Thursday, October 27, 3-4:30 p.m., online
Register for Using Jupyter Notebooks in Data Science Research and Education
Jupyter Notebooks interweave code, data, and text into an executable “notebook” that can be published or shared as a self-contained object. They can support interactive data science and scientific computing across disciplines and programming languages (including Python, R and Julia) and promote open science and transparency in research. Additionally, Jupyter Notebooks can be used to teach programming and computational literacy.
This panel discussion aims to highlight Jupyter use in research and teaching across campus and foster conversations among Pitt community members who are interested in this topic. It will begin with a practical overview of available Jupyter resources from the Center for Research Computing. Each panelist will then discuss their specific use of Jupyter as outlined below. A question and answer period will follow the panel discussion.
As part of Open Access Week (#OAweek), this event is hosted by the project team of the Pitt Seed grant Cultivating a Data Science Learning Community. All who are interested in data science topics are encouraged to attend! This session will be recorded and shared only with attendees.
Confirmed Panelists Include:
- Richard Boyce, Associate Professor, Department of Biomedical InformaticsDr. Boyce will discuss how he utilizes Jupyter notebooks in analytics and informatics courses, for education within Pitt’s high performance computing environment, and for constructing self assessing notebooks that connect to large databases and/or perform machine learning. Languages used: Python and SQL
- Andrew Haddad, PhD student in Clinical Pharmaceutical Sciences under the mentorship of Dr. Philip EmpeyAndrew Haddad will discuss how he uses Jupyter Notebooks within the All of Us Researcher Workbench to conduct large scale analysis as part of a demonstration project. This project utilizes both whole genome sequencing data and EHR data from nearly 100,000 participants to evaluate the prevalence of actionable pharmacogenomic phenotypes and the associated medications. Languages used: Python, SQL, Bash
- Na-Rae Han, Teaching Professor, Department of Linguistics and Director of the Robert Henderson Language Media CenterDr. Han will showcase how some faculty in the Department of Linguistics have adopted Jupyter Notebooks as a platform for teaching how to process linguistic data. Languages used: Python is the primary language in undergraduate teaching (LING 1340 Data Science for Linguists), while graduate students are taught how to run R in Jupyter Notebooks.
- Daniel Perrefort, Research Assistant Professor, Department of Physics and AstronomyDr. Perrefort will discuss how Jupyter notebooks can be used in conjunction with computing resources provided by the Pitt Center for Research Computing, including how to set up a CRC account and run notebooks on CRC machines for the first time.