Welcome to the seventy fifth 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
[Event: SciWiki Doc-a-thon] Share the Knowledge! We’re updating the Fred Hutch SciWiki with the knowledge and tutorials you need to work effectively with computation at Fred Hutch. Want to help? Join our SciWiki Doc-a-Thon. We’ll have editors to guide you in your contributions, and make sure that your contribution gets acknowledged. Have some pizza, learn to work on GitHub, and contribute knowledge or edit articles! Make things easier for you and others. Click the link above for more details.
[Scientific Publication: Ten Simple Rules for Teaching an Introduction to R] by Ava Hoffman and Carrie Wright. This article discusses the challenges and solutions in teaching programming skills, particularly R, highlighting the importance of providing better resources for inexperienced instructors to meet the growing demand. It outlines ten key rules derived from the authors' experiences teaching R at various academic institutions, emphasizing practical strategies like intensive courses, team teaching, and live coding sessions. The rules aim to make R accessible to learners with limited computational backgrounds, fostering better engagement and understanding through structured courses and community-supported resources.
Our Weekly Bookmarks Bar
[Academic Paper: The “Why” behind including “Y” in your imputation model] In epidemiological data analysis, missing data is frequently addressed through imputation methods, including deterministic (fixed value) and stochastic (random value) approaches. Including the outcome variable in the imputation model is essential for unbiased results in stochastic imputation, but should not be used in deterministic imputation. This study clarifies when and why to include the outcome in the imputation model, enhancing the understanding of imputation practices and supporting statistical recommendations with mathematical derivations.
[Tutorial: One Hundred Exercises To Learn Rust] This is a comprehensive course designed to teach Rust's core concepts through interactive, hands-on exercises, covering everything from syntax to the ecosystem without requiring prior knowledge of Rust or systems programming. The course, developed by Mainmatter for classroom delivery over four days, also supports self-study through a GitHub repository, encouraging learners to progress with the aid of mentors or peers for an optimal learning experience.
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