Master in Applied Data Science

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.

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.

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.

This module is an introduction to database concepts and the Structured Query Language (SQL) data base. Students will learn how to create a basic database structure, manipulate and extract data on real databases and datasets. This module serves a foundation module for Visualising Big Data.

External Lecturer

Thomas Mick

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.

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

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).

This module teaches students to use data visualisation and business intelligence to support marketing decision making in a variety of enterprises. Students will learn how to visualise, present data and understand how to tell a story using data.

External Lecturer

Kerem Tomak

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.

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.

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.

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.

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.

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.

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.

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 2019 here.

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 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.

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.

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.

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.

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. 

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.

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. 

St. Gallen Business Game

Hec Business Game

• 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)
• 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.

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.