Welcome to the forty ninth ever issue of Monday Morning Data Science from the Fred Hutch Data Science Laboratory. We are excited to show you what we have been working on (Fresh from the Lab), plus links that we think you would be interested in (Our Weekly Bookmarks Bar). Part of the purpose of this newsletter is to start conversations, so if you have a question or there is something you would like to share with us please let us know by responding directly to this email.
Fresh from the Lab
[Job Posting: Data Science Partner Engagement Director] This position will be responsible for strategic partnership in the burgeoning fields of AI, technology and data science in support of research at Fred Hutch to mobilize external resources to advance science and clinical care. Apply at the link above.
[Job Posting: User Experience Researcher] The Data Science Lab is looking for a human-centered designer who can create personas, build prototypes, and lead original research studies with a focus on understanding and improving the day-to-day working experiences of biomedical data science practitioners. Find out more about the position and apply above!
Our Weekly Bookmarks Bar
[Event: PyLadiesCon] PyLadies Conference (PyLadiesCon) is the first Pyladies conference held– an exciting online and FREE event dedicated to empowerment, learning, and diversity within the Python community! 🗓️ Save the Date: December 2nd and 3rd, 2023
[Blog Post: “Releasing collapse 2.0: Blazing Fast Joins, Reshaping, and Enhanced R”] by Sebastian Krantz. Collapse 2.0, released after 3.5 years since the first version, introduces blazing fast joins, advanced data reshaping, and enhanced configurability. It offers a wide range of features for R users, including a new website, an updated cheat sheet, and a new vignette tailored for tidyverse users.
[Article: How to check a simulation study] This article provides advice on improving the reliability of simulation studies in epidemiology and biostatistics with recommendations such as verifying known properties, recreating in data sets, coding in stages, and checking for outliers.
As always you can contact us by replying directly to this email, you can contact the Data Science Lab at data@fredhutch.org, or you are welcome to join us on the Fred Hutch Data Slack Workspace. For more information about the Fred Hutch Data Science Lab, visit our website: https://hutchdatascience.org/. See you next week!
- The Fred Hutch Data Science Laboratory