Master in Data Analytics & Management: part-time

Master in Data Analytics & Management

In Frankfurt or in Hamburg, the Master in Data Analytics & Management empowers managers with the skills and competencies to create purpose and data-driven organisations to lead digital and business transformation.

Managers are required to foster purpose-driven innovation and motivate organisational change through new and disruptive technologies. Our students acquire skills in big data, digital transformation, 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.

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Highlights

Compatible with full-time work
Possibility to begin in March 2022 at our Hamburg campus or in October 2021 at our Frankfurt campus
Block lectures take place during 7 weeks (over 3 semesters)
A combination of technical, strategical and data-driven organisational skills
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
Cooperation with SAP Innovation Center
An extensive network of industry experts and potential employers
Access to our AI Lab at Frankfurt location
New, modern campus and study centres in the centre of Frankfurt and Hamburg with direct and quick access to airport and train connections

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

*Applicants who have completed a professional training and have multiple years of work experience may also be accepted into post-experience master programmes without a first academic degree. The professional training and experience must be related to the intended study programme.

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 and therefore encourage you to complete your application early; there are also financial advantages for candidates who complete their application early.

Frankfurt:
Early Bird Discount (EUR 3,500): 30 April 2021
Final Application Deadline: 15 August 2021

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

On Campus Night

We are once again able to host on-campus events at Frankfurt School at Frankfurt location! We warmly invite you to attend an Open Campus Night, were you can explore the campus, learn about Frankfurt School, and speak to representatives from our master's programmes.

Register here

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.

Register here

Ideal Candidates

The Master in Data Analytics & Management is for professionals who wish to obtain the necessary skill set to successfully manage sustainable business innovation through data-driven strategies. The programme is specifically tailored to the needs of professionals who want to stay fully employed throughout their studies and at the same time, gain expertise to further develop their businesses.

Frankfurt School schedules the programme in a blended-learning format with seven block weeks giving you the possibility to adapt it to fit your full-time position and career aspirations.

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

Locations

The part-time Master in Data Analytics & Management 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.

Factsheet & Timetable

Curriculum

The abundance of data in combination with technologies such as machine learning is a rapidly changing business. Initially companies tend to focus on the technical challenge but often it’s actually the strategical and organisational challenge that turns out to be bigger. The Master in Data Analytics & Management will give you the skills to understand the technical foundations of digital transformation, driving strategic and business model implications and managing the necessary organisational change aiming at a purpose-driven and sustainable business transformation.

We start off by giving you insights into the foundations of key technologies like machine learning and blockchain. Then we focus on how this technological innovation can be translated into business-use cases that can add value to the bottom line. The final third of the programme is dedicated to the human aspect of digital transformation - how to bring key stakeholders on board to lead a sustainable change, which is crucial for the long-term success of digital transformation.

Module 1

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.

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

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

Module 2

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.

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.

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.

Module 3

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.

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.

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.

Module 4

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.

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.

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.

Module 5

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.

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.

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.

Module 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.

Module 7

Ethics

In our final 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.

Law & Data Protection

Law and Data Protection

In our final 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.

Building Digital Strategy

In our final 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.

Master's Thesis

Module 1: Data Bootcamp

Module 2: Machine Learning Bootcamp

Module 3: Disruptive Business Models

Module 4: Business Model Optimisation

Module 5: Organisational Transformation

Module 6: Excursion Digital Transformation & Experiential Learning

Module 7: Digital Strategy

Learning Experience

We believe in a holistic and practical approach, where our students' development is at the heart of every class. Applying a process-based approach, 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.

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.

The first two modules of the Master in Data Analytics & Management include not only a conceptual level, but also a technical implementation of the relevant concepts. If you are interested in digital transformation on a more conceptual level, the Master in Corporate Performance & Restructuring might be a good option for you.

SAP Innovation Center

Thanks to the support of our cooperation partner SAP Innovation Center Potsdam, we can provide our students with the knowledge and skillset in a hands-on approach.

The first five modules are supplemented by an additional experiential learning module in which we cooperate with the SAP Innovation Center. They will supervise different projects on which students work while studying and in block week 6 at the SAP Innovation Center in Postdam, students will present the project outcomes.

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.

Company Visits

Company visits allow students to experience first-hand how digital transformation is implemented and lived. This experiential and reflective learning provides them with the tools needed to tackle challenges in the workplace.

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.

Centre for Human and Machine Intelligence

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.

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.

Network

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.

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 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.

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.

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.

Frankfurt:
Early Bird Discount (EUR 3,500): 30 April 2021
Final Application Deadline: 15 August 2021

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

Apply

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 October 2021 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.

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