Visualization, Math, Time Series, and More: Our Best Recent Deep Dives | by TDS Editors | Feb, 2024

Welcome to the 150th edition of the Variable! Choosing the articles we share in this space is always one of our weekly high points, as it offers us—and hopefully you, too—an opportunity to appreciate the depth and diversity of experiences our authors bring to TDS.

We couldn’t think of a better way to celebrate this milestone than to put together a selection of some of our best recent deep dives. These are the posts that might require the most effort on the part of both writers and editors, but that also deliver on their ambition. Whether they tackle introductory topics or advanced research, they approach their subject matter with nuance and great detail, and patiently walk the reader through new questions and workflows. Let’s dive in!

Photo by Malgorzata Bujalska on Unsplash
  • 9 Simple Tips to Take You From “Busy” Data Scientist to Productive Data Scientist in 2024
    List-based articles come with the risk of rushing through too many items and leaving the reader with very few concrete insights. Madison Hunter’s latest career-advice post shows that it’s possible to cover quite a bit of ground and offer actionable advice even when you divide your material into more digestible morsels.
  • Building a Random Forest by Hand in Python
    To truly grasp how an algorithm like random forest works, few approaches are more effective than building it yourself. This may sound daunting, but fortunately Matt Sosna is here to keep you on the right path with a patient guide that implements the algorithm from scratch in Python.
  • Binary Logistic Regression in R
    Whether you’re taking your first steps with logistic regression or looking for some hands-on practice for coding in R, Antoine Soetewey’s new article is the one-stop resource you don’t want to miss—it outlines when and how to use a (univariate and multivariate) binary logistic regression, as well as how to visualize and report results.
  • 12 RAG Pain Points and Proposed Solutions
    We end on a similar note to the one we started with: a comprehensive, practical guide on a timely technical topic—in this case, Wenqi Glantz’s troubleshooting post on common issues you might run into in your retrieval-augmented generation workflows, and how to move past them.

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