The Data All Around Us: From Sports to Household Management

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

Every once in a while, it pays off to take a few steps back to appreciate the value that robust data analysis can bring to nearly every aspect of daily life. We tend to focus on business outcomes and product development, and for good reasons, but the world stretches far beyond the common workflows of data scientists, and exploring the richness of the field can benefit practitioners regardless of the projects they’re currently working on.

To celebrate the diverse use cases for data-focused approaches and to encourage our readers to expand both their skill set and their imagination, we’re thrilled to share a lineup of excellent articles that take us on unexpected journeys with data—from score patterns in athletic competitions to the optimized table assignments at weddings. Enjoy your reading!

• Data Science at Home: Solving the Nanny Schedule Puzzle with Monte Carlo and Genetic Algorithms
  “The thought of work meetings, nap times, and unpredictable shifts have our minds running in circles — until I realized I could use the same algorithms that solve business problems to solve a very personal one.” Courtney Perigo walks us through the meticulous problem-solving process she’s created to address the thorny challenge of scheduling childcare at the times it’s needed the most.
• Uneven Scoring in Multi-Event Athletics
  With the Paris Olympics still fresh in our memories, now’s as good a time as any to dive into the many areas where data science and sports intersect. David Mulholland chose a topic—the intricate scoring systems in the decathlon and heptathlon—that may appear niche at first, and unpacked its stakes with great nuance, showing how smart data analysis can reveal otherwise hard-to-detect patterns and insights.

• The Price of Gold: Is Olympic Success Reserved for the Wealthy?
  Staying on the theme of sports and data, Maria Mouschoutzi, PhD leans into her knowledge of statistics and data visualization, as well as her past experience as a rhythmic gymnast, to attempt to answer a complex question: to what extent does the economic status of a country contribute to its Olympic medal count?
• Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python
  Zooming in on the restricted quadratic multi-knapsack problem (RQMKP), mathematical programming, and Python, Luis Fernando PÉREZ ARMAS, Ph.D. uses the tricky art of wedding seating arrangements to demonstrate how data and math can help us solve real-world problems—and outlines several extensions and advanced methods you can apply in other, more everyday situations.

If you’re ready to dive back into some of our core data science and ML topics, we have a top-notch selection of articles to recommend this week:

• To streamline and customize her research and presentation workflow, Lingzhen Chen turns to a recently launched LlamaIndex feature, and explains how to use it effectively.
• Writing at the intersection of geospatial data, machine learning, and environmental studies, Conor O’Sullivan weighs the benefits and limitations of deep learning approaches for coastal-erosion monitoring.
• Taking your first steps in reinforcement learning? Don’t miss Jesse Xia’s beginner-friendly guide, which uses environments from the OpenAI Gymnasium Python package.
• In a thorough and accessible deep dive, Nicolas Arroyo Duran presents a novel method for training generative machine learning models capable of approximating any stochastic function with multivariate outputs.
• For anyone interested in learning about cutting-edge RAG approaches, Steve Hedden’s latest hands-on guide offers a patient, step-by-step workflow for implementing Graph RAG with knowledge graphs and vector databases.
• How should you go about designing a “starter” AI project, especially at companies that haven’t yet embraced the technology? Julia Winn provides concrete tips for product managers who’d like to branch out into new areas.
• Taking the classic “forehead detective” guessing game as his point of departure, Krzysztof K. Zdeb shares the results of his experiments playing it with LLMs, and opens up a broader discussion on models’ current reasoning capabilities.
• If you frequently work with geospatial data and would like to grow your knowledge of available tools and processes, Amanda Iglesias Moreno shows us how to extract subway-route data from OpenStreetMaps via the Overpass API.

Thank you for supporting the work of our authors! As we mentioned above, we love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

The Data All Around Us: From Sports to Household Management was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.