Bøker av Q. Ethan Mccallum

Its tough to argue with R as a high-quality, cross-platform, open source statistical software productunless youre in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. Youll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they dont.With these packages, you can overcome Rs single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address Rs memory barrier.Snow: works well in a traditional cluster environmentMulticore: popular for multiprocessor and multicore computersParallel: part of the upcoming R 2.14.0 releaseR+Hadoop: provides low-level access to a popular form of cluster computingRHIPE: uses Hadoops power with Rs language and interactive shellSegue: lets you use Elastic MapReduce as a backend for lapply-style operations

What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how theyve recovered from nasty data problems.From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it.Among the many topics covered, youll discover how to:Test drive your data to see if its ready for analysisWork spreadsheet data into a usable formHandle encoding problems that lurk in text dataDevelop a successful web-scraping effortUse NLP tools to reveal the real sentiment of online reviewsAddress cloud computing issues that can impact your analysis effortAvoid policies that create data analysis roadblocksTake a systematic approach to data quality analysis