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Loading... Data Science at the Command Line: Facing the Future with Time-Tested Tools (edition 2014)by Jeroen Janssens (Author)
Work InformationData Science at the Command Line: Facing the Future with Time-Tested Tools by Jeroen Janssens
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Sign up for LibraryThing to find out whether you'll like this book. No current Talk conversations about this book. A really great description of command line tools and how to use and combine them to perform basic data operations with the OSEMN model (Obtain, Scrub, Explore, Model, iNterpret) of data science. I do this at work quite a bit, but I learned about some new tools and methods. The premise of the book is that you can perform many of these data operations (except interpretation) using chains of these well-tested tools. ( ) Great book, easy examples (some have outdated links) - to this really under-rated topic. While the book is plenty shorter than most books on data science, it walks through many tools that help a lot for command line tasks. Working with Linux since quite a while, many of the tools are well known, many are not, and Joeren has a lot of tools added himself, especially to make repetitive tasks easier. If anything, exploring some more options of some tools, or help to install a few of the more complex programs might be good, but overall: excellent. no reviews | add a review
This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You ?ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 80 tools ?useful whether you work with Windows, macOS, or Linux. You ?ll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you ?re comfortable processing data with Python or R, you ?ll learn how to greatly improve your data science workflow by leveraging the command line ?s power. This book is ideal for data scientists, analysts, and engineers; software and machine learning engineers; and system administrators. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTM, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create reusable command-line tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, clustering, regression, and classification algorithms No library descriptions found. |
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Google Books — Loading... GenresMelvil Decimal System (DDC)005.7Information Computer Science; Knowledge and Systems Computer programming, programs, data, security DataLC ClassificationRatingAverage:
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