30587 - DATA-DRIVEN ANALYSIS AND DECISION-MAKING IN BUSINESS
Department of Management and Technology
Course taught in English
Go to class group/s: 31
CLEAM (6 credits - II sem. - OP | SECS-P/06) - CLEF (6 credits - II sem. - OP | SECS-P/06) - CLEACC (6 credits - II sem. - OP | SECS-P/06) - BESS-CLES (6 credits - II sem. - OP | SECS-P/06) - WBB (6 credits - II sem. - OP | SECS-P/06) - BIEF (6 credits - II sem. - OP | SECS-P/06) - BIEM (6 credits - II sem. - OP | SECS-P/06) - BIG (6 credits - II sem. - OP | SECS-P/06) - BEMACS (6 credits - II sem. - OP | SECS-P/06) - BAI (6 credits - II sem. - OP | SECS-P/06)
Course Director:
FELIX POEGE
FELIX POEGE
Mission & Content Summary
MISSION
We live in an era of business uncertainty: adopting a more rational approach to decision-making and integrating a data-driven analysis in firms' decision-making processes can have profound effects. In this light, the course provides an overview of the process of using data to inform firms' decision-making process and validate a course of action before committing to it.
This course aims to improve the student's ability to make decisions in business by leveraging a structured approach based on theorizing about possible future scenarios and implementing data-driven actions. The first part focuses on theoretical aspects of decision-making, while the second part focuses on data-driven analysis and interpretation of data from a business point of view. Through concrete and practical applications, students will learn how to diagnose business problems, offer appropriate solutions, and generate innovative opportunities.
CONTENT SUMMARY
The course focuses on the best practices that a firm can adopt to make rational decisions, namely:
- A structured course of action to make more rational decisions
- A language to describe decisions and distinguish strategies, scenarios, and outcomes
- Models and statistical techniques:
- Structured descriptive statistics
- Linear regression model
- Applications and real cases using statistical software (Stata)
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Identify the main sources of uncertainty and depict a well-structured approach to decision-making
- Learn how to identify future scenarios and update the beliefs attached to them
- Learn how to use and apply data in realistic business contexts
- Improve their ability to diagnose business problems, offer appropriate solutions, and generate innovative opportunities.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Adopt a more structured approach to identify the sources of uncertainty, imagine different actions to be implemented under possible future scenarios
- Assess what data would be useful to make a more informed decision
- Adopt a proper language to describe decisions and distinguish strategies, scenarios, and outcomes
- Analyze data in business contexts approaches for choosing appropriate metrics and analytical methods
- Derive business insights and predictions from data, elaborate and interpret data to inform quality decision-making
Teaching methods
- Collaborative Works / Assignments
- Interaction/Gamification
DETAILS
The learning experience of this course includes:
- Face-to-face lectures aimed at deepening the understanding of the theoretical foundation behind rational decision-making and mastering basic data analysis techniques;
- Case study discussions concerning concrete examples of decision problems in companies facing uncertainty. Interactive class activities will be adopted to deepen the understanding of different options and foster class participation;
- Group assignments that allow students to confront real business decisions and identify the best practices to deal with them.
Assessment methods
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ATTENDING STUDENTS
The assessment for attending students will be based on three main components:
- In-class participation (10% of the final grade) aimed at testing the student's ability to interact in a multicultural environment and to think critically through contributions given to the class discussion.
- Group assignment (40% of the final grade) designed for the purpose of verifying the student's ability to:
- Analyse decision problems and identify the main elements, namely the possible strategies, outcomes, and scenarios
- Analyse data by applying the techniques learned in class to address decision problems
- Work on a team to deliver a clear and articulated answer to real-world business decisions
- Final written exam (50% of the final grade), based on questions related to the topics covered in class, aims to assess the student's learning level of the methodologies and concepts discussed during the course.
Attendance will be recorded. To take the exam as an attending student, an attendance rate equal to or higher than 75% must be reported.
NOT ATTENDING STUDENTS
The assessment for not attending students will be based on a written exam (100% of the final grade) based on questions related to the topics covered in class, which aims to assess the student's learning level of the methodologies and concepts discussed during the course.
Teaching materials
ATTENDING STUDENTS
Slides and selected papers.
NOT ATTENDING STUDENTS
Slides and selected papers.
Hill, R. C., Griffiths, W. E., & Lim, G. C. (2018). Principles of econometrics. John Wiley & Sons. Selected chapters.
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