Real-World Problems, and How Data Helps Us Solve Them | by TDS Editors | Nov, 2023

With the constant buzz around new tools and cutting-edge models, it’s easy to lose sight of a basic truth: the real value in leveraging data lies in its ability to bring about tangible positive change. Whether it’s around complex business decisions or our everyday routines, data-informed solutions are only as good as the impact they make in the real world.

To help you learn how to connect the dots more effectively and to inspire you to try out new approaches, we’ve gathered some of our recent favorite articles that come with a strong problem-solving angle. They span a wide range of use cases, from the strategic to the extremely personal, but share a pragmatic and detail-oriented outlook on data’s role in our lives. Enjoy!

  • Exploring Time-to-Event with Survival Analysis
    In an accessible introduction to survival analysis, Olivia Tanuwidjaja covers some of the essential concepts and techniques it relies on, and shows how this approach can be applied across a diverse spectrum of problems—from the medical field to maintenance prediction and customer analytics.
  • How Does a Decision Tree Know the Next Best Question to Ask from the Data?
    Some machine learning practitioners might consider binary classification tasks basic, but their usefulness has remained constant even as more complex techniques have emerged in recent years. To help you get started, Gurjinder Kaur recently shared a beginner-friendly primer on decision trees; it explains in great detail how they operate in the context of a model trained to predict whether a given fish is more likely to be tuna or salmon.
Photo by moren hsu on Unsplash
  • My Life Stats: I Tracked My Habits for a Year, and This Is What I Learned
    For his TDS debut, Pau Blasco i Roca presents a yearlong project that lies at the intersection of data analytics and the so-called quantified self. Pau has been tracking his daily activities for 332 days, and shows how you can draw meaningful insights even from data that might appear trivial at first glance.
  • Methods for Modelling Customer Lifetime Value: The Good Stuff and the Gotchas
    For industry data scientists, calculating customer lifetime value is a common goal — and one that gets more complicated the deeper you dig into a business’s operations. Katherine Munro’s comprehensive, practical guide to CLV offers much-needed clarity on this topic and maps out the various modeling options at your disposal, including their respective strengths and limitations.
  • Improving the Strava Training Log
    If you’re a marathoner—and even if you’re not—you won’t want to miss barrysmyth’s latest deep dive, where he takes us through the entire process of downloading, analyzing, and visualizing his Strava training log. It’s a particularly helpful read thanks to its focus on making a successful leap from “here’s a lot of running data!” to “here’s how to use data to run a better race.”

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