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Male student on campus Male student on campus
Degree 60 ECTS
Master of Science (M.Sc.)

Duration
4 Semester | part-time

Tuition Fee
EUR 33,000

HH: EUR 32,500

Application deadline
15 Sept / 1 Feb

Programme Start
Nov / Mar

Language
ENG

Master in Data Analytics & Management

The Master in Data Analytics & Management programme empowers you with the skills and competences to lead data-driven organisations through digital and business transformation.

Managers are required to foster such purpose-driven innovation and motivate organisational change through new and disruptive technologies. You will acquire skills in big data, digital transformation, disruptive/new business models, communication and leadership, which are all required to manage digital transformation in an ever-changing world.

Learning Goals

LG 1 Expert Knowledge and Comprehension of Management and Data Analytics to Drive Innovation and Transformation

Graduates will have an in-depth knowledge and a critical understanding of the key theories, methods and techniques to identify, analyse and implement business and digital transformation processes. They will have a profound knowledge of the parameters and instruments necessary for driving innovation and transformation.

LG 2 Usage and Development of Knowledge of Data-driven Innovation and Processes

Graduates will analyse the business' status quo and design creative solutions for data-driven innovation and business transformation. They will consult businesses professionally and proficiently.

LG 3 Effective Communication and Cooperation

Graduates will be effective communicators and project leaders in practical business contexts. They will write, present, discuss and defend research-based findings in interdisciplinary fields of research. Graduates will contribute to team performance.

LG 4 Professionalism and Self-Image

Graduates lead and support data- and purpose-driven innovative business transformation. They will base their professional activities on in-depth theoretical and methodological knowledge and they will develop these further. They have a thorough and critical understanding of their ethical and legal responsibilities. They will reflect upon their decision-making processes.

Highlights
  • Compatible with full-time work
  • Cooperation with SAP Innovation Center
  • Current trends such as Blockchain, Machine Learning, Data Science and AI
  • Our lecturers are specialised in state-of-the-art scientific knowledge and are experienced in managing digital transformation
  • An extensive network of industry experts and potential employers
  • Access to our AI Lab at Frankfurt location
  • A combination of technical, strategical and data-driven organisational skills
  • New, modern campus and study centres in the centre of Frankfurt and Hamburg 

Requirements

  • A first academic degree* (Bachelor, Diploma or equivalent)
  • Preferably 2 years of relevant work experience
  • Excellent written and spoken English skills** (TOEFL - 90 iBT/IELTS 7.0 or equivalent)
  • Successful participation in our admission interview

If you are currently living abroad, please check visa restrictions for part-time study programmes.

*Applicants without a first academic degree must complete an elibilitiy assessment and demonstrate a level of knowledge comparable to that of a first university degree relevant to the intended course of study.

**Language proficiency waivers are possible for candidates, who have completed a previous degree in English, lived in an English speaking country for longer than a year or whose work language is English (confirmation from an employer required).

Deadlines & Discounts

We admit candidates on a rolling basis however if you submit your application before the Earlybird deadline, you can benefit from our Earlybird Discount.

Hamburg:
Early Bird Discount (EUR 3,500): 1 November 2021
Final Application Deadline: 1 February 2022

Frankfurt:
Early Bird Discount (EUR 3,500): 31 May 2022
Final Application Deadline: 15 September 2022

On campus event

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.

Curriculum

This programme will teach you how to utilise data to transform a business model into a data and purpose-driven organisation. In order to do so, step one is to cover the technical background such as machine learning, IoT, Blockchain, data visualisation and collecting and aggregating data. In a second step, we proceed to translate how such technologies advance and transform a business model as well as to identify the business’ strategic implications, organisational transformation and opportunities. The third step builds on the human aspect of digital transformation: leadership constitutes a vital component in achieving purpose-driven and sustainable change management.

Blockweek 1

IoT

IoT

This module provides insights into the technical foundations of collecting, managing and validating data. It gives you an introduction to IoT sensor technologies and their integration into data-collecting infrastructures. We will work with physical sensors and actuators and programme them to try out these concepts in practice. You will also study big-data infrastructure solutions of the tabular and unstructured types (e.g. Spark), graph databases (Neo4j) and object stores. In the blockchain part, you will learn how blockchains are created through a decentralised database with a consensus algorithm and how to programme a basic smart contract.

If you do not have any experience in Python programming, one of the following two classes will be a pre-requisite for completing this module: coursera or udemy.

Lecturers: Istvan Szukacs & Levente Szabados

Collecting, Aggregating &

Managing Data

Collecting, Aggregating & Managing Data

This module provides insights into the technical foundations of collecting, managing and validating data. It gives you an introduction to IoT sensor technologies and their integration into data-collecting infrastructures. We will work with physical sensors and actuators and programme them to try out these concepts in practice. You will also study big-data infrastructure solutions of the tabular and unstructured types (e.g. Spark), graph databases (Neo4j) and object stores. In the blockchain part, you will learn how blockchains are created through a decentralised database with a consensus algorithm and how to programme a basic smart contract.

If you do not have any experience in Python programming, one of the following two classes will be a pre-requisite for completing this module: coursera or udemy

Lecturers: Istvan Szukacs & Levente Szabados

Blockchain

Blockchain

This module provides insights into the technical foundations of collecting, managing and validating data. It gives you an introduction to IoT sensor technologies and their integration into data-collecting infrastructures. We will work with physical sensors and actuators and programme them to try out these concepts in practice. You will also study big-data infrastructure solutions of the tabular and unstructured types (e.g. Spark), graph databases (Neo4j) and object stores. In the blockchain part, you will learn how blockchains are created through a decentralised database with a consensus algorithm and how to programme a basic smart contract.

If you do not have any experience in Python programming, one of the following two classes will be a pre-requisite for completing this module: coursera or udemy.

Lecturer: Robert Richter

Blockweek 2

Data Visualisation & Data Science

Data Visualisation & Data Science

In this module you will focus on the most fundamental techniques of data acquisition and data cleaning as well as data preparation for exploratory analyses. You will study how to use the tools of the Python ecosystem to create visualisations and thus draw insights from descriptive statistics for business decision-making. You will also learn about the basic tasks of machine learning: clustering, classification and regression methods. We will introduce you to deep neural networks and a high-level summary of convolutional and recurrent network methods. The module finishes with a summary of basic ecosystem elements like Dashboard and API tools for the deployment of machine learning.

If you do not have any experience in Python programming, one of the following two classes will be a pre-requisite for completing this module: coursera or udemy.

Lecturers: Florian Ellsäßer & Levente Szabados / Jan Nagler (Hamburg)

Machine Learning & Deep Learning

Machine Learning & Deep Learning

In this module you will focus on the most fundamental techniques of data acquisition and data cleaning as well as data preparation for exploratory analyses. You will study how to use the tools of the Python ecosystem to create visualisations and thus draw insights from descriptive statistics for business decision-making. You will also learn about the basic tasks of machine learning: clustering, classification and regression methods. We will introduce you to deep neural networks and a high-level summary of convolutional and recurrent network methods. The module finishes with a summary of basic ecosystem elements like Dashboard and API tools for the deployment of machine learning.

If you do not have any experience in Python programming, one of the following two classes will be a pre-requisite for completing this module: coursera or udemy.

Lecturers: Florian Ellsäßer & Levente Szabados / Jan Nagler (Hamburg)

Deploying Machine Learning

Deploying Machine Learning

In this module you will focus on the most fundamental techniques of data acquisition and data cleaning as well as data preparation for exploratory analyses. You will study how to use the tools of the Python ecosystem to create visualisations and thus draw insights from descriptive statistics for business decision-making. You will also learn about the basic tasks of machine learning: clustering, classification and regression methods. We will introduce you to deep neural networks and a high-level summary of convolutional and recurrent network methods. The module finishes with a summary of basic ecosystem elements like Dashboard and API tools for the deployment of machine learning.

If you do not have any experience in Python programming, one of the following two classes will be a pre-requisite for completing this module: coursera or udemy.

Lecturers: Florian Ellsäßer & Levente Szabados / Jan Nagler (Hamburg)

Blockweek 3

New Business Models

New Business Models

The third module builds on the previous 2, covering how the abundance of data and the advancement of algorithms is harnessed to generate competitive advantage. The first part of the module gives you an overview of the new types of business models that are emerging based on data and machine learning and shows how existing business models are affected by digitalisation. In the second part of the module you will have the chance to apply this knowledge to concrete use-cases and derive implications for your organisation. A particular focus will be on how platform economics are becoming increasingly important for the generation of sustainable business models.

Lecturer: Jochen Schlapp

Platform Economics

Platform Economics

The third module builds on the previous 2, covering how the abundance of data and the advancement of algorithms is harnessed to generate competitive advantage. The first part of the module gives you an overview of the new types of business models that are emerging based on data and machine learning and shows how existing business models are affected by digitalisation. In the second part of the module you will have the chance to apply this knowledge to concrete use-cases and derive implications for your organisation. A particular focus will be on how platform economics are becoming increasingly important for the generation of sustainable business models.

Lecturer: Gigi Giustiziero

Innovation Management

Innovation Management

The third module builds on the previous 2, covering how the abundance of data and the advancement of algorithms is harnessed to generate competitive advantage. The first part of the module gives you an overview of the new types of business models that are emerging based on data and machine learning and shows how existing business models are affected by digitalisation. In the second part of the module you will have the chance to apply this knowledge to concrete use-cases and derive implications for your organisation. A particular focus will be on how platform economics are becoming increasingly important for the generation of sustainable business models.

Lecturer: Jochen Schlapp

Blockweek 4

Identifying Opportunities

Identifying Opportunities

This module focuses on using digital strategies to optimise the existing business model. Participants take a closer look at how data and patterns inferred from such data can harness the existing organisational strategy. As part of the module you will identify high-impact use-cases for your company that can be implemented with existing tools and solutions. You also get to know what this implies for how decisions are made on an everyday basis and how strategies are implemented by a new way of distributed and data-driven decision-making throughout the organisation. 

Lecturers: Gergely Szertics & Levente Szabados

Building Organisational Capabilities

Building Organisational Capabilities

This module focuses on using digital strategies to optimise the existing business model. Participants take a closer look at how data and patterns inferred from such data can harness the existing organisational strategy. As part of the module you will identify high-impact use-cases for your company that can be implemented with existing tools and solutions. You also get to know what this implies for how decisions are made on an everyday basis and how strategies are implemented by a new way of distributed and data-driven decision-making throughout the organisation.

Lecturers: Gergely Szertics & Levente Szabados

Agile Methodologies and Design Thinking

Agile Methodologies

This module focuses on using digital strategies to optimise the existing business model. Participants take a closer look at how data and patterns inferred from such data can harness the existing organisational strategy. As part of the module you will identify high-impact use-cases for your company that can be implemented with existing tools and solutions. You also get to know what this implies for how decisions are made on an everyday basis and how strategies are implemented by a new way of distributed and data-driven decision-making throughout the organisation. 

Lecturers: 

Agile Methodologies: Gergely Szertics & Levente Szabados

Design Thinking: Markus Fitza

Blockweek 5

Leading Digital Transformation

Leading Digital Transformation

How can digital transformation be promoted within one's own company and how can management get employees on board? In this module you will learn how to structure an organisation to facilitate data-driven organisational decision-making. You will learn about maturity models, change curves and playbooks (that we use in the AI course) and how to bring on board organisational stakeholders to create sustainable organisational change.

Lecturers: Gergely Szertics & Anna Langfalvy

Purpose & Data-Driven Change Management

Purpose & Data-Driven Change Management

How can digital transformation be promoted within one's own company and how can management get employees on board? In this module you will learn how to structure an organisation to facilitate data-driven organisational decision-making. You will learn about maturity models, change curves and playbooks (that we use in the AI course) and how to bring on board organisational stakeholders to create sustainable organisational change.

Lecturers: Gergely Szertics & Anna Langfalvy

System Thinking

System / Design Thinking

How can digital transformation be promoted within one's own company and how can management get employees on board? In this module you will learn how to structure an organisation to facilitate data-driven organisational decision-making. You will learn about maturity models, change curves and playbooks (that we use in the AI course) and how to bring on board organisational stakeholders to create sustainable organisational change.

Lecturers: Gergely Szertics & Anna Langfalvy

Blockweek 6

Data-Driven Management - Experiential Learning

Excursion Digital Transformation & Experiential Learning

The first five modules will be supplemented by an additional experiential learning module in which we cooperate with the SAP Innovation Center. You will be working on real-word data-driven management problems of a company we cooperate with, or of your own company, if you so choose.

Lecturers: Gergely Szertics, Levente Szabados, Florian Ellsäßer

Choose one Dedicated Elective

Blockweek 7

Digital Strategy

Ethics, Law & Data Protection, Building Digital Strategy

In our Digital Strategy module, you will have the possibility to reassess the knowledge and skillsets from the previous modules. You will critically reflect on what you have learned about digital transformation and data-driven organisations and what this implies on a wider societal level. You will also reflect on how data, organisational goals and organisational transformation can be bound together in a digital business strategy.

Lecturer: Sebastian Köhler

Blockchain in Business

Blockchain Business Models, Emerging Topics in Fintech & Blockchain, Scalability of Blockchain projects

Students are introduced to token economy, blockchain as a service, blockchain-based software products, and peer-to-peer blockchain models. The approaches that are used to meet environmental, social, and governance goals with blockchain are illustrated. The design of CBDCs and various variants for a digital euro or the digital dollar in real-life is touched on. Topics such as interoperability amongst blockchain networks and hybrid blockchains and their implications for business are contained. Further examples that are subject to this module comprise instant cross-border transfers, real-time settlements on stock exchanges, and blockchain as an ‘electronic notary’. Limitations of current blockchain projects and the meaning of scalability for broad adoption, possibilities regarding IoT, and real-life pilot projects are outlined to the students. Future applications of blockchain technology such as identity management and distributed data storage are covered in this module. 

Master's Thesis

Blockweek 1: Data Bootcamp
Blockweek 2: Machine Learning Bootcamp
Blockweek 3: Disruptive Business Models
Blockweek 4: Business Model Optimisation
Blockweek 5: Organisational Transformation
Blockweek 6: Excursion Digital Transformation & Experiential Learning
Blockweek 7: Digital Strategy or Blockchain in Business

Highlights

Study Structure

Thanks to the dual structure, alternating block weeks and self-study, the Master in Data Analytics & Management programme can be optimally combined with your professional activity. After the introductory week, the block modules take place once approximately every nine weeks, from Monday to Saturday at Frankfurt School. Between each intensive block week, you will work alone or in groups on study topics that you can choose to base on your own business, or on real-life cases.

You will then complete your studies with your six-month Master's Thesis and a final conference. Our lecturers and professors accompany you during both phases of study, providing guidance, advice and recommendations throughout the whole programme.

SAP Innovation Center

Thanks to the support of our cooperation partner SAP Innovation Center Potsdam, we can provide you with the knowledge and skillset in a hands-on approach. SAP will provide you with business intelligence solutions (data intelligence and analytics cloud) in modules I and II as well as with a business challenge supporting modules I through V. During module IV, you will be able to present the results of your work.

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The Blockchain Center

The Frankfurt School Blockchain Center is a think tank and research centre which investigates implications of blockchain technology, crypto assets and distributed ledger technology (DLT) for companies and their business models. Besides the development of prototypes, it serves as a platform for managers, startups, technology and industry experts to share knowledge and best practices. 

Find out more about our Blockchain Center here.

The Centre for Human and Machine Intelligence (HMI) 

The Centre for Human and Machine Intelligence (HMI) conducts basic and applied research at the intersection of Artificial Intelligence & Machine Learning, Decision & Social Science and Finance & Management. Our focus is to develop and advance our understanding of computational systems that augment human capabilities for judgment and decision-making.

Find out more about HMI here.

The AI Lab

The AI Lab at our Frankfurt location provides a space where new learning concepts can be developed, tested, and immediately implemented into the teaching programme. The AI Lab is equipped with four high-end computers using the latest GPUs for AI acceleration. Students are also invited to attend our Hackathons and additional workshops. 

Find out more about our AI Lab here.

Locations

The part-time Master in Data Analytics & Management programme is available in Frankfurt and Hamburg - one programme, one network, same curriculum. The schedule is designed to allow candidates to choose the location that bests suits their needs.

MDAM Location

Important Dates

Early Bird Deadline: 31 May
Final Application Deadline: 15 September

Programme Start: November 2022
Total Tuition Fee: 33,000 EUR

Important Dates

Early Bird Deadline: 1 November
Final Application Deadline: 1 Feb

Programme Start: March 2022
Total Tuition Fee: 32,500 EUR

Learning Experience

Studying at FS

Whilst you will receive a strong theoretical foundation, the emphasis of the programme will be on the practical application of methods and theory. As such, a significant proportion of the modules involves teaching by practitioners, who have everyday experience with data-driven decision-making and digital transformation. Students will advance in critical thinking and problem-solving strategies, which in return enable them to apply the knowledge, skills, competences and methods for their businesses. 

FS Alumni Community

As a student of our part-time degree programmes, all the Frankfurt School Alumni Network is available to you. Take advantage of the numerous advantages of the FS Alumni & Friends Network, exchange ideas and expand your professional and private contacts. We offer you numerous events and platforms for this purpose, such as workshops, lectures, continuing education programmes and numerous events.

Continuous Learning

Throughout your professional career, you will continue to develop personally and professionally. We would like to accompany you in this process even after you have completed your studies. As alumni, we invite you to participate in one module of your degree programme per year. That way, you can regularly refresh and expand your knowledge and skills.

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.

Talk series

Class profile 2021

Overview

Number of students 23
Nationalities 13
Average age 25

Industry Background

Financial Industry 26%
Information & Technology 17%
Industrial Products & Services 17%
Manufacturing 13%
Consultancy 27%

Gender

Female 26%
Male 74%

Academic Background

Business Administration & Management 44%
Economics 22%
Engineering, IT and Computer Science 17%
Other 17%

Network & Career

Career Growth Opportunities

Students participating in this programme aim at deepening and advancing their already sophisticated careers to the next level by adding innovative and revolutionary components, competences and methodology as well as objectives for their businesses. Our graduates are qualified to lead in areas such as IT- Consulting, Data-driven Management, Purpose-driven Management, digital transformation, business analytics, data analytics, change management, etc.

Career opportunities MDAM

Connect with your Peers

Our students typically come from consulting and industry with an extensive range of practical and educational backgrounds which will open up your network to new areas. During the intensive block weeks you'll get the chance to exchange thoughts and learn from each other’s know-how and practical experience.

Connect with our Corporate partners

Additionally, our exclusive corporate and alumni connections allow you to strengthen your career network in a wide range of industries while studying. Our Frankfurt School Alumni Network brings together a large number of national and international executives at various events and workshops.

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

  • CV or resume
  • Certified copy of your undergraduate transcript of records and degree award certificate
  • Proof of English Skills, TOEFL - 90 iBT, 577 ITP / IELTS 7.0 or equivalent

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

If you are currently living abroad, please check visa restrictions for part-time study programmes.

Deadlines & Discounts

We admit candidates on a rolling basis and therefore encourage you to complete your application early; there are also financial advantages for candidates who complete their application early.

Hamburg:
Early Bird Discount (EUR 3,500): 1 November 2021
Final Application Deadline: 1 February 2022

Frankfurt:
Early Bird Discount (EUR 3,500): 31 May 2022
Final Application Deadline: 15 September 2022

2. Interview

Successful applicants will be invited to an interview. The interview will be held either at Frankfurt School, or through a video call or phone call. The purpose of the interview is to gain a better understanding of your professional and academic experience and discuss your motivations for the programme and goals for the future.

3. Results

You will receive a final decision on your application shortly after. The decision is made by our Admission Committee and is based on your application and interview performance.

4. Programme Start

The first block week of the programme starts in November 2022 for our Frankfurt location and in March 2022 for our Hamburg location.

We look forward to welcoming you at your campus!

Financing

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