To see the ‘Sheet book‘ and ‘Table of associated records‘ go the bottom, Pag. 2Pag. 3. ( 👩‍🎓 🖥 For free online version, on the image).

A review of the book for research, educational and recommendations.


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Recommendation numbered, Nº:  27032020p1


📘 url personal use : |🔓 1 Openaccess – 🛒 2 to buy | (Copy & paste at the browser)

-Creative Commons address: bit.ly/2Unhlq2
-Paid version address: amzn.to/3ap5jlt

Summary

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. 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 text, CSV, HTML/XML, and JSON
  • 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

(Source: datascienceatthecommandline.com)

Chapters

  • Welcome
  • Preface
  • 1 Introduction
  • 2 Getting Started
  • 3 Obtaining Data
  • 4 Creating Reusable Command-line Tools
  • 5 Scrubbing Data
  • 6 Managing Your Data Workflow
  • 7 Exploring Data
  • 8 Parallel Pipelines
  • 9 Modeling Data
  • 10 Conclusion
  • References

Author

[Unofficial biography. For informational purposes only]

Jeroen Janssens

He teaches data science; often through training and coaching, occasionally through speaking, and infrequently through writing. His interests include visualizing data, building machine learning models, and automating things using either Python, R, or Bash. Jeroen is the founder and CEO of Data Science Workshops. They organise open enrollment workshops, in-company courses, inspiration sessions, hackathons, and meetups. All related to data science of course. Previously, he was an assistant professor at Jheronimus Academy of Data Science and a data scientist at Elsevier in Amsterdam and various startups in New York City. (Source: jeroenjanssens.com)


Please, thank the author and Publisher


Thank you very much for this work to @jeroenhjanssens, via @States_AI_IA #datascience #dataset #openscience #openaccess #ai #artificialintelligence #ia #thebibleai #ebook #free #thanks