OPCO 2070 - Introduction to Data analysis with python

This course offers a practical introduction to data analysis using real-world datasets. It emphasizes hands-on learning

and focuses on developing Python skills for effective data exploration and interpretation. As the field increasingly

relies on data to understand complex social, economical, and political phenomena, the ability to collect, clean,

analyse and interpret data automatically and rigorously is an essential skill. Therefore, in this class we focus on

practical Python coding session, while inviting the students to individuate the research questions they are interested in

and develop a pipeline to analyse them.

The objectives of the class are threefold: (I) understand the role of data in social, political and economical research

(II) teach core Python skills for data analysis (III) create your own research design to investigate a real-world dataset

and put it to the test in a collective empirical project, thus also learning to effectively communicate your findings.

This is an introductory course, that does not require any prior programming knowledge. However, for the hands-on

sessions, basic Python coding skills are wished. The students are required to install Python and get familiar with its

concept by reading the following literature:

Sweigart, A. (2025). Automate the boring stuff with Python: Practical programming for total beginners (2nd ed.). No

Starch Press. Freely available at: https://automatetheboringstuff.com/ . Reading of Chapter 1 is mandatory.

For the installation of Python, students can follow the official documentation:

Python documentation: https://wiki.python.org/moin/BeginnersGuide/Download

Upon inscription, a document with instructions will also be sent to the students, to guide them through the necessary

installation process. It is essential that students follow those instructions and proceed to the necessary installations

before the first lecture. We will reserve some time during our first class to troubleshoot eventual installation

problems.

This course is composed by strongly interactive, hands-on classes. Typically, one lesson will start with

an explanation of the new concepts, together with several practical demonstration. Then, the second

part of the lesson will consist of exercises to do together, in a workshop modality. The slides and the

sample code will be made available to the students on GitHub.

This course is evaluated on a pass/fail basis. Students are expected to complete the following

assessments in order to progress throughout the semester:

• After each lesson, students will receive homework exercises to complete on a voluntary basis. Each

homework sheet will be reviewed and corrected.

• To validate the course, students will work on a group project. Each group will choose a dataset

among the given ones and elaborate 2 to 3 research questions. The students will have to design a

pipeline to analyse the dataset according to their research question, visualize the results, and report a

qualitative and quantitative analysis. Both the paper and the code have to be submitted, and both will

be reviewed.

In Class Presence: 2 hours a week every 2 weeks / 12 hours a semester

Repetition of concepts studied in class: 20 minutes every 2 weeks / 1 hour a semester

Homework: 2h every 2 weeks / 10 hours a semester

Research and Preparation for Group Work: 1 hour a week / 10 hours a semester

Research and Writing for Individual Assessment: 20 minutes a week / 2 hours per semester