WCTC Library: Intro to Data Analysis: Books &amp; eBooks

Selected eBooks

Soft Computing and Machine Learning with Python by Zoran Gacovski (Editor) Soft computing and machine learning with python examines various aspects of machinelearning with python with a detailed information on soft computing. It includes fourdifferent sections, where section 1 and 2 are dedicated towards soft computing theoryand machine learning techniques and on the other hand section 3 and 4 are dedicatedto the details of python language and machine learning with python. The book providesthe reader with the insights into the development of python and machine learning, soas to understand the classification multigraph models of secondary RNA structure usinggraph-theoretic descriptors.
Publication Date: 2019
Find It Fast by Robert Berkman Go beyond Google to mine big data and social media Author Robert Berkman gives expert advice on how to search the internet to locate the best information sources, how to find and utilize the professionals behind those sources, and how to combine these techniques to complete an information search on any subject. This fully updated 6th edition includes how to search beyond Google, leveraging big data in the search process, and how to search the social web. Readers will also find expert advice on how to know if a site is a trusted source; understanding how and why sources differ; using precision search strategies and taming information overload; and finding, evaluating, and identifying experts. Whether it's consumer advice, information for a job or project, facts for starting a new business, or answers to questions on obscure topics, Find It Fast is the perfect resource for learning to hone one's internet searching skills.
Publication Date: 2015
Unstructured Data Analysis by Matthew Windham Unlock the power of regular expressions and entity resolution to transform your analytics projects Unstructured data is the most voluminous form of data in the world, and analysts rarely receive it in perfect condition for processing. In other words, textual data needs to be cleaned, transformed, and enhanced before value can be derived from it. Unstructured Data Analysis: Entity Resolution and Regular Expressions in SAS® shows SAS programmers of virtually all skill levels how to harness the robust power of regular expressions and entity resolution within the SAS programming language for a wide array of everyday applications of unstructured data analyses. This book uses a practical, examples-based approach to present techniques for unstructured data processing and provides the foundational information needed to perform advanced applications. Beginning with regular expressions in SAS, readers will progress to learning the building blocks of Entity Resolution Analytics including entity extraction, ETL, entity resolution, network mapping and analysis, and management concepts. Filled with motivational examples and helpful guidelines, this book is a critical reference for every analytics professional who works with unstructured data.
Publication Date: 2018