to model and understand data across a variety of business fields and problems. The first two engineering modules teach fundamentals of finance, financial economics, data science and Python and pair these with novel developments in the field of digital business models, allowing our students to grasp the status quo and business opportunities that arise in this lucrative business field. The third and fourth engineering modules introduce business decision-makers to machine learning and engineering aspects to ground data-driven decision-making in hard science. The last engineering module is devoted to teach how alternative data, for example, in the form of text data, and advances in machine learning can be used to innovate in tomorrow’s business world. Most of these engineering modules are divided into a conceptual and a hands-on computational part to allow our Master's students to understand and work with predictive analytics and learning schemes in a variety of decision-making contexts. For the Master's thesis, we encourage our students to aim high and to solve a data problem for individuals, institutions or society at large. We believe there is no better time to start your own data-driven technology adventure than during your Master's thesis. The vibrant technology environment of the KIT, together with the numerous businesses in the area of Karlsruhe, offers a rich pool of problems that wait to be solved. Each module has a duration of 10 days. Preparatory Courses: For applicants who hold an academic degree outside of the required fields but have several years of relevant professional experience, we offer preparatory modules in "Probability and Statistics". The exact dates are available upon request. Engineering Modules (EM)*: EM 1 Digital Financial Markets: Global Financial Markets Block Chain Technology Digital Currencies and Business Models Introduction to Python EM 2 Financial Economics for Data Scientists: Financial Economics Fundamentals of Financial Data Science EM 3 Machine Learning for Data-Driven Decision-Making: Machine Learning for Decision-Makers Fundamentals of Financial Machine Learning Kernel and Bayesian Methods in Machine Learning EM 4 Engineering Aspects of Financial Markets: Fundamentals of Financial Engineering Derivatives and Value of Optionality EM 5 Alternative Data and Machine Learning for Business Applications: Text Mining and Natural Language Processing Advances in Machine Learning and Pattern Recognition Management Modules (MM)*: MM 1: Marketing & Information: Data-Driven Marketing Information Systems Management Data Analytics Legal Aspects of Information MM 2: Finance & Value: Management Accounting Sustainability Strategic Financial Management Case Studies MM 3: Decisions & Risk: Decision Modelling (+ Computer Tutorials) Risk-Aware Decisions (+ Case Studi