Ready to elevate your data game beyond basic spreadsheet functions? This category focuses on the practical techniques, powerful tools, and insightful visualizations you need to extract value from any dataset. We’ll cover everything from cleaning messy data with Python and leveraging complex APIs (like Strava’s) to crafting compelling reports that drive better decisions. If you have data, we’ll show you how to make it talk.
Excel’s regex functions bring pattern recognition and text parsing to the spreadsheet. This article explores how Regex Pattern Matching works under the hood – from formal grammars and finite state machines to practical formulas for cleaning, extracting, and restructuring messy data.
Let’s face it: that report you worked on — nobody’s actually going to read it.
In the best-case scenario, people might skim through it, pausing briefly under the allure of a brightly-coloured diagram.
But if you’ve designed your diagrams properly, a brief glance is all someone should need to understand what the data is saying — at least at a high level.
If you haven’t already heard, Strava provide a powerful API that allows users to access their data using code. This gives us the ability to create interesting visualisations, analyse progress, and create custom trackers and planners, all with just a few lines of code.
Strava offers a powerful Web API that allows users to access and analyse data from their activities. Whether you’re a fitness enthusiast looking to track your progress or a developer interested in building a simple app that leverages activity data, the Strava API provides a wealth of possibilities.
