Data do not make decisions, managers do. However, firms and managers increasingly leverage data to make better decisions. Building on foundations in business knowledge and theories, the course introduces students to strategic, data-driven decision-making for addressing business-related questions. The course starts from the idea that analytics is not primarily about applying technical tools, but about translating managerial problems into meaningful data analytics questions, identifying relevant data, and using appropriate analytical methods to generate insights. Students learn how to identify opportunities and patterns, evaluate assumptions, and improve strategic decision-making. To this end, the course combines conceptual foundations with hands-on applications in statistical software. Students work with business datasets to explore, visualize, and interpret data, develop and test predictions, and apply basic statistical, regression, and classification models. Throughout the course, emphasis is placed not only on analytical techniques, but also on the critical interpretation of results, the limitations of data-driven analyses, and the discussion and communication of insights in a way that supports managerial action.

• Understand basic business problems, develop predictions, and evaluate them using data analysis;
• Develop an understanding of the relevance of data-driven decision-making in organizations;
• Recognize the potential of business analytics approaches for strategic decision-making based on real-world examples, discuss their application, and critically reflect on them;
• Understand, apply, and reflect on the fundamentals of statistics;
• Learn how to visualize and interpret complex data;
• Become familiar with, understand, and analyze regression and classification models as well as model evaluation;
• Apply statistical software (R/Python) to business analytics problems.

This is a core course. Presence is mandatory in all sessions.

The course follows an interactive, application-oriented, and student-centred teaching and learning approach. It combines conceptual foundations, case discussions, and hands-on data analysis. The aim is not only to introduce students to core concepts and methods in strategic business analytics, but also to enable them to apply these concepts to real business problems. To this end, class sessions combine short theoretical inputs with the discussion of assigned readings, practical cases, and guided work with statistical software. Students will learn how to translate managerial questions into analytical problems, work with data, interpret empirical results, and derive implications for strategic decision-making.

A substantial part of the learning process takes place outside the classroom. Students are expected to prepare carefully for each session by reading the assigned materials, working through preparation questions and cases, and familiarizing themselves with relevant datasets or analytical tools in R/Python. Class time is then used to discuss the application of data analysis methods to concrete business problems, managerial relevance and limitations of analytical approaches, and clarify open questions. In selected sessions, students will work individually or in teams on data-based case assignments and presentations. Overall, the course is designed to foster active participation, critical reflection, and the ability to communicate data-driven insights in a clear and decision-oriented way.

1.         Individual preparation and participation                    30%

2.         Group performance on cases                                        40%

3.         Written final exam                                                            30%

            Final Grade                                                                        100%

The following grading scale applies:

Excellent (1)       87.5%-100.0%

Good (2)            75.0% -<87.5%

Satisfactory (3)   62.5% -<75.0%

Sufficient (4)      50.0% -<62.5%

Fail (5)                          <50.0%

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