OPCO 2070 - Data analysis with Python - Introduction

A hands-on introduction to programming in Python using Jupyter Notebooks. Covers core syntax, data types, control structures, functions, file I/O, and error handling. Students then work with Pandas and Matplotlib for structured data analysis and visualization. Datasets are provided in near-clean form, requiring only light manipulation; handling missing values, renaming columns, filtering rows; so the focus stays on programming logic and analytical techniques. Sessions combine live coding in notebooks, whiteboard exercises, and interactive Wooclap quizzes to reinforce concepts. Weekly assignments draw on real-world datasets, e.g., public policy records. By the end of the course, students can write clean, reproducible code to collect, clean, and analyze data independently.

No prerequisites

The material will be delivered on Moodle and GitHub, in the form of presentations and Jupyter notebooks. Moreover, interactive quizzes will be given during the sessions.

The students will be evaluated continuously during the year with small quizzes during the lectures (e.g. Wooclap quizzes). This evaluation will constitute 20% of the final grade. The remaining 80% will be evaluated through a (group?) project that the students will have to submit at the end of the course. The topic will be decided during the year together with the professors, and will be related to what is studied in the main courses followed by the students. Both the practical results (code, models, data visualization, …) and a final report will be evaluated.

In Class Presence: 2 hours a week every week / 24 hours a semester

Repetition of concepts studied in class: 20 minutes a week / 4 hours a semester

Homework: 1h a week / 12 hours a semester

Research and Preparation for Group Work: 1 hour a week / 12 hours a semester

Research and Writing for Individual Assessment: 20 minutes a week / 4 hours per semester