F1TG 4700 - Programming, Databases, and Machine Learning Foundations for Decision-Makers

This course offers students a practical introduction to computational tools and thinking. It covers the basics of programming, data management, and machine learning, equipping students with skills to work with data in modern research and policy contexts. Through accessible tools and real-world examples, students will explore how code, databases, and algorithms can support analysis, critical inquiry, and decision-making.

Learning Outcomes:
Introduction to Python Programming:
• Variables, data types, and expressions.
• Functions and modular programming.
• Control structures: conditionals and loops.
Python Libraries for Data Handling:
• Dataframe object representation of data.
• Importing, cleaning, and manipulating datasets.
• Practical examples and real-world use cases.
Fundamentals of Relational Databases:
• Overview of database models and architecture.
• Tables, keys, and relationships.
• Introduction to SQL syntax and commands.
• Filtering, sorting, and joining tables.
• Simple and complex queries and sub-queries for data wrangling.
• Common database vulnerabilities and best practices for secure database interaction.
Foundations of Machine Learning:
• Overview of supervised and unsupervised learning.
• Review of machine learning tasks and key algorithms for classification, regression, and clustering.
• The machine learning pipeline: exploratory data analysis, feature engineering, and model training.
• Machine learning model evaluation metrics.
• Interpreting machine learning results in real-world contexts.

Professional Skills
- Understand key programming concepts (e.g., variables, functions, control structures) and their place in business-wide systems and applications.
- Explain the structure and purpose behind relational databases and black-box machine learning models.
- Write basic scripts to handle and analyse data.
- Construct and execute simple queries to retrieve and manage data.
- Use computational tools to apply basic machine learning techniques.
- Interpret results from data queries and machine learning outputs in social and political contexts.
- Identify common database vulnerabilities and discuss basic data security principles.
- Evaluate the role and limitations of computational methods as decision drivers.
- Weigh the ethical implications of using machine learning models.

Sébastien CORNIGLION,Hanna ABI AKL

Séminaire

English

- Attendance: 2 hours a week / 24 hours a semester
- Online learning activities: 24 hours a semester
- Reading and Preparation for Class: 24 hours a semester
- Research and Writing for Individual Assessments: 24 hours a semester

Programme's pre-semester Boot Camp (or basic understanding of computer systems, computer logic and algorithmic thinking).

Autumn 2025-2026

- Assessment 1: Class Labs: Six (6) in total throughout the course; representing 40% of the final grade.
- Assessment 2: Multiple-Choice Questions (MCQs) Exam: covering theoretical concepts seen and discussed in class; at the end of the course (after last day of class), representing 60% of the final grade.

Feedback is to be provided at the end of the course in written format after students pass the final exam and receive their grades.