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Degree
Master of Science (M.Sc.)

Language
EN

Tuition Fee
EUR 32,500

Application deadline
30 June

Programme Start
23 August

Duration
4 Semester | full-time

Master in Applied Data Science

The Master in Applied Data Science is a programme 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.

Learning Goals

LG 1: Expert Knowledge and Understanding of Applied Data Science

Graduates will have an in-depth knowledge and a critical understanding of the key theories, principles and methods in Data Science. They will be able to identify, analyse and evaluate complex data problems.

LG 2: Application of Analytical Techniques and Machine Learning Algorithms to Solve Complex Data Problems in Business

Graduates will have the ability to construct and critically assess computational, data-driven models to solve complex data problems in business.

LG3: Effective Communication and Cooperation

Graduates will be able to communicate effectively in academic and/or private business contexts. They will formulate technical problem solutions and represent them in discourse. They are responsible team members who address and reflect different perspectives.

LG 4: Professional and Responsible Behaviour

Graduates are practiced collaborators in the business environment. They have a thorough understanding of their ethical and legal responsibilities as applied data scientists. They will base their professional activities on theoretical and methodological knowledge.

Highlights

  • A combination of applied Machine Learning, Data Science and Business
    Problem Solving
  • Extended company projects on real-life cases in cooperation with leading companies during semester 3
  • Ethical ramifications of the fourth wave of industrialisation
  • Flexible programme structure allowing part-time employment with our 3-Day Model
  • Access to our AI Lab
  • Extensive network of cooperating companies and universities worldwide
  • Study, network and experience Life in Frankfurt
  • 120 ECTS

Requirements

  • First university degree (Bachelor or Diploma) of at least 180 ECTS credits, preferably in a quantitative field
  • Excellent written and spoken English skills (TOEFL - 90 iBT, 577 ITP / IELTS 7.0 or equivalent)
  • GMAT/GRE score or Frankfurt School Admission Test/BT Methods Test
  • Successful participation in our admission interview

Deadlines & Discounts

We encourage you to complete your application as soon as possible as there are financial advantages for candidates who submit a complete application early.

Early Bird I
(EUR 4,000 discount)*
30 Nov 2020
Early Bird II
(EUR 2,000 discount)**
31 Mar 2021
Final Application and Scholarship Deadline 30 Jun 2021

*In order to secure the discount you must have received an admission letter by 31 March

**In order to secure the discount you must have received an admission letter by 30 June

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Meet us Online and On-Campus 

Learn more about our Master programmes at one of our Master Info Evenings and find out which programme could help you excel in your career. 

You may also come and explore our campus and speak to representatives from our master's programmes face-to-face, at one of our Open Campus Nights.

Where can you meet us?

We also offer several other ways to get in touch with us such as class visits, fairs outside of Frankfurt and personal consultations. Please visit our page to find out more!

Cooperation Company Project

During semester 3, students are able to test the knowledge they have learned in previous semesters by working on real business use-cases together with leading companies. Over 3-4 months students will work closely with the company from the start to finish of the project, thus gaining end-to-end, hands-on experience to better prepare them to enter the job market.

Master in Applied Data Science: Curriculum

Our Master in Applied Data Science curriculum provides students with the skills required to recognise and meet the data science needs of contemporary business, cross-functional, and understand the connected ethical ramifications. Students will master core data science and machine learning concepts in modules as well as gain company exposure and perspectives with our Cooperation Company Project.

1

Quantitative Fundamentals

Quantitative Fundamentals

Students will acquire a rudimentary understanding of linear algebra, probability theory, information theory and their use in machine learning and data science. Paying particular attention to mathematics for information systems, this module serves as a foundation module for Machine Learning 1 & 2.

Lecturer

Prof. Dr. Jan Nagler

Algorithms & Data Structures

Algorithms & Data Structures

Using Python, this module provides you with an introduction to basic algorithms, as well as the design analysis of algorithms and data matching structures. This allows you to implement taught algorithms and learn the basics of Python.

Lecturer

Vahe Andonians

Intro to Data Analytics in Business

Intro to Data Analytics in Business

Data Analytics (or Data Science) is an emerging field in industry and academics. It covers methodologies, algorithms and processes to tackle the challenges in times of big data, where we are confronted with large amounts of high-dimensional data of different types. While the classical statistical approach has some weaknesses in this context, new ways and methods of data analysis have been established under the term machine learning. Today, they are widely used in science and practice benefitting from the calculation power of modern computer technologies.

This module provides an introduction to Data Analytics, covering computational techniques and algorithms for finding and analysing patterns, even in large-scale datasets. Topics to be covered include data preparation, integration, analysis, visualisation, segmentation, classification, prediction and decision making. You will implement and apply the methods using the programming language, Python and the related libraries.

Lecturer

Lukas Böttcher

Computational Statistics & Probability

Computational Statistics & Probability

This course introduces causal inference and generalised linear multilevel models from a Bayesian perspective. The aim of the course is to give you a hands-on introduction to the fundamentals of statistical modeling.  We will cover the basics of regression up to advanced multilevel models, focusing on the algorithmic details throughout the course to build your understanding of and confidence with model-based computational statistics.

Lecturer

Prof. Dr. Gregory Wheeler

The Language of Business

The Language of Business

This module serves as introduction to accounting as the language of business and its various purposes and applications. On a very fundamental level, accounting statements are a primary source of systematic public information about businesses, providing the basis for answering many relevant questions. As such, it is important for those interested in business data analytics.

External Lecturer

Pia Puth

2

Databases and Cloud Computing

Databases and Cloud Computing

Nowadays everyone is aware of the ever-growing importance of the data streams fueling the economy and becoming the future catalyst for our society. Learn how to master these streams by understanding the key concepts of the most important frameworks and technologies for data storage and management.

External Lecturers

Prof. Dr. Peter Roßbach & Kerem Tomak

Machine Learning 1

Machine Learning 1

This module is a hands-on, case-study based introduction to contemporary regression-based techniques in machine learning. Machine Learning 1 has a focus on supervised learning algorithms (used to make accurate predictions about the future from current data) and unsupervised learning (used to discover unknown structure in your current data).

Lecturer

Prof. Dr. Gregory Wheeler

Guided Studies in Financial Management

Guided Studies in Financial Management

The course provides an introduction to financial management, including capital budgeting and capital markets. The main focus is on designing and conducting empirical analyses in small teams.

Lecturer

Prof. Dr. Frank Ecker

Machine Learning 2

Machine Learning 2

This hands-on module focuses on statistical machine learning and probabilistic data analysis involving highly parameterised models. Topics include time series analysis, variational inference, graphical models and unsupervised learning. You will learn how to implement supervised and unsupervised machine learning models and gain an understanding of the computational challenges faced when performing statistical inference on high-dimensional data.

Lecturer

Prof. Dr. Jan Nagler

AI & Humanity: Ethics of Data Science

AI & Humanity: The Ethics of Data Science

This module explores the ethical and legal questions that information technologies raise for issues such as privacy, responsibility or fairness. Participants will gain an in-depth comprehension of legal and ethical issues surrounding information technologies, as well as the crucial legal and ethical questions that we should ask about such technologies. On successful completion of this module, students will have developed and strengthened their analytic and critical skills, as well as their ability to apply those skills to ethical and legal problems and develop solutions to those problems.

Lecturer

Prof. Dr. Sebastian Köhler

Summer School, Internship or Skills Development Courses

3

Strategy and Performance Management

Strategy and Performance Management

This module gives you the latest insights into strategy development and execution with a strong emphasis on organisational and machine learning on data analytics. Students become acquainted with models, tools and techniques to develop, analyse and execute organisational strategy and its success.

Lecturer

Prof. Dr. Matthias Mahlendorf

Deep Learning

Deep Learning

This module covers deep neural networks, which are currently the “workhorse” of machine learning and most commonly used methods. Our main purpose will be to understand the theoretical background necessary to employ deep neural networks to solve problems of image recognition and language processing. Particularly, we focus on different theoretical concepts to make deep neural networks which are thus essential for building successful applications. The module has a practical focus, taking theory and then applying it immediately in each class.

Lecturer

Prof. Dr. Florian Ellsäßer

Natural Language Processing

Natural Language Processing

This module is focused on applying machine learning techniques to gain language understanding. Natural language processing is one of the main sub-fields of machine learning and has driven major algorithmic break-throughs in recent years. Language is a form of time series so break- throughs in natural language processing such as LSTM networks have been closely connected to advances in machine learning in general.

Lecturer

Prof. Dr. Florian Ellsäßer

Cooperation Company Project

Cooperation Company Project

This module is a practical project conducted with a partner company which allows students to apply the skills they have learned during other semesters. Students will work in groups of 3-4 on small, current data science projects within the company under the supervision of a professor and company representative. Students will learn how to illustrate and decompose business problems as well as cleaning and managing data at all stages and then applying data science and machine learning to create a service or software for the project. 

Lecturer

Vahe Andonians

4

2 Electives or Entrepreneurship Accelerator or Semester Abroad

Master Thesis

Full list of electives

Students have the opportunity to replace their two elective modules with either a semester abroad at one of our international partner universities or take part in our Entrepreneurship Accelerator.

Master Thesis

Master Thesis

You are required to conduct independent research in order to complete your Master's thesis. You will review relevant scientific publications and acquire an in-depth knowledge in the respective field before applying research methods and writing concepts to structure your work. The thesis period is typically three months and takes place during the 4th semester.

All modules bear 6ECTS.
The Cooperation Company Project bears 12ECTS.
The thesis bears 18ECTS.

Entrepreneurship Accelerator

As technology advances rapidly and businesses transform to become more sustainable, it is a necessity for future managers of international corporations to think and act with an entrepreneurial mindset. We offer you the chance to apply your learning to your own real life start-up project with our Entrepreneurship Accelerator as an alternative to your two electives in semester 4.

Rankings & Accreditations

Frankfurt School is one of the best European Business Schools. Accredited by AACSB, EQUIS and AMBA, the three leading international associations of business schools. Frankfurt School is one of the few institutions worldwide, which has been awarded the so-called "Triple Crown".

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Study Model

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".

Students typically attend classes three days a week, on Thursdays, Fridays and Saturdays. This leaves three working days for self-study, language courses or part-time employment. Some lectures or excursions are organised as blocked week events.

Information for FS Bachelor Students

FS Bachelor students are offered a 3-semester track of the programme. You will start the programme with the regular 4-semester track and finish one semester early. For more information please contact us.

Important dates (relevant for FS Bachelor students)
  • Early bird deadline (EUR 2,000 discount): 31 May 2021
  • Final deadline: 30 June 2021
  • Programme start: 23 August 2021

Study Abroad

Frankfurt School partners with 80+ universities worldwide that are primarily focused on business and management and giving our students the opportunity to gain comparative viewpoints, diverse cultural and study environments and widen their international network. This gives you the chance to immerse yourself in a new environment that prepares you for a global career. Students taking the 4th semester track of the programme are able to go abroad in their 4th semester.

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Learning Experience

Our Master in Applied Data Science applies a practical approach to your studies by preparing you for the realities of data science in the working world. We do this by strengthening your statistical, mathematical and computational skills and by exposing you to everyday working life as part of our cooperative company projects. 

AI Lab

The AI Lab provides a space where new learning concepts can be developed, tested and immediately implemented into the teaching programme. You can request access to this creative study space shared study space during their studies. As a Master in Applied Data Science student, you are also invited to attend relevant events, workshops and hackathons that are held in the space. Additionally, the Lab is equipped with up-to-date technology including four high-end computers using the latest GPUs for AI acceleration.

Find out more about our AI Lab here.

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Mapping the Cryptocurrency Market - Blockchain Workshop, Luca Frignani with Master in Applied Data Science students

Cooperation Company Project

Working on extended company projects in cooperation with leading partner companies enables you to learn on the go. Students will work together in groups alongside a professor to come up with solutions for real-world problems.

Case Studies

Students will work on current and past case studies as part of their modules. Students will especially go deeper into how to solve real-life data problems with Machine Learning in Machine Learning 1and Machine Learning 2. This allows you to work on real-life case studies preparing you for the realities of the working world. 

Hackathons

Our Hack@LAB hackathons allow students to solve problems chosen directly by a leading company. Students from a range of skill-sets come together and work on the problems using machine-learning techniques and algothrims. 

Read about our Hackathon with Deloitte

Read about our Hackathon with the ECB

Competitions & Awards

Frankfurt School encourages students to participate in various challenges and competitions throughout the year, giving selected students the opportunity to prove themselves and compete against others from top universities worldwide.

St. Gallen Business Game

Hec Business Game

German Classes

The Master in Applied Data Science is taught entirely in English. However, German classes are provided for all non-German speaking students throughout the duration of the programme. We strongly encourage our students to learn German before they arrive in Frankfurt in order to improve their employment opportunities.

Student Career Paths

The combination of practical learning methods and ability to work part-time ensures our Master in Applied Data Science students are able to secure jobs in a variety of companies and industries by the time they graduate. Below are examples of where our students worked during and following their studies. Take a look at some more here.

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Careers & Your FS Network

On completion of the Master in Applied Data Science, you will be qualified to connect the dots for businesses. Companies, including the Big Four, are seeking experts who understand specific wants and needs and can provide relevant solutions for genuine business transformations. Job opportunities will include but not be limited to Data Analyst, Business Analyst, Data Visualisation Engineer, Internal Data Science Consultant and new roles in all sectors that are experiencing a digital transformation.

Career Services

Our exclusive corporate connections allow you to build a strong network for your career. Our Careers Services team are available to provide you with individual consultations on careers within business and management. This along with our regular guest lectures and company visits, plus the opportunity to work part-time throughout your full-time studies, puts you in the spotlight for employment after graduation.

Cooperation with Proresult

Proresult is a financial service consulting company with projects in Frankfurt. For students with a background or interest in financial consulting and C1 level German skills, this is a fantastic opportunity to gain experience while studying. The cooperation guarantees a two-year part-time paid position at the company (3 days a week). In return, Proresult covers tuition fees in full.

One cooperation opportunity will be offered per intake. The selection process considers academic excellence, personal and professional achievement as well as performance in the assessment process. When applying for this cooperation, applicants agree to share their data with Proresult for the selection process. Job interviews and the final selection of a candidate for the cooperation will be conducted by Proresult.

Candidates should complete the cooperation application as part of their online application. Find out more here.

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Contact

You can contact Proresult directly if you would like to know more about the position.

Contact:

Raoul Stirkat

raoul.stirkat@proresult.de

#FSMaster on Social Media

Study in Frankfurt

Frankfurt is the commercial hub of Europe where major global corporations, consultancy firms, high-tech industries and startups are located alongside the European Central Bank. Frankfurt School plays a key role in supporting these strategic partner industries through its cutting edge research and innovative solutions focused on consultancy, tech, banking and finance.

Invest in your international career development by leveraging on the opportunities and business networks offered by the city of Frankfurt.

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Student Life

Frankfurt School takes pride in its international student community. Many of our aspiring and inspiring individuals lead important initiatives such as fundraising for environmental causes or for tech and innovation start-ups as well as engaging in consultancy competitions, sports and wellbeing activities.

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Class Profile

Overview

Number of students 48
Nationalities 17
Average age 25

Origin of Students

Germany 28%
Asia 52%
Americas 10%
Europe excl. Germany 10%

Gender

Female 42%
Male 58%

Educational Background

Accounting & Finance 21%
Business Administration 23%
Economics 19%
Engineering 8%
International Business & Management 10%
IT & Computer Science 9%
Other sciences 10%

Application Process

1. Online Application

The first step of our application process is to complete the online application form. You will need to upload the following supporting documents: 

Required Documents

  • Certified copy of your undergraduate transcript of records and degree award certificate
  • Certified copy of your TOEFL / IELTS results or equivalent (TOEFL - 90 iBT, 577 ITP / IELTS 7.0 or equivalent)
  • Official GMAT score report, GRE score report or FS Admissions Test
  • CV or resume (must be in English)
  • Other documentation supporting professional experience or other extracurricular activities, if applicable

Each of the documents listed above are required for completing your application. However, you do not need to upload them all at once.

Deadlines & Discounts

Applications are considered on a rolling basis, therefore we encourage you to apply as early as possible. Applications received before the end of November and March will benefit from our early bird discounts of EUR 4,000 and EUR 2,000 respectively. Applicants interested in a scholarship must complete the relevant section within their online application.

Early Bird
(EUR 4,000 discount)*
30 Nov 2020
Early Bird
(EUR 2,000 discount)*
31 Mar 2021
Final Application and Scholarship Deadline 30 Jun 2021
Start of programme 23 Aug 2021

*You must have received an admission letter by 31 March and 30 June respectively

2. Assessment Centre and/or Interview

Successful applicants will be invited to an Assessment Centre and Frankfurt School Admission Test, if applicable. The interview will be held either at Frankfurt School, or over the phone/video call. The purpose of the interview is to gain a better understanding of your character, personality, expectations, motivations and goals.

3. Results

The final decision regarding your Master in Applied Data Science admission will be based on a combination of undergraduate grades, English language abilities, admission test results and interview performance.

We also take into account other significant experiences, commitments or awards such as internships, international experiences and volunteer projects.

4. Programme Start

On this day, all students are expected to be at Frankfurt School. For non-EU applicants who require a visa to enter Germany, please keep in mind that it can take up to two or three months to obtain the necessary visa.

We look forward to welcoming you at your campus!

Information for FS Bachelor Students

FS Bachelor students are offered a 3-semester track of the programme. You will start the programme with the regular 4-semester track and finish one semester early. For more information please contact us.

Financing and Scholarships

Investing in your future

Your degree is an investment in your professional future. As a business school of international standing, not only do we offer you ideal conditions for earning a degree – we also offer you excellent career prospects.

Since we can guarantee the quality of our teaching and research, we expect and encourage the highest levels of commitment and motivation from our students.

Hagenmüller Foundation Scholarship

Professor Dr. Karl Friedrich Hagenmüller was a founding member of the bank academy, which later became Frankfurt School.

The Hagenmüller-Foundation is pleased to support one Master in Applied Data Science student a year through a partial scholarship of EUR 5,000. The Hagenmüller Foundation Scholarship is awarded to an outstanding candidate on an individual basis. As an applicant of the programme you automatically go into the running to receive the scholarship and it will be awarded before studies start.