Automated Data Collection with R: A Practical Guide to Web by Simon Munzert, Christian Rubba, Dominic Nyhuis, Peter Meiner

By Simon Munzert, Christian Rubba, Dominic Nyhuis, Peter Meiner

A arms on consultant to internet scraping and textual content mining for either newbies and skilled clients of R Introduces primary strategies of the most structure of the net and databases and covers HTTP, HTML, XML, JSON, SQL.

Provides simple recommendations to question net records and knowledge units (XPath and usual expressions). an intensive set of routines are awarded to steer the reader via each one procedure.

Explores either supervised and unsupervised strategies in addition to complicated recommendations corresponding to facts scraping and textual content administration. Case stories are featured all through besides examples for every strategy offered. R code and suggestions to routines featured within the ebook are supplied on a assisting web site.

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

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In summary, business leadership needs to lead the big data initiative, to step up and make big data a top business mandate. If your business leaders don’t take the lead in identifying where and how to integrate big data into your business models, then you risk being disintermediated in a marketplace where more agile, hungrier competitors are learning that data and analytics can yield compelling competitive differentiation. Homework Assignment Use the following exercises to apply what you learned in this chapter.

Tip: Don’t worry about whether or not you have the data sources you need to derive the insights you want (yet). Exercise #3: Brainstorm and write down data sources that might be useful in uncovering those key insights. Look both internally and externally for interesting data sources that might be useful. Tip: Think outside the box and imagine that you could access any data source in the world. CHAPTER 2 Big Data Business Model Maturity Index Organizations do not understand how far big data can take them from a business transformation perspective.

Phase 4: Data Monetization. In the Data Monetization phase, organizations leverage the customer, product, and operational insights to create new sources of revenue. This could include selling data—or insights—into new markets (a cellular phone provider selling customer behavioral data to advertisers), integrating analytics into products and services to create 5 6 Part I ■ Business Potential of Big Data “smart” products, or re-packaging customer, product, and operational insights to create new products and services, to enter new markets, and/ or to reach new audiences.

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