Applied Data Science & Artificial Intelligence
Today’s executives and management teams are facing all new challenges in decision-making processes, and it is becoming fundamental to have an understanding of artificial intelligence and big data in order to make informed business decisions. Data driven business models are creating competitive advantages for those who know how to harness the potential of these tools. A wave of innovation in these areas have created an opportunity for companies to truly disrupt the market – provided they know how to efficiently apply these concepts in practice.
"It is important today that as many people as possible learn to use these technologies - both on a technological and ethical level. AI technologies do not simply emerge, they are developed and shaped by humans. They already make our lives easier in many ways and offer countless opportunities. We are therefore firmly convinced that a large and diverse number of people should learn to use this technology consciously. If we succeed in doing so, many positive things will emerge from these technologies for the benefit of all. This can be compared with major international projects such as the International Space Station ISS, where the global community comes together and fosters progress."
The Applied Data Science & AI program has been specially developed by Goethe Business School in cooperation with Tech Quartier, to address executives, managers, team leads and decision makers who are looking to better understand Data Science & AI applications for their organizations. The program aims to develop not only a knowledge and understanding of data science and AI and how they are transforming business today, but also the ability to apply various tools and methods hands-on, ensuring you are capable of more than just thinking about data science - you are capable of executing, together with your team, real solutions. Participants will be able to develop scalable and effective data-driven organisations and be capable of implementing the latest models in practice.
The program takes place over 3 modules in a blended learning format, meaning some modules are online while others take place in-person. The program is challenge driven, combining technical skills with a practical, business focus to be transferred to your own organization through a challenge project completed during the modules.
The program begins with a non-technical introduction to Data Science and AI. A focus is laid on understanding why these topics are relevant for management in every field, and critical for executives to understand in order to effectively implement within organizations.
- Statistics & Analytics for business: Moving from traditional statistics to prediction with machine learning
- Data Science/AI development framework and workflow
- How machines (algorithms) learn from experience (data): types and examples
- Big Data: What does it really mean?
- Relationship between AI/ML technologies, AI/ML capabilities and business applications (across functional areas and industries)
- AI-based business models
- Developing and validating business cases (A/B testing)
- Forms of Human & AI collaboration in business
- AI ethics: The limits of AI/Machine Learning
Participants recieve their challenge assignment at the end of this module.
In this module, participants expand on their basic understanding of data science and AI developed in module 1, and focus on learning hands-on applications to machine learning. By developing a deeper technical understanding of the tools used, participants will be able to develop and apply solutions independently. An introduction to Python, R, and data visualization tools are presented using real data sets.
- Data Science/AI toolbox and technologies
- Data handling: Finding, collecting, modelling and storing data
- Evaluating data science / machine learning algorithms for decision-making
- Visualization, representation and interpretation: Data Storytelling
The final module brings the individual aspects of previous modules together and considers implementation within organizations. Scaling up to create a data-driven organization is the focus, with ethical, logistical, and legal considerations for your company brought into context.
- Developing operating models and scalable organizational structures
- How to organize for analytics (ecosystem organization)
- Data protection, data storage, and building analytical infrastructures (cloud vs. on-premise options; DSGVO)
Prof. Dr. Alexander Benlian is the Director of the Endowed Chair for Information Systems & E-Services at TU Darmstadt. He received his PhD at the Ludwig-Maximilian-University in Munich and worked as an Assistant Professor. He is fascinated by paradigm-shifting phenomena emerging in and triggered through digital channels. More generally, he is interested in the fields of entrepreneurship, service research, behavioral economics and psychology. His research has been published in leading academic and practitioner-oriented journals, like the Journal of Management Information Systems and MIS
Dr. Thomas Funke has been involved in the global start-up ecosystem for 15 years now. In addition to setting up a research studio at the WU Vienna, which deals with tools and methods of the startup world, he founded different startups and still lectures at universities. He is especially motivated by seeing how starting a business can release new forces in young people that lead to a strengthened self-efficacy. Thomas Funke is particularly fascinated by training and educating young entrepreneurs. Furthermore, he loves measuring and decoding the different entrepreneurship ecosystems.