Bulletin of the American Physical Society
APS April Meeting 2021
Volume 66, Number 5
Saturday–Tuesday, April 17–20, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session G06: Computation at All Stages of Physics EducationCareers Education Invited Live Undergrad Friendly
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Sponsoring Units: FED Chair: Adrienne Traxler, Wright State University |
Sunday, April 18, 2021 8:30AM - 9:06AM Live |
G06.00001: The Value of Computational Education in Developing Confidence and Insight: Case Studies from Syracuse University Invited Speaker: Walter Freeman In addition to its standalone value, computational physics education is a highly valuable tool for nurturing students' overall growth and maturity, as they develop the instincts, insight, worldview, and confidence that characterize successful physicists. Computation provides students ready access to rich physical phenomena and unanswered questions that can quickly lead to rewarding independent research that engenders growth and a sense of ownership and confidence. In particular, computational physics gives students an opportunity to explore unanswered questions from the ground up -- creating and implementing models, generating and analyzing data, and drawing physical conclusions -- using tools that they have complete ownership of. While many of these benefits can be realized in a computational physics class, I have found that computational physics independent research can be a profoundly rewarding experience for undergraduate physics majors as they grow into physicists. In this talk, I will discuss my experiences at Syracuse University using computation as a tool to foster student growth and confidence, and discuss how computational training helps physics students develop a more mature, expert understanding of physics as a discipline. [Preview Abstract] |
Sunday, April 18, 2021 9:06AM - 9:42AM Live |
G06.00002: Physicality, Modelling and Making in a Computational Physics Class Invited Speaker: Timothy Atherton Computation is deeply interwoven with virtually every aspect of contemporary Physics research practice including design of experiments, creation of theory, simulations as well as collection, analysis and visualization of data. In contrast, computational activities in Physics classrooms have tended to focus on coding, problem-solving and simulation. To bridge this gap between pedagogy and practice, we have developed a series of making activities whereby students create physical artifacts from low-cost materials, collect quantitative data describing their motion, build models to predict their behavior and reconcile experiment and theory. Results from our first two trials in a group and project-based Computational Physics class will be presented, showing how this approach enables students to engage in disciplinary practice. An epistemic model of how computation produces knowledges is used both to create the design and analyze student work. Design and implementation advice for instructors interested in adopting similar techniques will be provided. [Preview Abstract] |
Sunday, April 18, 2021 9:42AM - 10:18AM Live |
G06.00003: On Transitioning from Academia to Data Science Invited Speaker: Noa Tamir When hearing about transitioning to a data science job, one is often faced with a list of technical skills: from programming packages and frameworks, to applied statistics topics, and data visualizations. But making the transition to data science is not only about technical competencies. It is also valuable to understand the context and environment in which one will be working, the required soft skills, and the culture of potential work places. Most of these cannot be found in job descriptions and data science courses. I will present my understanding of the gap between recent graduates' expectations and competencies and the job itself, as well as some insights into the job market they would need to traverse to get hired. In addition, I will share my teaching approach as a career mentor and a university teacher. I have not only made the transition to data science, but have since supported many in their first steps of their careers. As a former Director of Data Science, and Team Lead, I hired and trained talented academics. I am currently an independent consultant, and teach a M.Sc. Data Science Lab at HTW Berlin, a university of applied sciences. [Preview Abstract] |
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