Mikhail Klassen

V nedrakh populjarnykh sotsialnykh setej - Twitter, Facebook, LinkedIn i Instagram - skryty bogatejshie zalezhi informatsii. Iz etoj knigi issledovateli, analitiki i razrabotchiki uznajut, kak izvlekat eti unikalnye dannye, ispolzuja kod na Python, Jupyter Notebook ili kontejnery Docker. Snachala vy poznakomites s funktsionalom samykh populjarnykh sotsialnykh setej (Twitter, Facebook, LinkedIn, Instagram), veb-stranits, blogov i lent, elektronnoj pochty i GitHub. Zatem pristupite k analizu dannykh na primere Twitter. Prochitajte etu knigu, chtoby * Uznat o sovremennom landshafte sotsialnykh setej * Nauchitsja ispolzovat Docker, chtoby legko operirovat kodami, privedennymi v knige; * Uznat, kak adaptirovat i postavljat kod v otkrytyj repozitorij GitHub; * Nauchitsja analizirovat sobiraemye dannye s ispolzovaniem vozmozhnostej Python 3; * Osvoit prodvinutye priemy analiza, takie kak TFIDF, kosinusnoe skhodstvo, analiz slovosochetanij, opredelenie klika i raspoznavanie obrazov; * Uznat, kak sozdavat krasivye vizualizatsii dannykh s pomoschju Python i JavaScript.

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers. In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter. Get a straightforward synopsis of the social web landscape Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook Adapt and contribute to the code’s open source GitHub repository Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition Build beautiful data visualizations with Python and JavaScript toolkits