Data Mining, Statistics,

The Ultimate Guide to Data Science and Analytics

Learn Regression Techniques, Data Mining, Forecasting, Text Mining using R

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.

Learn Regression Techniques, Data Mining, Forecasting, Text Mining using R

Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications.

This book is not just another theoretical text about statistics or data mining. No, instead it is aimed for database administrators who want to use SQL or bolster their understanding of statistics to support data mining and customer relationship management analytics.

With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation.