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Master in Applied Data Science
120 ECTS
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.
The programme provides a combination of quantitative research and analytical skills. As knowledgeable data scientists, our students/graduates will be able to apply computational skills to contemporary business problems and navigate the modern data ecosystem.
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.
Highlights
- 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
- 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
- Possibility to apply for a scholarship
Requirements
- 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, 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 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 I (EUR 4,000 discount)* |
30 Nov 2021 |
| Early Bird II (EUR 2,000 discount)** |
31 Mar 2022 |
| Final Application and Scholarship Deadline | 30 Jun 2022 |
*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
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".
Our Master in Applied Data Science curriculum is based on four pillars. Pillar one deals with the technology of data science: machine learning, algorithms, deep learning and cloud computing to name a few. During the second pillar, students get to see how data science influences the business world in terms of processes. Ethical and legal ramifications of data science/AI constitute the third pillar. Students and graduates will understand the societal implications of data science and AI. The last pillar is the application in the business landscape.
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
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
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
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
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
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
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
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
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
AI & Humanity: 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
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
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
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
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
2 Electives or Entrepreneurship or Study Abroad
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 module.
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.
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 programmes face-to-face, at one of our Open Campus Nights.
Data Science Talk Series
Our Data Science Talk Series is a great opportunity to interact with professionals from industries and academia. Exchange ideas and learn more about topics such as AI, Machine Learning, Cyber Security, Visualization, Deep Learning to name a few.
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!
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.
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.
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 to complete two electives, 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 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.
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.
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.
Hackathons and Competitions
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
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
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.
Class Profile
Overview
| Number of students | 39 |
| Nationalities | 14 |
| Average age | 25 |
Origin of Students
Gender
Educational Background
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.
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 2021 |
| Early Bird (EUR 2,000 discount)* |
31 Mar 2022 |
| Final Application and Scholarship Deadline | 30 Jun 2022 |
| Start of programme | 22 Aug 2022 |
*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.
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.
Contact
You can contact Proresult directly if you would like to know more about the position.
Contact:
Raoul Stirkat
Contact
Gabrielle Crossley
Recruitment Officer
Eleanor Stephen
Head of Masters Recruitment and Admissions
Get more information
Where can you meet us?
We offer several 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!
