Workshop Summary: Introduction to Python for Exo Comet Data Analysis

This week, I observed a session focused on introducing Python for analysing data related to exocomets. This was the fifth session in Jake's project, aimed at teaching students the Python coding skills needed to plot the data.

Preparation and Flexibility:

Jake had clearly discussed the requirements of the session with the school in advance as they emailed prior to the session explaining that the relevant software (ANACONDA) had been installed on the school PC’s. This meant that within 5 minutes of the session starting everyone was up and running with the software with only very minor problems. The main goal of the session was to get everyone to the point where they could plot the data.

Engaging Content and Differentiation:

Jake began with a basic introduction to coding, sharing his own struggles with terminology as an undergraduate to emphasize the practical applications of Python in the project. To accommodate different experiences of Python (some students had taken computer science GCSE and/or had a hobby involving coding), Jake set an open task for students to showcase their existing knowledge through visual outputs like graphs or plots. Those less confident with Python followed along as Jake covered the basics of plotting data. He also used the presence of visitors to recap the previous sessions, where students had focused on the mathematics of stars, their flux, and calculating distances in order to apply it to searching for exocomets. Students discussed their surprise at how flux changes can differentiate objects passing across a star, noting that a comet's irregular shape affects flux differently than a spherical planet.

Fundamental topics:

Jake provided a clear introduction to Python, starting from scratch and periodically checking in with students. When discussing graphs, Jake linked the concepts back to GCSE maths, covering independent and dependent variables, and relating them to the data they would go on to analyse (time on the x-axis, flux on the y-axis). Students learned the importance of including keys, different line styles, and other elements for clear scientific communication.

Assessment for learning:

Students critiqued their first graphs, identifying the need for axis labels, titles, and grids for clarity. Jake reassured students that making mistakes is part of the learning process. One student expressed feeling lost, prompting Jake to set a task for the group while he provided individual support.

Engagement and Outcomes:

Students actively engaged with the material, experimenting with their graphs. By the end of the session, students were equipped with the practical skills needed to plot their data, ready for the next session's focus on real data analysis. Jake encouraged students to explore further and use online resources to enhance their understanding, giving them free time at the end of the session to just play around with their plots and see what they could do, giving them a sense of agency over their learning.

Jake’s session successfully introduced the students to Python coding for data analysis, fostering a supportive and engaging learning environment for something that could be quite mundane to go through.