Beginning Data Science with Python and Jupyter (Packt)
Thursday, 18 October 2018

This step-by-step guide is aimed at beginners who know a little Python and are looking for a quick, fast-paced introduction. Author Alex Galea explores machine learning models with real datasets. The book ends by showing how easy it can be to scrape and gather data from the open web.

<ASIN:1789532027>

Author: Alex Galea
Publisher: Packt Publishing
Date: June 2018
Pages: 194
ISBN: 978-1789532029
Print: 1789532027
Kindle: B07DNJ9KKL
Audience: Python developers
Level: Introductory
Category: Data Science 

 

  • Identify potential areas of investigation and perform exploratory data analysis
  • Plan a machine learning classification strategy and train classification models
  • Use validation curves and dimensionality reduction to tune and enhance your models
  • Scrape tabular data from web pages and transform it into Pandas DataFrames
  • Create interactive, web-friendly visualizations to clearly communicate your findings

For recommendations of books on big data, see Reading Your Way Into Big Data. For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

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Authors: Ajay Kapur, Perry R. Cook, Spencer Salazar and Ge Wang
Publisher: Manning
Pages:344
ISBN: 9781617291708
Print: 1617291706
Audience: Technically minded musicians and would-be musicians
Rating: 5
Reviewer: Mike James

 

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Author: Evan Burchard
Publisher: O'Reilly
Date: March 2017
Pages: 422
ISBN: 978-1491964927
Print: 1491964928
Kindle: B06XK1V629
Audience: JavaScript programmers
Rating: 3
Reviewer: Ian Elliot

There is a lot of JavaScript out there that needs refactoring - can this book help?


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