Digital Transformation

This module opens by examining the current impact and opportunities of the digital era, illustrating how innovations like AI, data analytics, and digital platforms are transforming businesses and markets. At its core, the course focuses on capacity-building, empowering participants to develop and apply entrepreneurial skills in real-life settings. Students learn how to harness data and artificial intelligence to identify new business opportunities and, importantly, to design and innovate sustainable digital business models.  Through hands-on case studies and by applying digital transformation tools, they practice agile implementation for business model innovation processes.

By the end of the module, students will be equipped to lead digital transformation projects confidently, translate emerging trends into actionable business models, understand customer and ecosystem dynamics, and deliver impactful, innovative solutions.

This module covers the data science lifecycle, focusing on data analytics with Python and R, data management, and data processing. It equips students with theoretical foundations and practical skills to apply data science to real-world business challenges.

Data Analytics in R

This course takes students beyond the basics of R programming and introduces advanced techniques for data analysis, modeling, and visualization. Students will learn how to apply analytics to business problems and communicate their findings effectively to non-technical stakeholders. They will work with advanced R syntax, integrate R with other tools and technologies such as APIs, and apply machine learning algorithms to solve complex business problems.

Data Management and Analytics in Python

The course introduces Python for data science, focusing on data wrangling, scalable architectures, and the deployment of data-driven solutions. You will gain hands-on experience throughout the entire data science process - from understanding the problem to obtaining, pre-processing, and exploring data, all the way through to modeling, testing, and deployment. It covers the fundamentals of Python for data science, emphasizing its core features and guiding you in selecting the right tools for each stage of the data science workflow.

Students will also learn how to preprocess and analyze data with Pandas, build machine learning pipelines using sklearn, and access data from APIs and databases, equipping them with the skills to implement data-driven solutions successfully.

This module provides a comprehensive introduction to the key aspects of data science and artificial intelligence, focusing on legal, ethical, and practical challenges. It covers topics like data science foundations, machine learning, and AI-driven solutions, with a particular emphasis on NLP, chatbot development, and data visualization. Students will gain hands-on experience while learning to navigate the ethical and legal considerations in data handling and AI deployment, preparing them for real-world applications.

Legal Foundations of Data Science & AI

The course explores the legal and ethical aspects of data science and artificial intelligence, focusing on governance frameworks that shape data management, AI, and algorithmic decision-making. Students will study ethical guidelines, codes of conduct, and standardization initiatives that promote responsible behavior by data scientists. Additionally, legal frameworks concerning data science and AI are examined. Emphasis is placed on governance techniques, including transparency, explainability, accountability, and control, which are crucial for building trust in AI-driven systems. The module helps students understand the application of these principles in real-world scenarios.

NLP & AI

Natural Language Processing (NLP) and its applications in modern technology are explored in this course. Students will learn foundational NLP techniques such as topic modeling, sentiment analysis, and named entity recognition using R. The course then covers more advanced topics, including text embedding and transformer models, and hands-on experience will be gained through the creation of personalized chatbots using Python, fine-tuning models, and integrating unique datasets. By the end, students will be proficient in performing NLP tasks, building and deploying chatbots, and applying NLP concepts to real-world solutions.

Data Visualization & Storytelling

This course explores the evolution of data visualization practices and emphasizes essential design principles for effective communication. Students will learn how to structure and encode data, create compelling visualizations, and craft data-driven narratives. The course includes practical exercises where students apply these principles to create visualizations and refine their storytelling skills. Group presentations and feedback will help enhance their ability to present data-driven stories effectively.

Data Science Project

Students will have the opportunity to put their learned data science skills to the test by tackling a real-world business challenge. The course begins with identifying a suitable business problem and dataset, followed by pitching the idea to a panel of coaches. Throughout the course, students will apply the data science process - ranging from data cleaning and exploration to modeling and result evaluation. They will also explore digital experimentation and causal inference as part of their analysis. The course culminates in a presentation of the findings to coaches and peers, offering students the chance to practice professional communication.

By the end, students will know how to manage a data science project, apply relevant methodologies, and communicate their findings effectively while considering ethical and legal implications.

Advanced Data Analytics

Focusing on business applications of advanced analytics, this course covers forecasting techniques and decision-making under uncertainty. Students will explore Time Series analysis and Monte Carlo simulations using R and KNIME. Furthermore, the course teaches how to integrate probability into business models and how to use these methods to handle uncertainty in decision-making processes.

As part of the International Immersion this module is devoted to additional, current topics in digital transformation & data science. To ensure that the contents are up-to-date, topics will be changed regularly. Recently, covered topics have included data science approaches in the field of finance & management, Algorithmic (de)biasing and the ethics of AI.

Through case-based learning and competitions, students will apply recent concepts to real-world challenges, gaining practical insights into the evolving digital landscape.

Besides the compulsary modules, participants choose one elective. Topics covered in the various electives include:

• Data Governance

• Organisational Design & Agile Leadership

At the end of the Executive MBA program, students have to write a master's thesis in order to receive the academic degree of Executive Master of Business Administration (EMBA). The intention of the master's thesis is to guarantee that the graduate of the Executive MBA program is able to write scientific papers on his or her own.

Yes, our Executive MBA tuition fees are fully tax deductible for taxpayers in Austria. With our flexible payment modalities, you can spread the fees over several tax periods. However, as tax deductibility can vary greatly from person to person, we recommend that you also contact a tax advisor or inquire directly at the tax office.

If you are taxpayer of another country, please contact your local tax authorities for information on available tax deductions.

To apply for a scholarship, you need to hand in the following additional documents:

• Documentation of income

• Relevant documentation of exceptional circumstances (e.g. supporting dependents, medical care).

Learn more about application deadlines at WU Executive Academy and the process for each scholarship.

Yes, the WU Executive Academy offers a wide range of Executive MBA scholarships and financing options.

Scholarships are awarded on a case-by-case basis. The committee decides whether and to what extent a scholarship will be granted by evaluating your:

• High motivation for joining the program

• Enthusiasm for a challenging learning experience

• Clear understanding of personal contribution to team learning and the overall cohort experience

• Relevant skills and work experience

• Strong record of career progression

• Excellent interpersonal, intercultural, and communication skills

• Passion and commitment to achieve personal growth and success

• Diversity of the student group in terms of academic, professional, and cultural background

• Financial situation

Find additional eligibility criteria for each scholarship.

The Executive MBA Digital Transformation & Data Science is designed for professionals and executives who want to combine their technical and business skills to succeed in the digital economy. It is particularly suitable for:

• Managers, business or data analysts who want to leverage data to solve business challenges.

• IT professionals, engineers, or technical experts aiming for leadership positions or looking to enhance their business knowledge.

• Executives from areas such as controlling, HR, and production who wish to refresh their business and leadership knowledge and develop a better understanding of digital technologies.

• Professionals from social sciences and humanities who want to combine a solid business education with technological expertise.

Our program is designed to be flexible and inclusive of different experience levels. To ensure that all participants can learn effectively, we have developed a three-tiered system that allows students to self-select the level that best suits their prior knowledge and learning goals:

• Basic: This level is ideal for participants with little prior experience (i.e. in coding). It provides the foundational knowledge needed to follow the module content and apply basic concepts. The focus is on building a solid understanding without feeling overwhelmed by complexity. Prior programming knowledge is not required; participants with little or no programming experience will be specifically supported.

  
  

• Advanced: Participants with a strong grasp of theoretical foundations and concepts can engage with more challenging content at this level. The emphasis is on applying advanced methods and deepening expertise to achieve outstanding results in class.

  
  

• Masterclass: Designed for experienced professionals, this level allows participants to transfer their knowledge to new application areas or develop independent projects. It offers a stimulating environment for those who are already experts or enjoy tackling complex problems on their own.

This structure ensures that all participants can engage at a level that matches their skills while continuously expanding their knowledge.

No, programming skills are not required to participate in the Executive MBA Digital Transformation & Data Science. The program is designed for professionals who want to understand the principles of digital transformation and how to leverage data in business processes, without the need for advanced technical expertise. During the program, you will be introduced to the relevant concepts and techniques needed.

Preparation begins well before the first in-person session. At least two weeks before the module starts, pre-assignments and key readings are made available on Moodle, ensuring that participants enter the classroom with a solid foundation. After each module, post-assignments help reinforce and apply the newly acquired knowledge, allowing for deeper reflection and learning. “After the module is before the module”: Once you have completed the post-assignment for one module, you will begin with the pre-assignments for the next module.

Upon successfully completing the Executive MBA Digital Transformation & Data Science, you will be awarded the "Executive Master of Business Administration (EMBA)" title from WU Vienna University of Economics and Business. This qualification confirms your comprehensive skills in digital transformation and data science, preparing you for leadership roles in the digital and data-driven business world.

Our selection process involves careful consideration of the following factors:

• High motivation for joining the program

• Enthusiasm for a challenging learning experience

• Clear understanding of personal contribution to team learning and the overall cohort experience

• Relevant skills and work experience

• Strong record of career progression

• Excellent interpersonal, intercultural, and communication skills

• Passion and commitment to achieve personal growth and success

• Diversity of the student group in terms of academic, professional, and cultural background

Applicants must meet the following formal requirements:

• Undergraduate degree (Bachelor 180 ECTS or equivalent) from a recognized post-secondary institution*

• Minimum of 3 years of postgraduate work experience

• Leadership experience preferred

• Proficiency in written and spoken English

^{* For the Executive MBA programs, outstanding professional experience may allow for application in exceptional cases.}