Data Science at the Command Line. 2nd Edition - Helion

ebook

Autor: Jeroen Janssens
ISBN: 9781492087861
stron: 282, Format: ebook
Data wydania: 2021-08-17
Księgarnia: Helion

Cena książki: 186,15 zł (poprzednio: 216,45 zł)
Oszczędzasz: 14% (-30,30 zł)

Osoby, które kupiły tę książkę, wybierały także »

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

Osoby które kupowały "Data Science at the Command Line. 2nd Edition", wybierały także:

Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
Ruby on Rails. Ćwiczenia 18,75 zł, (3,00 zł -84%)
DevOps w praktyce. Kurs video. Jenkins, Ansible, Terraform i Docker 190,00 zł, (39,90 zł -79%)
Przywództwo w świecie VUCA. Jak być skutecznym liderem w niepewnym środowisku 58,64 zł, (12,90 zł -78%)
Scrum. O zwinnym zarządzaniu projektami. Wydanie II rozszerzone 58,64 zł, (12,90 zł -78%)

Spis treści

Data Science at the Command Line. 2nd Edition eBook -- spis treści

Foreword
Preface
- What to Expect from This Book
- Changes for the Second Edition
- How to Read This Book
- Who This Book Is For
- Conventions Used in This Book
- OReilly Online Learning
- How to Contact Us
- Acknowledgments for the Second Edition (2021)
- Acknowledgments for the First Edition (2014)
1. Introduction
- Data Science Is OSEMN
  - Obtaining Data
  - Scrubbing Data
  - Exploring Data
  - Modeling Data
  - Interpreting Data
- Intermezzo Chapters
- What Is the Command Line?
- Why Data Science at the Command Line?
  - The Command Line Is Agile
  - The Command Line Is Augmenting
  - The Command Line Is Scalable
  - The Command Line Is Extensible
  - The Command Line Is Ubiquitous
- Summary
- For Further Exploration
2. Getting Started
- Getting the Data
- Installing the Docker Image
- Essential Unix Concepts
  - The Environment
  - Executing a Command-Line Tool
  - Five Types of Command-Line Tools
  - Combining Command-Line Tools
  - Redirecting Input and Output
  - Working with Files and Directories
  - Managing Output
  - Help!
- Summary
- For Further Exploration
3. Obtaining Data
- Overview
- Copying Local Files to the Docker Container
- Downloading from the Internet
  - Introducing curl
  - Saving
  - Other Protocols
  - Following Redirects
- Decompressing Files
- Converting Microsoft Excel Spreadsheets to CSV
- Querying Relational Databases
- Calling Web APIs
  - Authentication
  - Streaming APIs
- Summary
- For Further Exploration
4. Creating Command-Line Tools
- Overview
- Converting One-Liners into Shell Scripts
  - Step 1: Create a File
  - Step 2: Give Permission to Execute
  - Step 3: Define a Shebang
  - Step 4: Remove the Fixed Input
  - Step 5: Add Arguments
  - Step 6: Extend Your PATH
- Creating Command-Line Tools with Python and R
  - Porting the Shell Script
  - Processing Streaming Data from Standard Input
- Summary
- For Further Exploration
5. Scrubbing Data
- Overview
- Transformations, Transformations Everywhere
- Plain Text
  - Filtering Lines
    - Based on location
    - Based on a pattern
    - Based on randomness
  - Extracting Values
  - Replacing and Deleting Values
- CSV
  - Bodies and Headers and Columns, Oh My!
  - Performing SQL Queries on CSV
  - Extracting and Reordering Columns
  - Filtering Rows
  - Merging Columns
  - Combining Multiple CSV Files
    - Concatenate horizontally
    - Joining
- Working with XML/HTML and JSON
- Summary
- For Further Exploration
6. Project Management with Make
- Overview
- Introducing Make
- Running Tasks
- Building, for Real
- Adding Dependencies
- Summary
- For Further Exploration
7. Exploring Data
- Overview
- Inspecting Data and Its Properties
  - Header or Not, Here I Come
  - Inspect All the Data
  - Feature Names and Data Types
  - Unique Identifiers, Continuous Variables, and Factors
- Computing Descriptive Statistics
  - Column Statistics
  - R One-Liners on the Shell
- Creating Visualizations
  - Displaying Images from the Command Line
  - Plotting in a Rush
  - Creating Bar Charts
  - Creating Histograms
  - Creating Density Plots
  - Happy Little Accidents
  - Creating Scatter Plots
  - Creating Trend Lines
  - Creating Box Plots
  - Adding Labels
  - Going Beyond Basic Plots
- Summary
- For Further Exploration
8. Parallel Pipelines
- Overview
- Serial Processing
  - Looping Over Numbers
  - Looping Over Lines
  - Looping Over Files
- Parallel Processing
  - Introducing GNU Parallel
  - Specifying Input
  - Controlling the Number of Concurrent Jobs
  - Logging and Output
  - Creating Parallel Tools
- Distributed Processing
  - Get List of Running AWS EC2 Instances
  - Running Commands on Remote Machines
  - Distributing Local Data Among Remote Machines
  - Processing Files on Remote Machines
- Summary
- For Further Exploration
9. Modeling Data
- Overview
- More Wine, Please!
- Dimensionality Reduction with Tapkee
  - Introducing Tapkee
  - Linear and Nonlinear Mappings
- Regression with Vowpal Wabbit
  - Preparing the Data
  - Training the Model
  - Testing the Model
- Classification with SciKit-Learn Laboratory
  - Preparing the Data
  - Running the Experiment
  - Parsing the Results
- Summary
- For Further Exploration
10. Polyglot Data Science
- Overview
- Jupyter
- Python
- R
- RStudio
- Apache Spark
- Summary
- For Further Exploration
11. Conclusion
- Lets Recap
- Three Pieces of Advice
  - Be Patient
  - Be Creative
  - Be Practical
- Where to Go from Here
  - The Command Line
  - Shell Programming
  - Python, R, and SQL
  - APIs
  - Machine Learning
- Getting in Touch
A. List of Command-Line Tools
- alias
- awk
- aws
- bash
- bat
- bc
- body
- cat
- cd
- chmod
- cols
- column
- cowsay
- cp
- csv2vw
- csvcut
- csvgrep
- csvjoin
- csvlook
- csvquote
- csvsort
- csvsql
- csvstack
- csvstat
- curl
- cut
- display
- dseq
- echo
- env
- export
- fc
- find
- fold
- for
- fx
- git
- grep
- gron
- head
- header
- history
- hostname
- in2csv
- jq
- json2csv
- l
- less
- ls
- make
- man
- mkdir
- mv
- nano
- nl
- parallel
- paste
- pbc
- pip
- pup
- pwd
- python
- R
- rev
- rm
- rush
- sample
- scp
- sed
- seq
- servewd
- shuf
- skll
- sort
- split
- sponge
- sql2csv
- ssh
- sudo
- tail
- tapkee
- tar
- tee
- telnet
- tldr
- tr
- tree
- trim
- ts
- type
- uniq
- unpack
- unrar
- unzip
- vw
- wc
- which
- xml2json
- xmlstarlet
- xsv
- zcat
- zsh
Index