Data Wrangling with JavaScript

I recently read and enjoyed Ashley Davis’s new book Data Wrangling with JavaScript. As its title suggests, it doesn’t spend very much time on statistical theory; instead, it covers the “other 90%” of squeezing answers out of data:

  1. Establishing your data pipeline
  2. Getting Started with Node.js
  3. Acquisition, storage and retrieval
  4. Working with unusual data
  5. Exploratory coding
  6. Clean and prepare
  7. Dealing with huge data files
  8. Working with a mountain of data
  9. Practical data analysis
  10. Browser-based visualization
  11. Server-side visualization
  12. Live data
  13. Advanced visualization with D3
  14. Getting to production

There are lots of code samples and plenty of diagrams, and you can download both the data sets the author uses in examples and the Data-Forge library he has developed for data crunching in JavaScript. I suspect readers will need some prior familiarity with JavaScript to dive into this, and as I said a few weeks ago, I don’t think Python and R have anything to worry about unless and until JavaScript acquires operator overloading, but Davis shows just how far you can go with what’s available in modern JavaScript today, and that the journey is a lot smoother than people might think.

Disclaimer: Toby Hodges and I recently released JavaScript versus Data Science, the more advanced bits of which overlap with some of the earlier material in this book.

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