Data Science at the Command Line
Publisher: O'Reilly Media
Final Release Date: June 2014
Pages: 208

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In this practical guide, you’ll learn how to leverage the power of the command line for doing data science. By combining small, yet powerful, command-line tools, you can quickly obtain, scrub, explore, and model your data. Even if you’re already comfortable processing data with R or Python, being able to integrate the command line into your existing workflow will make you a more efficient and productive data scientist.

  • Learn essential concepts and built-in commands of the *nix command line
  • Get started with your own Data Science Toolbox on either Linux, Mac OS X, or Microsoft Windows
  • Use classic command-line tools such as grep, sed, and awk
  • Obtain data from websites, APIs, databases, and spreadsheets
  • Parallelize and distribute data-intensive pipelines to remote machines, including AWS EC2
  • Clean data in CSV, JSON, and XML/HTML formats using csvkit, and jq, and scrape
  • Apply dimensionality reduction, clustering, regression, and classification algorithms
  • Visualize data and results from the command line using gnuplot and ggplot
  • Turn Bash one-liners and existing Python and R code into reusable command-line tools
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oreillyData Science at the Command Line
 
4.0

(based on 1 review)

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(7 of 7 customers found this review helpful)

 
4.0

Book on an essential but forgotten skill

By Vijay N Phadke

from Fremont, CA

About Me Developer

Verified Reviewer

Pros

  • Easy to understand
  • Helpful examples

Cons

  • Work In Progress

Best Uses

  • Expert
  • Intermediate

Comments about oreilly Data Science at the Command Line:

The book subject got my attention as I do quite a bit of data wrangling on command line myself using standard unix commands. I was interested to see if the Author has more interesting stuff to offer.
It's a bit early to say but the book looks good so far and I think the book will improve as it progresses.
Chapter 4 is the best by far and I would love to see Author continuing in the same direction. Section 'Converting One-liners into Shell Scripts' is the case in point because majority of programmers take that path and is quite familiar.
Chapter 2 Getting started is too elaborate with too many screen shots, and can be shortened considerably. The Author can direct the reader to the companion website for more details as the book can quickly get out of sync!

In general this book is recommended even for non data scientists who need to wrangle data in their daily work leveraging the superb linux command line tools!

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September 2014 (est.)