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When we think about problem solving, our attention tends to focus on the solving part: the powerful hack, a new magic tool, a few lines of code that make everything click into place. In reality, a lot has to happen for these finishing touches to work: from developing a solid understanding of what the problem really is to outlining a workable process that ensures we achieve ongoing 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 insight into the mindset of professionals as they explore available resources (data, tools, and time, to name a few) and weigh the pros and cons of different workflows. We think they might inspire you to look at whatever project you're working on right now from a new perspective. Enjoy your reading!
- Algorithmic thinking for data scientists
For a comprehensive introduction to the benefits of algorithmic thinking, which involves “combining rigorous logic and creativity to formulate, solve, and analyze problems, usually with the help of a computer,” don't miss Chinmay KakatkarExcellent article. The focus is on writing efficient code, but you can apply the principles laid out here in a wide range of use cases. - The Ultimate Guide to Finding Outliers in Your Time Series Data (Part 1)
Detecting patterns and eliminating anomalies in your data set remains an essential task for data scientists. Sara NobregaThe new guide is a comprehensive and practical resource that describes several powerful techniques and focuses on how to choose the right one for the project you are working on. - Jet Sweep: Route Optimization to Visit Every NFL Team at Home
The salesman problem is a classic optimization challenge; Signal two presents an interesting tour of its theoretical complexity and introduces some twists: we are analyzing NFL stadiums instead of sales routes, and we use linear programming and geospatial data to generate the best possible itinerary to visit them all.
