Master in Data Analytics & Management

Program Overview

The Master of Science in Data Analytics & Management program empowers professionals with the skills to lead data-driven organizations through digital and business transformation. Through block weeks and self-study, students gain expertise in big data, disruptive business models, and leadership, enabling them to drive innovation and manage change in an ever-evolving world. Graduates are equipped to excel in roles such as IT consulting, data-driven management, and digital transformation.

Program Outline

Degree Overview:

• Master of Science (MSc)
• 60 ECTS
• Duration:
• 4 Semesters | PART-TIME
• On-campus participation is mandatory
• Tuition Fee:
EUR 35,500
• excl.
250 EUR enrolment fee
• Application deadline:
15 September
• Program Start:
28 October
• Language:
ENG The Master in Data Analytics & Management programme empowers you with the skills and competencies to lead data-driven organisations through digital and business transformation. Managers are required to foster such purpose-driven innovation and motivate organizational change through new and disruptive technologies. You will acquire skills in big data, digital transformation, disruptive
ew 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.

Outline:

The 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.

• Block week 1
• Data Bootcamp:
• Provides insights into the technical foundations of collecting, managing and validating data.
• Introduction to IoT sensor technologies and their integration into data-collecting infrastructures.
• Study of big-data infrastructure solutions of the tabular and unstructured types (e.g.
Spark), graph databases (Neo4j) and object stores.
• Learn how to create blockchains through a decentralized database with a consensus algorithm and how to programme a basic smart contract.
• Block week 2
• Machine Learning Bootcamp:
• Focus on the most fundamental techniques of data acquisition and data cleaning as well as data preparation for exploratory analyses.
• Learn how to use the tools of the Python ecosystem to create visualisations and thus draw insights from descriptive statistics for business decision-making.
• Introduction to the basic tasks of machine learning: clustering, classification and regression methods.
• Overview of deep neural networks and a high-level summary of convolutional and recurrent network methods.
• Block week 3
• Disruptive Business Models:
• Overview of the new types of business models that are emerging based on data and machine learning and how existing business models are affected by digitalisation.
• Application of this knowledge to concrete use-cases and derive implications for your organisation.
• Focus on how platform economics are becoming increasingly important for the generation of sustainable business models.
• Block week 4
• Business Model Optimisation:
• Use of digital strategies to optimise the existing business model.
• Examination of how data and patterns inferred from such data can harness the existing organisational strategy.
• Identification of high-impact use-cases for your company that can be implemented with existing tools and solutions.
• Exploration of 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.
• Block week 5
• Organisational Transformation:
• How digital transformation can be promoted within one's own company and how management can get employees on board.
• Learning how to structure an organisation to facilitate data-driven organisational decision-making.
• Study of maturity models, change curves and playbooks and how to bring on board organisational stakeholders to create sustainable organisational change.
• Block week 6
• Experiential Learning:
• Work on real-world data-driven management problems of a company we cooperate with, or of your own company, if you so choose.
• Combine, synthesize and apply the previous modules of the Master in Data Analytics and Management programme.
• Put learned skills to practice by utilising data to solve a real-world business problem in a partnering organisation.
• Work in teams with this organisation in order to utilise their data to derive concrete business implications.
• Block week 7
• Choose one Dedicated Elective
• Digital Strategy
• Blockchain

Teaching:

• The dual structure, alternating block weeks and self-study, can be optimally combined with your professional activity.
• The block modules take place once approximately every nine weeks, from Monday to Saturday at Frankfurt School.
• 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.
• Lecturers and professors accompany you during both phases of study, providing guidance, advice and recommendations throughout the whole programme.

Careers:

• 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.

Tuition Fee EUR 35,500

• excl.
250 EUR enrolment fee