Handling Big Data
Master of Finance / 24. Oktober 2019
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Professor für Finance
Grigory Vilkov ist Professor für Finance an der Frankfurt School of Finance & Management und akademischer Direktor des Master of Finance. Seine Forschungsschwerpunkte sind der Einsatz derivativer Instrumente, die Untersuchung impliziter Informationen aus Optionsmarktdaten, Asset Pricing und Portfoliomanagement sowie allgemeine Gleichgewichtsmodelle mit Friktionen.


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In addition to the new core module Data Analytics and Machine Learning, starting this year Frankfurt School of Finance & Management has incorporated the tool DataCamp in the Master of Finance to prepare students for a career in a disruptive working environment.

DataCamp will be used as a refresher on Python, as assignments, and as additional material throughout the curriculum. Modules such as Statistics and Econometrics, Quantitative Portfolio Management, Portfolio Risk Management and Financial Products and Modeling will use DataCamp courses with Python as mandatory homework assignments. The courses aim to enable participants to handle, analyse and interpret big data especially in financial contexts.

Skills such as coding are important for the path to success in any career. That is why we want to ensure that our students are well prepared to start working in demanding positions when they graduate. Hence, nowadays young professionals need a technical skillset. Therefore, we offer state of the art academic programmes and teach our students to analyse data so they can make fast and qualified decisions.

Programming is a tool to access and organise large amounts of data so the most significant skills our students learn in courses such as Data Analytics, Machine Learning and Algo Trading & Financial Analysis are coding, data retrieval and organisation as well as efficient data handling for a number of purposes and various applications.

Alexander Maas who started the Master of Finance at Frankfurt School in 2016, stresses how he has learned to source various forms of financial data for analyses: “In the course Algo Trading & Financial Analysis I learned about the common programming syntax for Python, the different packages in Python and the uses for each.” In his view, it is essential to learn a lot about Data Science due to the demand of companies asking for knowledge in programming.

Ion Tapordei, Master of Finance student Class of 2018, thinks that the amount of data available and continuously generated in the financial environment gives the opportunity of making better financial decisions. Therefore, the ability to analyse data is essential.

“Today the challenge in finance is the ability to analyse a lot of data in an efficient manner, to derive relevant models and eventually to automatise the process in an algorithm. This toolset can make an essential difference for a successful finance professional. More and more subjects include programming and Frankfurt School continues doing what it stands for – preparing future talents for the industry.”

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