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How to clean quantitative user data — a visual guide
How learning 100 lines of code can save you hours of fixing user data spreadsheets
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Learning just 100 lines of code can save you hours of fixing your user research data. And it’s less frightening than you might think.
I’ve recently been spending a lot more time with the quantitative side of user research. Between surveys with 100+ participants, tracking metrics across multiple design iterations, and working with Google Analytics, I’ve gotten to experience the ‘joys’ of working with real-world datasets.
These joys include reading Excel spreadsheets until my eyes crossed, trying to track down missing values, and a whole lot of inconsistencies across multiple design iterations.
But after messing around with the typical tools we might use as designers (like Excel, note-taking programs like Reframer, or Visualization tools like Tableau), I’ve realized that using Python is often the best option to quickly format and make sense of the data that I’ve collected. But telling designers to learn a coding language can often be a hard sell.
I know from experience that many UX Designers have a mixed range of emotions towards coding, particularly fear.