— Notes —
Data Elixir Article Search is back online!
Search by keyword(s) and/or publication and/or author.
Author and publication don't always work but if people start using it, I'll make it more robust and add functionality.
To get started, try these:
If you have any suggestions or issues, please let me know >>
— In the News —
She talks! How will we ever know what to believe? This short article from TechCrunch includes a 5 minute video that shows how the technique works. See the paper for details. Amazing.
Analytics has famously influenced professional baseball and basketball in recent years. International football could become an even bigger deal. The Liverpool Football Club has been making extensive use of data analytics and is finishing a phenomenal season. This article in The New York Times explores how far they're going with it and offers a glimpse into how analytics could impact the sport beyond the U.K.
— Sponsored Link —
Dimensional Research recently conducted a global survey of hundreds data scientists and other AI professionals in large companies across 20 industries to determine their experiences with ML development projects. The survey suggests that most organizations find ML model training to be more challenging than they expected.
— How-to —
These practical ideas from Chris Moradi at StitchFix will help make your ETL pipelines easier to debug, maintain, and extend.
Nice guide to getting machine learning models into production. It's fairly high-level but there are links throughout to go deeper. Includes discussion of the complexities involved, design considerations, tooling, testing, developments to watch, etc.
This tutorial by Bryan M. Li shows how to build a Transformer with the Keras Functional API using Tensorflow 2.0. This is well done and easy to follow.
Vettery specializes in tech roles and is completely free for job seekers. Interested? Submit your profile, and if accepted onto the platform, you can receive interview requests directly from top companies growing their data science teams.
— Resources —
This new open access book presents a tutorial-level overview of the methods underlying Automated Machine Learning (AutoML). Sections cover existing systems based on these methods and challenges of AutoML. Free to download or if you prefer print, follow the links to order online.
Nice collection of resources to catch up on the latest trends in natural language processing. In addition to a lot of paper picks, this article includes links to introductory posts, recommended blogs, online courses, and books.
— Data Viz —
Evan Peck won a best paper award at ACM CHI 2019 for the work described in this post. When asked to rank visualizations, interviewees tended to filter the visualizations through their own experience. For a world that increasingly needs to share and understand data, that's important.
— Career —
Here's a reasonable approach for defining the responsibilities and expected capabilities of Junior, Senior, and Principal Data Scientists. These will vary by organization, of course, but these descriptions are a good starting point for discussions and offer things to think about for navigating your career.