Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session M36: Data Science in the Physics CurriculumEducation Invited Undergrad Friendly
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Sponsoring Units: GDS FED Chair: Mohammad Soltanieh-Ha, Boston Univ Room: 601/603 |
Wednesday, March 4, 2020 11:15AM - 11:51AM |
M36.00001: Designing curricula for data science based on fundamental skills and competencies informed by expert interviews Invited Speaker: Devin Silvia With computational modeling and data analysis skills becoming an increasingly integral part of modern scientific work and research, understanding the specific competencies and skills required for individuals to participate in this work is growing more important. Previous efforts have defined these competencies and skills to varying degrees, with recommendations ranging from writing scientific software to designing entire degree programs in data science; however, the existing computational science education literature is incomplete in some ways. Primarily, much of the existing literature lacks a research-based foundation. We conducted a series of semi-structured interviews with experts in academic and industry settings to broadly describe the skills that students need to have in order to participate in work and research in computational science. In this talk, I will present the results of our research and highlight some of the design choices we have made for our introductory computational science course and how those choices connect to our research findings. |
Wednesday, March 4, 2020 11:51AM - 12:27PM |
M36.00002: A university-wide approach to integrative data science education and career paths Invited Speaker: Sarah Stone All areas of science, particularly the physical sciences, are experiencing a data revolution that is changing our approaches to discovery. This “data science” transformation is being driven by factors such as advancements in high-throughput instrumentation, new modes of scalable computing, and perhaps most importantly, new breakthroughs in algorithms such as deep learning. Harnessing these advances with a coherent and intentional approach is essential to enabling new discoveries. As such, researchers need access to state-of-the-art data science methods and tools. Moreover, these methods and tools must evolve rapidly, driven by the requirements of discovery. This rate of advancement is currently limited by the “intellectual infrastructure” of trained data science practitioners. At the University of Washington’s eScience Institute, we have developed a university-wide approach to addressing this challenge by developing an integrative education program through formal data science options, now offered by 18 academic units at UW, and informal education programs. We have also supported transformational individuals, in both traditional and non-traditional academic roles, who bridge data science methods development and applied research. As a hub for data science on the UW campus, the eScience Institute has grown and fostered an interdisciplinary community focused on advancing data-intensive research and education across all fields. |
Wednesday, March 4, 2020 12:27PM - 1:03PM |
M36.00003: A beginner's guide to using data science for physicists Invited Speaker: Trevor David Rhone The data revolution is changing the way we live. Exploiting data using data science tools has sparked innovation, spanning areas as diverse as spam filters, driverless cars and image recognition. Data science is rapidly becoming the latest addition to the physicist’s toolkit. But, what is data science? Why is it useful? How does it work? This talk provides a beginner’s guide to data science for the inquisitive physicist. We will explore the major aspects of data science and discuss useful applications. In addition, I will provide a step by step guide on how to get started learning data science and share the secret to mastery of this exciting new field. |
Wednesday, March 4, 2020 1:03PM - 1:39PM |
M36.00004: Deep Learning Data Science Competencies to Promote Workplace Readiness Invited Speaker: Amir Shahmoradi Data science is a rapidly growing field with a 663% increase in the number of job postings for a data scientist between 2013 and 2018. The demand for college graduates who are trained in data science skills spans every field of science. Therefore, undergraduate and graduate programs must be responsive to align their curricula with these dynamic needs. However, the data scientist title is relatively new and formal data science competencies are yet to be defined. In this talk, I will describe our efforts at the University of Texas to 1. determine what these data science technical and soft skill competencies are by analyzing data from national job postings, and 2. systematically investigate how the pattern of required skills varies by co-occurrence, domain of science knowledge, and characteristics of the jobs and employers. Such knowledge can help identify gaps between academic preparation and the skills employers seek by identifying data science competencies employers are requesting within and between domains of science, and then evaluating how well these skills align with science curriculum and finally, predicting the future of job market’s supply versus demand for data science skills. |
Wednesday, March 4, 2020 1:39PM - 2:15PM |
M36.00005: Data Science Tools in the Classroom Invited Speaker: Mohammad Soltanieh-Ha Teaching methods for programming and data science-related topics have been evolving faster than ever before. This has been heavily influenced by the fast-growing popularity of cloud-based tools. In this talk, I will provide an overview of tools and techniques that can improve both the learning experience of the students and the instructor’s ability to manage the class and materials. I will discuss the best practices to manage and distribute code and data, as well as the platforms used in a data science project. Among a vast space of competitive solutions, I have decided to use Google products as the primary platform. Google Colaboratory (Colab) will be introduced as a solution to run and share the code. Beyond Colab, I will present an end-to-end data science project on a cloud-based ecosystem, using Google Cloud Platform (GCP). In addition to the essential elements of GCP, I will cover ways to tackle big data problems using Hadoop and Spark, as well as utilizing containerized applications for large scale parallel processing. I will illustrate how I have used GCP in my classes at Boston University and share feedback from the students. Additionally, I will touch on open-source auto-grading tools. |
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