Originally posted on: http://geekswithblogs.net/TATWORTH/archive/2014/10/08/orsquoreilly-half-price-on-early-release-books-until-0500-16october2014.aspx
Until 05:00 16/October/2014, O’Reilly are offering 50% off their Early Release e-books at http://shop.oreilly.com/category/early-release.do?sortby=publicationDate&page=all&code=WKERRE
“With the recent exhaustion of IPv4 in North America, Asia, and Europe, and Latin America it’s official: IPv4 is on life-support and is rapidly on its way to becoming a legacy protocol. Meanwhile, IPv6 adoption is surging. More than half of the traffic on the nation’s largest mobile network is over IPv6 while a social networking giant plans to no longer use IPv4 internally within 18 months. No matter where you work, as a networking professional, IPv6 adoption is in your future.”
“If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. The new edition switches over to Pandas for data processing. It also includes new chapters on multiple regression, survival analysis, missing value imputation and resampling.”
“Learn the algorithms and tools you need to build MapReduce applications with Hadoop for processing gigabyte, terabyte, or petabyte-sized datasets on clusters of commodity hardware. With this practical book, Author Mahmoud Parsian, head of the big data team at Illumina, takes you step-by-step through the design of machine-learning algorithms, such as Naive Bayes and Markov Chain, and shows you how apply them to clinical and biological datasets, using MapReduce design patterns.”
