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Master in Applied Data Science student Master in Applied Data Science student
Degree
Master of Science (MSc)

120 ECTS

Language
EN

Tuition Fee
EUR 35,500*

*excl. 250 EUR enrolment fee

Application deadline
30 June

Programme Start
26 August

Duration
4 Semester | full-time

Designed for graduates with mathematical acuity, our Master in Applied Data Science programme prepares students to become insightful and influential professionals in the fascinating and dynamic field of applied artificial intelligence and data science. Building from strong quantitative foundations, the programme guides students through core principles to a comprehensive understanding of data science applied to solve real-world business challenges.

 Highlights
  • Interdisciplinary Integration: Explore a curriculum that integrates applied machine learning, data science, business, and finance
  • Work & Study: Experience the perfect blend of career growth and flexibility through our 3-Day Model structure allowing part-time employment within our programme structure
  • Industry Collaboration: Engage in company projects, collaborating with industry leaders and dynamic startups to solve real-world business problems
  • Leading Faculty: Learn from our renowned faculty, pioneers in groundbreaking research and leaders in launching innovative FinTech ventures
  • Entrepreneurial Development: Benefit from personalised mentoring in our Entrepreneurship Accelerator and Incubator
  • Ethical and Legal Insight: Navigate AI's moral and legal dimensions responsibly in the fourth industrial wave
  • Network: Join a global Alumni network of business leaders and entrepreneurs shaping diverse industries
 Requirements
  • First academic degree (Bachelor or Diploma) of at least 180 ECTS credits
  • Excellent written and spoken English (TOEFL - 90 iBT, IELTS 7.0 or equivalent)
  • Valid GMAT/GRE score* or Frankfurt School Admission Test (BT Methods)**
  • Successful admissions interview

*We accept valid class or Focus edition GMAT scores.

**Please note: The Frankfurt School Admission Test (BT Methods) can be taken once.

Deadlines & Discounts

We encourage you to submit your application as soon as possible to benefit from our Earlybird discounts. Just keep in mind that the application must be complete to be eligible. 

Early Bird 1
(EUR 4,000 discount)*
30 Nov 2023
Early Bird 2
(EUR 2,000 discount)**
31 Mar 2024
Final Application and Scholarship Deadline 30 Jun 2024

*In order to secure the Early Bird 1 discount you must have received an admission letter by the 31 March

**In order to secure the Early Bird 2 discount you must have received an admission letter by 30 June

Learning Goals

Knowledge and understanding (broadening, deepening and understanding of knowledge)

Graduates will have 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.

This competency is particularly relevant to and developed in the core modules, such as Algorithms and Data Structures, Introduction to Data Analytics in Business or Computational Statistics and Probability and via various means of teaching, learning and assessment (e.g. projects, programming assignments or exam).

Usage and development of knowledge

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

The application of analytical techniques is at the core of almost all modules in the programme. The Master in Applied Data Science encourages not only to learn, but rather to apply models to the classroom (e.g. Machine Learning I and II or Deep Learning).

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.

These competencies are practiced in many modules such as Introduction to Data Analytics in Business or Guided Studies in Financial Management in which students have to do several week-long projects in order to understand and apply the knowledge and skills they have gained in the module. This competency is furthermore at the core of the Cooperation Company Project. Our students are able to test the knowledge they have learned in previous semesters by working on real business use cases together with leading companies in the Cooperation Company Project. Over a period of approx. two months, students will work closely and cooperatively with the company from the start to finish of the project, thus gaining end-to-end, hands-on professional and personal experience. 

Scientific self-image and professionalism

Graduates are practiced collaborators in business environments. 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.

The development of these competencies is distributed throughout the curriculum and in consequence, follows the natural student journey as they grow academically and professionally. The culmination of students’ individual awareness of their role in Business and Society can be found in their final project, the thesis, and in the core module AI & Humanity – The Ethics of Data Science. On successful completion of this module, students will have a thorough comprehension of central legal and ethical issues surrounding information technologies, as well as the crucial legal and ethical questions we must ask about such technologies. Students will furthermore be able to identify and evaluate legal and ethical problems related to information technologies, develop and critically assess appropriate responses to such problems, and assess their own evaluative outlook critically. Finally, students will have developed and strengthened their analytic and critical skills, as well as their ability to apply those skills to solve ethical and legal problems.

Rankings & Accreditations

FT_RANKINGS_2022_EU Business School

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".

Master in Applied Data Science: Curriculum

Our Master in Applied Data Science programme is structured on four pillars, each designed to form a cohesive and expansive educational journey.

Technological Mastery: The first pillar immerses students in the theoretical and technological foundations of data science and artificial intelligence, covering essential domains such as algorithms and data structures, computational statistics, machine learning, deep learning, and cloud computing, among others, laying the groundwork for technical proficiency.

Business Process Integration: The second pillar exposes students to the symbiotic relationship between data science and business processes, highlighting how data-driven insights drive better business operations and decision-making.

Ethical and Legal Awareness: The third pillar provides a critical examination of the ethical and legal landscapes in data science and artificial intelligence, preparing students to navigate the moral complexities and risks posed by statistical technology.

Practical Business Applications: The final pillar focuses on the practical application of data science and artificial intelligence within business, enabling students to translate their knowledge into actionable solutions that drive business innovation and growth.

Free pre-courses in Python and Mathematics are offered in August, before the study programme begins.

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

Prof. Dr. Lucas 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

Dr. Heike Dengler

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 & Dr. Jörg Gottschlich

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.

External Lecturer

Dr. Stefan Scharnowski

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: The 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

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

Prof. Dr. Gregory Wheeler

4

Electives or Entrepreneurship or Study Abroad

Electives or Entrepreneurship or Semester Abroad

Full elective list

A range of electives allows you to tailor your Master in Applied Data Science through a diverse and distinctive structure of time formats. Electives are taught not only by in-house faculty but also by leading international practitioners, providing you with the tools to meet your personal aspirations. Elective options are published during the third semester and students must choose elective modules to start in their last semester.

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

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 6 ECTS.
The Cooperation Company Project bears 12 ECTS.
The thesis bears 18 ECTS.

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.

Personalise your studies

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.

Entrepreneurship Centre

The mission of the Entrepreneurship Centre is to inspire, connect and provide training to Frankfurt School students as well as to external stakeholders such as investors, alumni, founders and partners. You can choose to take part in our Incubator for year-round guidance or boost your project by taking part in the Accelerator. You can also choose the Entrepreneurship module in semester 4 and make this specialisation part of your degree!

Electives

Students can choose in semester 4 from a wide range electives focused on finance, management, and data science related topics, giving them the opportunity to expand the depth of their Master programme and gain insights in addition to the primary topics in other areas of interest depending on their professional goals.

Study Abroad

Frankfurt School partners with 90+ 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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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.

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.

Timeline-Agrawal

Timeline-Merl

Employment statistics - Class of 2022

Starting Salary after Graduation*

Average Salary (Including Bonus) 68,700 Euro

*excluding trainees and interns

Where do our graduates work?

Our Master in Applied Data Science alumni have secured jobs in a variety of companies and industries since graduating. Take a look at some of their career paths.

List of employers

Accenture

Amazon

BASF

Bayer

Boston Consulting Group (BCG)

Commerzbank AG

Deloitte

Deutsche Bank AG

Deutsche Börse

DZ Bank AG

EY

GIZ

HSBC

ING Germany

JPMorgan Chase & Co

KMPG

Lufthansa Group

McKinsey & Company

Merck KGaA

Wayfair

Employment by Industry

Consultancy 25%
Financial Services (excl. Banking) 25%
Banking 15%
Healthcare/ Pharmaceuticals/ Life Sciences 10%
Technology 10%
Engineering 5%
FinTech 5%
FMCG 5%

Employment by Function

Data Science / Data Analytics 45%
IT Department 15%
Risk Management 5%
Forensic 5%
Transaction Banking 5%
Consulting 5%
Audit Department 5%
General Management 5%
Business Development 5%
Operations Management 5%

Time until employment

Out of all the students looking for employment, 82% found jobs within 6 months of graduation.

Country of job entry

Germany 90%
Poland 5%
UK 5%

Class Profile

Overview

Number of students 30
Nationalities 11
Average age 26

Origin of Students

Germany 20%
Asia 60%
Europe Excl. Germany 20%

Educational Background

Banking & Finance 10%
Business Administration 33%
Economics 17%
International Business & Management 3%
Engineering & IT 20%
Other 13%
Languages & Cultural Sciences 3%

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.

Teaching Methods

The School’s approach to teaching is on student-centered learning. Teaching shall be interactive, fostering collaborative student learning. As the School’s approach to teaching has always been interactive, the methodological focus of the majority of the degree programmes is primarily on classroom teaching, supplemented by online elements.

Students are encouraged to learn from one another through regular group-learning exercises such as group presentations, simulations and business games. All learning is based on considerable instructor/ student interaction. Effective problem-solving is a common focus of all teaching methodologies. Students are exposed to real business situations and simulated future career challenges. Through the use of such exercises, students are able to bridge theory and practice, combining theoretical business concepts with real-world business scenarios. 

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, 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 2023
Early Bird
(EUR 2,000 discount)*
31 Mar 2024
Final Application and Scholarship Deadline 30 Jun 2024
Start of programme 26 Aug 2024

*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 over a video call for all applicants. 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

The Master in Applied Data Science programme begins on 26 August 2024. 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.

Information for FS Bachelor Students

FS Bachelor students who are considering an FS Master's degree as a continuation of their studies are welcome to join our Master Open Campus Events or to contact us directly to discuss their options.

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.

Study Fee

Total fees: EUR 35,750 (including enrolment fees)

Deposit: EUR 3,000 (deducted from the total tuition fees)

The remaining fees will need to be paid in regular instalments at the beginning of each semester. Should you decide to opt for a full upfront payment, you will receive a 5% reduction on tuition fees (excluding discounts).

Please note that the tuition fee's exclude any additional costs i.e. travel expenses, hotel costs for modules abroad.

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.

ProResults logo

Contact

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

Contact:

Andreas Peters

andreas.peters@proresult.de

Contact

Where can you meet us?

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 programmes face-to-face, at one of our Open Campus Nights.

We offer several ways to get in touch with us such as class visits, fairs outside of Frankfurt and personal consultations.

Get more information

The Master in Applied Data Science is accredited by