Cultural Analytics (MIT Press)
Friday, 30 October 2020

What analytical methods can we bring to bear on the astonishing scale of digital culture--the billions of photographs shared on social media  every day, the hundreds of millions of songs created by twenty million musicians on Soundcloud, the content of four billion Pinterest boards? In this book, Lev Manovich presents concepts and methods for computational analysis of cultural data.

<ASIN:0262037106>

Drawing on a decade of research and projects from his own lab, Manovich offers a gentle, nontechnical introduction to the core ideas of data analytics and discusses the ways that our society uses data and algorithms.

Author: Lev Manovich
Publisher: MIT Press
Date: October 2020
Pages: 336
ISBN: 978-0262037105
Print: 0262037106
Kindle: B084V7XLC7
Audience: General
Level: Introductory
Category: Data Science

 

For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

 

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Python Machine Learning, 2nd Ed

Author: Sebastian Raschka and Vahid Mirjalili
Publisher: Packt
Pages: 622
ISBN: 978-1787125933
Print: 1787125939
Kindle: B0742K7HYF
Audience: Python developers interested in machine learning
Rating: 4.8
Reviewer: Mike James
Python and machine learning are made for each other.



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Author: Lee Wittenberg
Publisher: Mercury Learning
Pages: 200
ISBN: 978-1683920847
Print: 1683920848
Kindle: B071CMSFHF
Audience: Student developers
Rating: 4
Reviewer: Mike James
Data structures are the core of many a computer science course and a book explaining them in C++ is a great idea.

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