Master in Applied Data Science

Programme

The MSc in Applied Data Science is a response to the recent demand for business savvy data scientists with the collaborative skills to match. Our students will gain hands-on experience in solving real-world data science problems with our prominent industry partners, including Commerzbank and PwC, and will be equipped with the technical skills, business domain knowledge, and critical judgment to navigate the modern data ecosystem. Our students will master core data science and machine learning concepts, as well as the art and science of problem decomposition and solving. They will be able to identify business needs and wants, as well as problems, and propose relevant solutions using machine learning tools and by applying sophisticated statistical techniques. To do this, they will collect, transform and visualize data, create data models, as well as run predictions and simulations.

In a nutshell: The Frankfurt School of Finance & Management Master in Applied Data Science Programme provides the skills required to recognise and meet the data science wants of contemporary business, across-function and with an understanding of the connected ethical ramifications.

Curriculum

• BSc degree with a focus on mathematics, statistics, logic, or computation. Alternatively, non-quantitative degree, supplemented by official results obtained via the GMAT, GRE or FSAT
• Excellent written and spoken English (TOEFL - 90 iBT, or equivalent)
• Successful participation in our admissions interview

Language(s) of instruction:	English
Going abroad:	obligatory

Study Models: 4-Semester Track (3-day Model)

The Master in Applied Data Science follows a unique time model that permits you to work part-time whilst pursuing your full-time Master's degree. We call this the "3-day model". Our students typically attend classes three days a week. This leaves you with three working days for self-study, language courses or part-time employment. Some lectures or excursions are organised as blocked week events.