Course 2025-2026 a.y.

The course is organized into two blocks—Methods, Topics, each delivered via lectures, case studies, and lab sessions.

• FOUNDATIONS
  1. Course Introduction and Overview of Empirical Strategies
  2. Review of Econometric Principles

• METHODS
  1. Networks
  2. Supervised Learning Models
  3. Unsupervised Learning Models

• TOPICS
  1. Bibliometrics: Scientific Article Analysis
  2. Patent Valuation & Textual Similarity
  3. Geography of Innovation

• Acquire the theoretical foundations of econometric and network-analysis methods for empirical research.
• Understand key machine-learning algorithms (supervised and unsupervised) and their relevance to managerial decision-making.
• Learn the principles of scientometric and patent-analysis techniques for evaluating innovation and technology.

• Apply Python and econometric tools to perform data-driven analyses and derive managerial insights.
• Design and execute empirical studies—from data collection and preprocessing to model implementation and interpretation.
• Collaborate effectively in teams to develop and present a structured, data-based business or economic project.

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Collaborative Works / Assignments

• Lectures: Conceptual overviews and theoretical foundations.
• Case Studies: Real-world examples to link methods with managerial problems.
• Lab Sessions: Hands-on practice in Python on sample datasets.
• Group Project: Incremental development of an empirical study using real data.
• Guest Speaker: Insights from industry on practical applications.

Attending Students

To qualify as attending, students must complete at least two in-class tests and submit the group project. Attendance is encouraged but not formally recorded.

In-Class Tests (40%; three tests, best two count)

• Knowledge and theoretical understanding: Each test evaluates key econometric principles, foundational network-analysis concepts, and core supervised- and unsupervised-learning algorithms (ILOs “Knowledge and understanding”).
• Timely application: Short exercises require students to move swiftly from raw data to numerical results and managerial interpretations, demonstrating their ability to apply tools under time constraints (ILOs “Applying knowledge and understanding”).
• Formative feedback: Allowing the best two grades out of three encourages continuous improvement and concept reinforcement before subsequent tests.

b) Group Project (60%) Final written project

• Integrated, hands-on application: Teams design and execute a full empirical study—from data collection and cleaning to Python-based model implementation—on an innovation or technology topic, directly assessing study design and technical skills (ILOs “Applying knowledge and understanding”).
• Teamwork and communication skills: Written and oral project deliverables evaluate each student’s ability to collaborate effectively, present findings clearly, and contextualize results for managerial decision-making (ILOs “Applying knowledge and understanding”).
• Comprehensive knowledge assessment: Real-world data analysis and interpretation also measure students’ understanding of the underlying methodologies (ILOs “Knowledge and understanding”).

Non-Attending Students

a) Written Exam (50%) Closed-book test covering content of slide and readings discussed in class.

• Broad theoretical coverage: A closed-book exam assesses mastery of lecture slides and assigned readings, ensuring deep comprehension of econometric foundations, network-analysis methods, and major machine-learning techniques (ILOs “Knowledge and understanding”).
• Independent reasoning: Open-ended questions and practical exercises require students to think critically and integrate concepts without external aids.

b) Small Project (50%)

• Individual application of methods: A scaled-down empirical study tests each student’s ability to prepare datasets, run Python analyses, and interpret findings independently (ILOs “Applying knowledge and understanding”).
• Autonomy and project management: The written deliverable assesses organizational skills, clarity of exposition, and capacity to extract managerial insights from empirical results (ILOs “Applying knowledge and understanding”).

• Core Texts & Resources
  • Detailed reading materials and slide decks will be provided before each session.
  • A curated list of recommended journal articles will be communicated in Week 1.
  • Dataset and Python codebook will be made available before the labs