- First academic degree (Bachelor or Diploma) of at least 180 ECTS credits, preferably in a quantitative field
- Excellent written and spoken English skills** (TOEFL - 90 iBT, IELTS 7.0 or equivalent)
- GMAT/GRE score or Frankfurt School Admission Test(BT Methods Test)
- Successful participation in our admission interview
**Language proficiency waivers are possible for candidates, who have completed a previous degree in English, lived in an English speaking country for longer than a year or whose work language is English (confirmation from an employer required).
Months of entry
The Master's in Applied Data Science is a programme entirely taught in English, designed for young, mathematically-inclined graduates who wish to build a career in data science. Building on your solid quantitative foundations, you will learn the fundamentals of data science, how to apply cutting-edge methods to solve real-world business problems and assess the ethical and legal implications of applied data science to become responsible practitioners in the field.
- Flexible programme structure allowing part-time employment with our 3-Day Model
- A combination of applied Machine Learning, Data Science and Business Problem Solving
- Frankfurt School is #12 worldwide for Best Business Schools for Digital Transformation and #1 in Germany according to The Times Higher Education (THE).
- Extended company projects on real-life cases in cooperation with leading companies during semester 3
- Join our Entrepreneurship Accelerator or Incubator for personalised mentoring
- Ethical ramifications of the fourth wave of industrialisation
- Access to our AI Lab
- Extensive network of cooperating companies and universities worldwide
- Study, network and experience Life in Frankfurt
- Possibility to apply for a scholarship
Fees and funding
Frankfurt School offers multiple funding options that can be found here: https://www.frankfurt-school.de/en/home/programmes/financing/scholarship
Qualification, course duration and attendance options
- full time24 months
- Campus-based learningis available for this qualification
Course contact details
- Gabrielle Crossley