Data Science at the Command Line
- نوع فایل : کتاب
- زبان : انگلیسی
- نویسنده : Jeroen Janssens
- چاپ و سال / کشور: 2014
- تعداد صفحه : 212
- شابک / ISBN : 1491947853
Description
This hands-on 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—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.
Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line.
Obtain data from websites, APIs, databases, and spreadsheets
Perform scrub operations on plain text, CSV, HTML/XML, and JSON
Explore data, compute descriptive statistics, and create visualizations
Manage your data science workflow using Drake
Create reusable tools from one-liners and existing Python or R code
Parallelize and distribute data-intensive pipelines using GNU Parallel
Model data with dimensionality reduction, clustering, regression, and classification algorithms
To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.
Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line.
Obtain data from websites, APIs, databases, and spreadsheets
Perform scrub operations on plain text, CSV, HTML/XML, and JSON
Explore data, compute descriptive statistics, and create visualizations
Manage your data science workflow using Drake
Create reusable tools from one-liners and existing Python or R code
Parallelize and distribute data-intensive pipelines using GNU Parallel
Model data with dimensionality reduction, clustering, regression, and classification algorithms
