Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with UNIX Power Tools (Paperback)
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Learn how to greatly improve your data science workflow by leveraging the command line's power.
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- 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 100 Unix power 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, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTML, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create your own tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, regression, and classification algorithms Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | September, 2021 |
| Pages | 280 |
| Reading level | Professional and Scholarly |
| Subgenre | Computers |
| Edition | 2 |
| Publisher | O'Reilly Media |
| Original languages | English |
| Language | English |
| Assembled product height | 9.19 in (23.3 cm) |
| Assembled product weight | 1 lb (450 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
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Data Enthusiasts
Individuals eager to enhance their data manipulation skills using command line tools for better efficiency.
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UNIX Users
Users familiar with UNIX who want to apply command line techniques to data science tasks effectively.
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Self-Learners
Individuals motivated to learn data science independently through practical examples and hands-on exercises.
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Beginners
Complete newcomers to data science without UNIX knowledge may struggle to grasp the concepts presented in this book.
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Non-technical Users
Those who lack programming or command line experience may find the content overwhelming and challenging to understand.
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Casual Learners
Individuals seeking a light introduction to data science may find this book too advanced or technical for their needs.
Product Description
Customer Questions & Answers
-
Question:
Who is this book ideal for?
Answer: Data scientists, analysts, engineers, system administrators, and researchers. -
Question:
What can I do with the command line?
Answer: You can obtain data from websites, APIs, databases, and spreadsheets, perform scrub operations, explore data, compute statistics, and create visualizations. -
Question:
Can I use this book with Python, Jupyter, R, RStudio, and Apache Spark?
Answer: Yes, you can leverage the command line from these platforms.
Jeroen Janssens All Books Editorial Review
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BHD 20
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Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
Features & Benefits
- Learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data.
- Ideal for data scientists, analysts, engineers, system administrators, and researchers.
- Obtain data from websites, APIs, databases, and spreadsheets.
- Perform scrub operations on text, CSV, HTML, XML, and JSON files.
- Explore data, compute descriptive statistics, and create visualizations.
- Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark.