Integrating Python data analysis in an existing introductory laboratory course

TL;DR

This paper details the integration of Python data analysis using Jupyter Notebooks into a first-year physics lab course, enhancing students' computational skills without disrupting the existing curriculum.

Contribution

It presents a practical approach and materials for incorporating data analysis in Python into introductory physics labs, with assessment of its effectiveness.

Findings

Students improved their data analysis skills.

The approach was well-received by students.

Challenges included balancing programming with physics concepts.

Abstract

In this article we describe how we successfully incorporated data analysis in Python in a first-year laboratory course without significantly altering the course structure and without overburdening students. We show how we created and used carefully designed Jupyter Notebooks with exercises and physics application examples that allow students to master data analysis programming in the laboratory course. We use these Notebooks to guide students through the fundamentals of data handling and analysis in Python while performing simple experiments. We present our teaching approach and the developed materials. We discuss the effectiveness of our intervention based on the results from pre- and post- course questionnaires and students' group work. The results presented give insights about advantages and challenges of introducing computation at the early stage of the curriculum in a laboratory course setting and are informative for other instructors and the physics education research community.