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When we think about problem-solving, our focus tends to be on the solving part: the powerful hack, a new magical tool, a few lines of code that make everything click into place. In reality, a lot has to happen for these final touches to work—from developing a solid understanding of what the problem actually is, to sketching out a workable process that ensures we find consistent success rather than just a temporary band-aid.
Our weekly highlights this week stand out for their holistic approach to finding effective solutions to occasionally thorny challenges. They offer a glimpse into practitioners’ mindset as they explore their available resources (data, tools, and time, to name a few) and weigh the pros and cons of different workflows. We think they might just inspire you to view whatever project you’re working on at the moment from a new perspective. Enjoy your reading!
Algorithmic Thinking for Data Scientists
For a thorough introduction to the benefits of algorithmic thinking—which entails “combining rigorous logic and creativity to frame, solve, and analyze problems, usually with the help of a computer”—don’t miss Chinmay Kakatkar’s excellent article. The focus is on writing efficient code, but you could apply the principles laid out here across a wide range of use cases.
The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 1) Detecting patterns and weeding out anomalies in your dataset remains an essential task for data scientists. Sara Nóbrega’s new guide is a broad, actionable resource that outlines several powerful techniques and zooms in on how you should choose the right one for the project you’re working on.
Jet Sweep: Route Optimization to Visit Every NFL Team at Home
The traveling salesman problem is a classic optimization challenge; Sejal Dua presents an engaging walkthrough of its theoretical complexity, and introduces a few twists: we’re looking at NFL stadiums instead of sales routes, and using linear programming and geospatial data to generate the best possible itinerary to visit all of them.
