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Tip me over and our me out
Welcome to the ninetieth 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
[Opportunities: We are Hiring] New job opportunities in data engineering and administration focus on supporting secure, scalable, and compliant cloud-based infrastructures for research and healthcare applications. Roles range from a Cloud Data System Administrator ensuring system availability and security, to a Data Engineer (III) developing optimized pipelines on Databricks and AWS, and a Data Engineering Manager leading a team to align technical roadmaps with precision oncology and AI goals. All positions require expertise in cloud platforms, data management, security, and regulatory compliance.
Our Weekly Bookmarks Bar
[Blog Post: We've Made it Much Easier to Reuse Our Data] Our World in Data has introduced two new features to enhance data accessibility: improved data download options and a Chart Data API. Users can now download data in CSV format, accompanied by comprehensive metadata in JSON and README files, with the choice of obtaining either the complete dataset or a specific subset. The Chart Data API offers direct URLs for automated workflows, providing the same data and metadata, and allows selection between human-readable or code-friendly column names.
[Book: Supervised Machine Learning for Science] Christoph Molnar and Timo Freiesleben examine the integration of supervised machine learning into scientific research. The authors argue that while machine learning enhances predictive capabilities, its raw application lacks essential scientific attributes like interpretability, causality, and uncertainty quantification. They discuss how to incorporate domain knowledge and robust methodologies to align machine learning models with scientific objectives.
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