This module introduces big data as a key concept in today’s business world, as firms increasingly rely on large volumes of digital data from customers and transactions. This creates opportunities to extract valuable information using statistical and machine learning methods to gain a competitive advantage. Big internet companies are the most visible examples of this development, but the topic is also highly relevant for the financial industry, including credit analysis, fraud detection, insurance, and robo-advising. The course focuses on how big data can be used for prediction, for example, of bankruptcies or stock prices, and emphasizes supervised learning techniques such as regression and classification.
- Understand basic statistical methods for strategic decision-making
- Analyze large-scale business data using machine learning techniques
- Apply basic methods for analyzing data in R
- Apply what they have learned to real finance data
This course is part of the prestigious part-time Master in Finance, conducted in English on Fridays and Saturdays on Campus Westend. It offers a valuable opportunity to network and gain expertise without committing to a full degree program. Upon completion, participants receive a Certificate of Participation. This course may be credited towards the Certificate of Advanced Studies (CAS) in Financial Technology Management when completed together with the other required courses within one academic year. As the number of seats is limited, we recommend to register early. If you're a GBS or Goethe University alum, explore our attractive alumni discount options.
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Next course offering: Summer Semester 2027. New dates will be announced soon.
Dr. Navid Sabet
Dr. Navid Sabet is a Post-Doctoral Researcher at the Faculty of Economics and Business Administration at Goethe University Frankfurt. He completed his PhD in Economics at LMU Munich under the supervision of Davide Cantoni and holds an MPA in Public and Economic Policy from the London School of Economics. His research focuses on political economy, applied microeconomics, and public economics, with publications in journals such as the American Economic Journal: Economic Policy, the Journal of Political Economy Micro, and the Journal of Public Economics. He is also a Research Affiliate at CESifo.
Candidates with an undergraduate degree (i.e., bachelor or equivalent) of 180 credit points (CP) based on the European Credit Transfer System (ECTS) can apply for our Open Programs. The allocation of credit points for work experience will appear on the Academic Transcript of Records and be titled ‘Prior Learning Assessment’.
Open Program applicants must submit a recent proof of English language proficiency at the higher B2 level per the Common European Framework of Reference (CEFR). The GBS Office of Admissions will accept test results of the following tests, taken no longer than two years ago upon the date of submission:
- TOEFL iBT®
- TOEFL iBT® Home Edition
- IELTS
- Cambridge Certificate of Proficiency in English
- English Language Level Test (ELLT) - Oxford International Digital Institute
Applicants may request a waiver of proof of proficiency in English in case one of the following conditions applies. The applicant
- is a native speaker of English;
- has graduated from an international baccalaureate program
- has completed their undergraduate or master-level studies in a program taught entirely in English;
- has worked in an English-speaking country for at least one year.
If the applicant has not taken any of the above tests when submitting the application, the applicant can upload a confirmation of the test registration to the online admissions portal.
Applicants who submit a language certificate older than two years but without an expiry date will be invited for an additional interview with a native speaker of English.
Proof of relevant post-undergraduate work experience of at least one year is to be submitted, e.g., a performance review, reference letter, or written confirmation from your employer.