Course 2026-2027 a.y.

PART I — Programming & Data Science

A. Essentials of Coding

1. Installation and Setup
   • IDE and editors
   • Jupyter notebooks
   • Use on servers
2. Basics of Python Language
   • Variables
   • Built-in data types: numeric, boolean, strings
   • Containers: lists, dictionaries, tuples, sets
   • Operators: assignment, logical comparison, boolean
3. Control Flow Statements
   • if, elif, else
   • for loop
   • while loop
   • continue, break
   • try, except
4. Functions and Modules
   • Custom defined functions: keywords, docstring, variable scope
   • Anonymous (lambda) functions
   • Packages and modules

B. Data Manipulation

• Pandas library
• Series and DataFrames
• Indexing
• Essential functionalities: filtering, renaming, viewing, handling missing data
• Import and export data: CSV, Stata, Excel, JSON, and more
• Import from databases (e.g. MySQL) and from URLs (APIs)
• Data wrangling: operations on numeric data and strings
• Aggregating data and by-group operations
• Iterating DataFrames: apply functions
• Merge, join, append, and concatenate data
• Reshape: stack, unstack, pivot

C. Data Visualization

• Plotting data with Matplotlib and other libraries
• Common plot types

D. Introduction to Text Analysis

• Regular expressions
• NLTK and spaCy libraries
• Text representation: TF-IDF, word embeddings

PART II — Econometrics

A. The Nature of Econometric Data

• Data generation process: experimental vs. non-experimental data
• Data types: time-series, cross-section, panel/longitudinal
• The research process and the setup of an econometric model

B. The Simple Linear Regression Model

• Economic model and econometric model: introducing the error term
• Estimating regression parameters: the least squares principle
• Interpreting estimates
• Non-linear relationships and indicator variables

C. Interval Estimation and Hypothesis Testing

• Interval estimation
• Hypothesis testing
• P-value and the rejection region

D. The Multiple Regression Model

• Estimation and inference

E. Qualitative and Binary Dependent Variable Models

• LPM, Logit, and Probit

F. Panel Data Models

• Pooled model, fixed effects model, and random effects model

G. A Primer on Causal Identification

• Describe the fundamental principles of Python programming, including variables, data types, control flow, and functions
• Explain the structure and capabilities of the Pandas library for data manipulation, including indexing, filtering, aggregating, and reshaping DataFrames
• Identify appropriate data import and export formats (CSV, Excel, Stata, JSON) and explain how to retrieve data from databases and web APIs
• Describe the key principles of data visualization using Matplotlib and other Python libraries
• Explain the foundations of text analysis, including regular expressions and text representation techniques such as TF-IDF and word embeddings
• Describe the nature of econometric data, distinguishing between experimental and non-experimental data and across data types (time-series, cross-section, panel)
• Explain the simple and multiple linear regression models, including the least squares estimation principle and the interpretation of regression coefficients
• Describe the concepts of interval estimation and hypothesis testing, including p-values and rejection regions
• Identify the appropriate model for binary and qualitative dependent variables, including LPM, Logit, and Probit specifications
• Explain the main panel data model specifications (pooled, fixed effects, and random effects) and the conditions under which each is appropriate
• Describe the fundamental challenges of causal identification in econometric analysis

• Write clean, efficient Python code to import, clean, manipulate, and explore real-world datasets using Pandas
• Produce informative data visualizations that effectively communicate analytical findings to both technical and non-technical audiences
• Apply text analysis techniques, including regular expressions and NLP libraries, to extract meaningful insights from unstructured data
• Formulate and estimate simple and multiple linear regression models on real datasets, correctly interpreting coefficients and model fit
• Conduct hypothesis tests and construct confidence intervals to draw statistically sound conclusions from data
• Select and implement the appropriate econometric model (OLS, Logit, Probit, fixed/random effects) given the structure of the data and the research question
• Evaluate the results of econometric analyses critically, assessing assumptions, potential biases, and limitations
• Apply causal identification strategies to distinguish correlation from causation in observational data
• Communicate data analysis results clearly and concisely in written and oral form, adapting the level of technical detail to the audience
• Work autonomously and collaboratively to tackle data-driven problems, managing analytical workflows from data collection to final reporting

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

Guest Speaker's Talks

Throughout the course, practitioners from the entrepreneurship and venture capital ecosystem are invited to share their experience with students. Speakers may include venture capitalists, startup founders, and data analysts working in early-stage investment or innovation-driven firms. Their talks are designed to bridge the methodological content of the course with real-world applications, illustrating how data analysis and econometric techniques are used to evaluate startups, assess market opportunities, and support investment decisions. Talks may be held in class or remotely.

Practical Exercises

Hands-on coding sessions are integrated throughout the course to reinforce theoretical concepts through direct application. Students work with real or realistic datasets on startups and venture capital investments, covering variables such as funding rounds, investor profiles, firm characteristics, and performance outcomes. Exercises are conducted in Python (Jupyter notebooks) and progressively cover data wrangling with Pandas, data visualization, regression analysis, and econometric modeling. These sessions develop both technical proficiency and the ability to critically interpret quantitative results in an entrepreneurial context.

Collaborative Works / Assignments

Students work in small groups on a data analysis project that runs across the course. Each group elaborates a research question related to startup dynamics or venture capital investment patterns and must use the tools and methods learned during the course to produce a complete empirical analysis. The project includes data collection or retrieval, cleaning and exploration, econometric modeling, and a final written report with visualizations. This assignment fosters teamwork, division of analytical tasks, peer learning, and the ability to communicate complex quantitative findings in a clear and structured manner.

1. Written Examination (60%)

• A 90-minute exam at the end of the course covering data engineering and econometric methods.
  • Assesses theoretical understanding and practical problem-solving skills across both parts of the curriculum.

2. Group Project (40%)

• Teams design and execute an empirical study, culminating in an in-class presentation.
  • Evaluation criteria include research design, data pipeline robustness, econometric analysis and presentation effectiveness.
  • Individual contributions and presentation skills are assessed for each team member.

Written Examination (100%)

• A single, 2½-hour individual exam at the end of the course.
• Covers all topics from data preparation through econometric modeling.

Course materials for Part I will consist of tutorials, code notebooks, and reference readings distributed at the start of the term. For Part II, students will receive lecture slides and a selection of research papers in advance. In addition, the econometrics module will draw on specified chapters from:
Hill, R. C., Griffiths, W. E., & Lim, G. C. (2018). Principles of Econometrics. John Wiley & Sons.

Hill, R. C., Griffiths, W. E., & Lim, G. C. (2018). Principles of Econometrics. John Wiley & Sons (selected chapters). Additional readings for part I and II will be specified at the beginning of the course.