- Prof. Dr. Markus Böhm, University of Applied Sciences Landshut
IT-enabled Business Model Innovation: 12th- 13th September 2023
What to expect
- IT Strategy and value of IT
- Foundations of Digital Transformation
- IT-enabled Business Model Innovation
- Implementation of Digital Transformation Initiatives
- Skills for Digital Transformation
Short course description
This course introduces the foundations, potentials and challenges of Digital Transformation. It equips the participants with the required knowledge on IT Strategy to plan, evaluate and implement IT-enabled Business Model Innovations at incumbent organizations.
IT-enabled Business Model Innovation
Managing Technochange: 14th- 15th September 2023
What to expect
- Implementing information system in organisations
- Effecting organisational change
- Agile Transformation
- Leveraging IT for change and digital business
Short course description
The role of IT becomes increasingly important for the corporate strategy of companies. However, this importance depends on the information systems ability to deliver value and their implementation drives organisational performance. Therefore, it is necessary that companies’ information is meaningful and useful to decision making.
- Prof. Dr. Philippe Cudré-Mauroux, University of Fribourg
- Prof. Dr. Elena Mugellini, University of Applied Sciences Fribourg
Big Data and Interactive Systems: 19th- 20th September 2023
What to expect
- Big Data (concept, market, tools) and their use in AI applications
- Where could big data for data science in smart cities come from?
– IoT-Services, social media, smart city architecture, etc.
– What can we do with this data?
– Human-centered AI for cities and business models
– How to apply Soft Computing Methods in this Framework?
Short course description
From business intelligence and big data analytics to application to improve products and services. How can the daily-flood of data be made understandable and usable for successful business decisions? Drowning in data, a company in the digital era must know how to handle them but also how to use them in their strategy.
- Amir Tabakovic – Strategist, Innovator, Investor in ML Technology
• Chair of Expert Group Data Privacy and AI at Mobey Forum
• Guest lecturer at ESADE
Introduction to Business Analytics and AI for Managers: 21st- 22nd September 2023
What to expect
- Introduction to Artificial Intelligence and Machine Learning (AI/ML)
- Data driven organisation and democratisation of AI/ML
- ML problem framing and ML Canvas
- Practical examples of Supervised and Unsupervised ML problems
- Practical exercises: Pricing, Shopping Basket Analysis, Sentiment Analysis…
- AI/ML and Regulations; Consequences of GDPR on AI/ML; Data Privacy and AI
Short course description
This course offers a practical introduction to Artificial Intelligence and Machine Learning (AI/ML) for non-technical business leaders. It helps business domain experts better assess the potential of AI/ML for their businesses, avoid common pitfalls when implementing AI/ML in their organisations and initiate and execute successful data driven projects.
Data Analytics & Machine Learning for Managers: 7th- 8th November 2023
What to expect
- Non-technical but informative introduction to data analytics and machine learning for data driven
analysis of business processes (like customer behavior, production, turnover…) - Data visualization and descriptive statistics (e.g. the average or variability of prices)
- Regression: analyzing associations between business factors (like marketing and sales)
- Intuition of machine learning for optimally forecasting future business outcomes (e.g.sales),
based on information in past data (e.g. price, quality, competition) - Important concepts of machine learning: alternative algorithms (e.g. decision trees,
random forests, lasso, boosting…), performance assessment, and tuning of algorithms - Business cases and practical examples with real data using the no-code software “BigML”
Short course description
Understanding the diverse tools and objectives of data analytics and machine learning is key for proficiently analyzing data within companies and organizations. This course offers an introduction to essential statistical and machine learning techniques, enabling precise evaluations of the business environment and informed estimations and forecasts of businessrelevant factors, such as key performance indicators.
Competition: 14th- 15th November 2023
What to expect
- Economic fundamentals of competition
■ Definition of the relevant markets
■ Economic basis of competition policy
- Firms behaviors fundamentals affecting competition
■ Horizontal agreements
■ Vertical restraints
■ Abuse of dominant position
■ Mergers and acquisitions
- Competition fundamentals in the digital economy
■ Competition patterns in digital networks and platforms (eg. Google)
■ Disruptive new market entrants of the sharing economy (eg. Uber, Airbnb)
Short course description
The course on Competition highlights the main economics basis of competition behaviors and developments with a strong emphasis on the Digital Economy features such as the price setting algorithms or the impact of the sharing economy on the markets (Uber, Airbnb, etc.). The course scrutinizes the competition patterns in digital markets as well as the competition patterns in digital networks and platforms. Participants will have to deal in class with real and current competition cases such as Apple Pay case and Google on line advertising case.
Impact Evaluation for Managers: 28th- 29th November 2023
What to expect
- Non-technical, but informative introduction to impact evaluation for assessing the effect of interventions (e.g. discounts) on business outcomes (e.g. sales) for decision support
- Different evaluation designs: 1) experiments (A/B testing); 2) “instrumental variable” designs for fixing “broken” experiments; 3) “selection-on-observables” designs based on groups with and without intervention that are similar in observed characteristics; 4) “difference-in-differences” designs based on groups with comparable time trends
of business outcomes; 5) “regression discontinuity” designs based on indices e.g. customer score) which determine the receipt of an intervention (e.g. fidelity card) - Machine learning-based impact evaluation for detecting and optimally targeting customer segments for which interventions are particularly effective (e.g. loyal customers)
- Business cases and practical examples with real data using graphical interfaces in web applications or the no-code software “BigML” – no programming required!
Short course description
For effective decision making, it is crucial to evaluate the consequences or impact of specific actions or policies, whether it’s the impact of pricing strategies on sales or of employee training on productivity. This course provides an introduction to cutting-edge data-driven impact evaluation
methods—a critical tool for supporting decision-making within companies and organizations.