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Showing posts with label R. Show all posts
Showing posts with label R. Show all posts

02 September, 2020

Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining

Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining
Simon MunzertChristian RubbaPeter MeißnerDominic Nyhuis
A hands on guide to web scraping and text mining for both beginners and experienced users of R
  • Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL.
  • Provides basic techniques to query web documents and data sets (XPath and regular expressions).
  • An extensive set of exercises are presented to guide the reader through each technique.
  • Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management.
  • Case studies are featured throughout along with examples for each technique presented.
  • R code and solutions to exercises featured in the book are provided on a supporting website.
Categories:
Computers\\Programming: Programming Languages
Year:
2020
Edition:
1
Publisher:
Wiley
Language:
english
Pages:
472 / 477
ISBN 13:
9781118834817
ISBN:
111883481X
File:
PDF, 4.65 MB
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28 August, 2020

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Peter BruceAndrew BrucePeter Gedeck
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:
• Why exploratory data analysis is a key preliminary step in data science
• How random sampling can reduce bias and yield a higher-quality dataset, even with big data
• How the principles of experimental design yield definitive answers to questions
• How to use regression to estimate outcomes and detect anomalies
• Key classification techniques for predicting which categories a record belongs to
• Statistical machine learning methods that "learn" from data
• Unsupervised learning methods for extracting meaning from unlabeled data
Categories:
Computers\\Programming: Libraries API
Year:
2020
Edition:
2
Publisher:
O'Reilly Media
Language:
english
Pages:
368 / 363
ISBN 13:
9781492072942
ISBN:
149207294X
File:
PDF, 15.98 MB
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