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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
Jeroen Janssens is a senior data scientist at YPlan in New York City. His specialties are in machine learning, anomaly detection, and data visualization. Jeroen is passionate about building open source tools for doing data science. He obtained a B.Sc. in Life Sciences and an M.Sc. in Artificial Intelligence, both cum laude from Maastricht University, the Netherlands. Jeroen completed his Ph.D. in Machine Learning at the Tilburg center for Cognition and Communication, Tilburg University. Outside of work, you may find him biking the Brooklyn Bridge, beatboxing, or eating stroopwafels.
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!
Bottom Line Yes, I would recommend this to a friend