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Arcus OnRamp Workshops - Analysis in Python - Dec. 18, 2024
United States
Get hands-on practice running Python in an Arcus Lab! We’ll be working together on a retrospective electronic health record (EHR) analysis that replicates a real published study so you can see firsthand what it’s like conducting research with Arcus. If you've attended an Arcus On-Ramp SQL session, this is a continuation of the same replication, but for this workshop we’ll start with a SQL query already defined for us and just jump right into data cleaning and analysis in Python, including how to save and export your results so you can disseminate your findings. If you haven't been to
Join us for an Arcus OnRamp Workshop - Analysis in Python to get hands-on practice running Python in an Arcus Lab. We 'll work together on a retrospective electronic health record (EHR) analysis that replicates a real published study so you can get firsthand experience conducting research with Arcus.
If you've attended an Arcus On-Ramp SQL session, this is a continuation of the same replication. For this workshop, we 'll start with a SQL query already defined and jump right into data cleaning and analysis in Python, including how to save and export your results so you can disseminate your findings.
If you haven't been to a SQL session, no worries! You can attend the workshops in either order. If you have been to our R session, we will be replicating the same study, but this time using Python in a Jupyter notebook.
How to Register
Log in on the Arcus homepage, then sign up to attend your chosen session.
Registration closes one week before each workshop to allow time to add registered attendees as users in the webinar training lab.
Please note: Webinar participants complete CITI training and sign the Arcus Terms of Use before joining the workshop, as we will be working with de-identified patient data. Not sure if you have completed both of these requirements? Check by logging into Account Access Requirements with your CHOP credentials.
Contact Elizabeth Drellich, data instructional specialist, with questions.