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Degree
Master of Science (M.Sc.)

Language
EN

Tuition Fee
32,500 Euro

Application deadline
30 June

Programme Start
26 August

Duration
4 Semester | full-time

Master in Applied Data Science

The MSc in Applied Data Science is a response to the recent demand for business savvy data scientists with the collaborative skills to match. Our students will gain hands-on experience in solving real-world data science problems with our prominent industry partners, including Commerzbank and PwC, and will be equipped with the technical skills, business domain knowledge, and critical judgment to navigate the modern data ecosystem.

Highlights

  • A combination of applied Machine Learning, Data Science and Business
    Problem Solving
  • Ethical ramifications of the fourth wave of industrialisation
  • Extended co-op company projects in cooperation with leading companies throughout third and fourth semesters
  • Work part-time, study full-time with the 3-Day Model
  • Participate in our hackathons 

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:

30 November  4,000 Euro early-bird tuition discount
31 March 2,000 Euro early-bird tuition discount
30 June Final Application and Scholarship deadline

Requirements

  • Bachelor’s degree or equivalent

  • Excellent written and spoken English skills (Minimum TOEFL iBT 90, IELTS 7.0, or equivalent)
  • GMAT, GRE, or Frankfurt School Admission Test (Applicants holding a BSc degree with a focus on mathematics, statistics, or computation are exempt)
  • Successful participation in our admission interview



Master Info Evening

Learn more about our Master programmes at one of our Master Info Evenings! Benefit from personal conversations with our students and programme directors and find out which programme could help you excel in your career. 

GMAT Preparation Course

The GMAT is a challenging exam requiring substantial preparation for optimal performance. This course provides students with all the essential tips and necessary preparation for high performance and excellent exam results. 

Curriculum

Our students will master core data science and machine learning concepts, as well as the art and science of problem decomposition and solving. They will be able to identify business needs and wants, as well as problems, and propose relevant solutions using machine learning tools and by applying sophisticated statistical techniques. To do this, they will collect, transform and visualize data, create data models, as well as run predictions and simulations.

Our Master in Applied Data Science graduates will have expert knowledge in data science, be able to analyse, structure and evaluate complex economic problems, and be able to conceptualise and develop data science products and solutions. They will be effective and responsible team members and data science professionals, who recognise and respect the personal and cultural differences in an international environment.

In a nutshell: The Frankfurt School of Finance & Management Master in Applied Data Science Programme provides the skills required to recognise and meet the data science wants of contemporary business, across-function and with an understanding of the connected ethical ramifications.

Semester 1

Quantitative Fundamentals

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

Computational Semantics – Data Structures & Algorithms

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.

Intro to Data Analytics in Business

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.

Organisational Strategy

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.

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.

Semester 2

Guided studies in Financial Management

New module added for 2019 intake – more information to follow.

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).

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 should have developed and strengthened their analytic and critical skills, as well as their ability to apply those skills to ethical and legal problems to develop solutions to those problems.

Managing, storage and visualizing big data

This module is an introduction to database fundamentals, the Structured Query Language (SQL) data base, and data visualisation. Students will learn how to create a basic database structure, manipulate and extract data, and perform exploratory data analysis and visualisation.

Deep learning

This module covers deep neural networks, which are currently the “workhorse” of machine learning and most commonly used methods. Students will learn how to understand the theoretical background necessary to employ deep neural networks to solve image recognition and language processing. Students will also solve a practical machine learning problem using deep learning methods.

Semester 3

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.

AI: The New Frontier

This module focuses on advanced and current topics in AI and its industrial application. You will gain a deep understanding of current topics in AI, comparable to a graduate computer science seminar.

Text Mining & NLP

With a combination of theoretical and practical approaches, this module aims to introduce general machine learning techniques that can deal with time series and show how they can be effectively applied to give computers language understanding. Through a hands-on approach, you will learn how to apply machine learning techniques to gain language understanding and combine these techniques with domain specific applications such as word embedding, semantic distance and dependency tree parsing.

Academic Writing

This module prepares students with the necessary writing skills for their master thesis. Students will analyse academic text by identifying and underlining the main arguments, summarising and paraphrasing selected parts and detecting weaknesses or strengths in the line of argumentation. Furthermore, you will write an argumentative essay by demonstrating logical development of ideas, quoting directly or indirectly, synthesising and evaluating a significant number of sources convincingly, and by using academic vocabulary and phraseology.

Cooperation Company Project

This module is a practical project conducted with a partner company which allows you to apply the skills you have learned during other semesters. Students will work in groups of 3-4 on small, current data science projects within the company. A key aspect is that you will work on a project from start to finish, thus gaining end-to-end, hands-on experience to better prepare you to enter the job market.

Semester 4

Electives 1 and 2

A range of electives including Business Modelling and Simulation, and Human and Machine Predictions allow you to tailor your Master in Applied Data Science through a diverse and distinctive structure of time formats and block weeks. 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 at the end of third semester and students must choose 2 elective modules to study in their last semester.

You can find the entire list of electives that we offer for the upcoming 2019 summer semester here.

Study abroad or electives

All Master in Applied Data Science students can go abroad during the fourth semester. You have the option to study at one of our international partner universities and use the credits gained during the semester abroad to replace the electives.  

You can find the entire list of electives that we offer for 2019 here.

Thesis

You are required to conduct independent research in order to complete your master 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.

Study Abroad

Go abroad, meet new people, experience a new culture, and broaden your perspective.
Complete the 4th semester at one of our 80 partner universities worldwide or set up a self-organised semester abroad at a non-partner university.

Be fully immersed into a new environment & prepare yourself for the globalised business world.

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 through exposing you to every day working life as part of our cooperative company projects. 

AI Lab

Most of the Master in Applied Data Science classes take place in the AI Lab. The AI Lab provides a space where new learning concepts can be developed, tested, and immediately implemented into the teaching programme. There is room for creativity and experimentation with new formats and methods of working. Students are also invited to attend our Hackthons and additional workshops. 

Find out more about our AI Lab here.

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

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

Guest Lectures, Talks and Workshops

Frankfurt School’s close ties to the consulting and finance industry are evident in the guest lecture opportunities. Our guest lectures by external professionals with current knowledge of the market bring relevant and valuable cases to be solved in the classroom. Students can network with innovators and industry veterans regularly from their own university.

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 many of their modules. Students will especially go deeper into how to solve real-life data problems with Machine Learning in Machine Learning 1: Business Use Cases 1 and Machine Learning 2: Business Use Cases 2. This allows students to work on real-life case studies preparing you for the realities of the working world. 

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.

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.

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

The next Hackathon is the Explainable AI Hackathon with Accenture on 26 April. 

Careers

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 digital transformation.

We offer you the opportunity to work part-time throughout your studies. Proresult is a financial service consulting company who employs students with a background or interest in financial consulting and C1 level German skills. 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.

If you are interested please apply within the online application.

Career Services & Your FS Network

Our exclusive corporate connections allow you to build a strong network for your career. Our Careers Services team are readily 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, leaves you in the spotlight for employment after graduation.

#FSMaster

Student Life

Join our community of aspiring and inspiring individuals and get involved at FS by joining a student initiative, participating in a sport or business competition, by serving on our Student Council, or by creating your own event or initiative.

Class Profile

Overview

Number of students 15
Nationalities 8
Average age 25

Origin of Students

Germany 40%
China 13%
India 13%
USA 7%
Hong Kong 7%
Libya 7%
Philippines 7%
Taiwan 7%

Gender

Female 60%
Male 40%

Educational Background

Business Administration 27%
Computer Science/Engineering 20%
Economics/Finance 13%
Marketing 13%
Linguistics/Languages 13%
Other 13%

Application Process

1. Online Application

The first step in applying for our programme is the completion of our online application, which includes uploading the required documents in support of your application:

Required Documents

  • Certified copy of your undergraduate transcript of records and degree award certificate
  • Certified copy of your TOEFL / IELTS results or equivalent (TOEFL IBT minimum score of 90 / IELTS minimum score of 7,0) or TOEFL ITP (minimum score of 577)
  • If needed: Official GMAT score report, GRE score report or FS Admissions Test
  • CV or resume (must be in English)
  • Recommendation letter
  • 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.

Winter Semester Start

The final deadline for applications is June 30th.

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 4000 EUR and 2000 EUR respectively.  Applicants interested in a scholarship must complete the relevant section in our online application and submit it by the end of June.

30 November Deadline for early-bird discount of 4000 EUR
31st March Deadline for early-bird discount of 2000 EUR
30th June Final deadline for all applications. 
30th June Final deadline to be considered for FS Scholarships.
September Start of programme

2. Interview

After reviewing your application documents, we will invite qualified candidates to participate in a personal interview with a faculty member of Frankfurt School of Finance and Management. The interview will be held either at Frankfurt School, or over the phone for students that are not able to make an in-person interview. The purpose of the interview is to gain a better understanding of your character, personality, expectations, motivations and goals.

3. Programme Entry Invitation

We adopt a holistic approach in assessing our candidates, and the final decision regarding your admission will be based on a combination of undergraduate grades and/ or admissions test, English language abilities and the interview results. At this point, successful candidates will be invited to enter the programme by completing a diagnostic that maps out their knowledge spaces. These are used throughout the programme to give students continuous feedback on their progress.

Mehr Weniger

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!

Study Model

3-day Model Overview

3-day Model Overview

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.

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.

As we guarantee the quality of our teaching and research, we also expect the highest levels of commitment and motivation from our students.

Women in STEM Scholarship

All female applicants are able to apply for our Women in STEM (Science, Technology, Engineering and Mathematics) Scholarship. The scholarship encourages and supports women in fields underrepresented by women. Those who wish to apply can do so with a motivational letter within the application.