Data Modeling Design category: 403 books

Gain hands-on experience with SPARQL, the RDF query language that’s bringing new possibilities to semantic web, linked data, and big data projects. This updated and expanded edition shows you how to use SPARQL 1.1 with a variety of tools to retrieve, manipulate, and federate data from the public...

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single...

A practical and fast-paced guide that gives you all the information you need to start developing charts from your data.Learning QlikView Data Visualization is for anybody interested in performing powerful data analysis and crafting insightful data visualization, independent of any previous knowledge of QlikView. Experience with spreadsheet software will help you understand QlikView functions.

Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge....

This book includes step-by-step examples and case studies that teach users the many power tricks for analyzing data in Excel. These are tips honed by Bill Jelen, “MrExcel,” and Oz do Soleil during their careers run as a financial analyst charged with taking mainframe data and turning it into useful...

This report examines the important shifts in data products. Drawing from diverse examples, including iTunes, Google's self-driving car, and patient monitoring, author Mike Loukides explores the "disappearance" of data, the power of combining data, and the difference between discovery and recommendation....

In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems...

Big data has more disruptive potential than any information technology developed in the past 40 years. As author Jeffrey Needham points out in this revealing book, big data can provide unprecedented visibility into the operational efficiency of enterprises and agencies. Disruptive Possibilities...

The growing presence of smart phones and smart devices has caused significant changes to wireless networks. With the ubiquity of these technologies, there is now increasingly more available data for mobile operators to utilize. Big Data Applications in the Telecommunications Industry is a comprehensive...

SAP Master Data Governance (SAP MDG) offers centralized governance for master data domains like Financials, Supplier, Customer, and Material. With this application, you can manage master data that is ready and fit to use within SAP environments. Release 7.0 of SAP MDG brought the option of using...

Unleash the power and flexibility of the Bayesian framework About This Book Simplify the Bayes process for solving complex statistical problems using Python; Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises; Learn...

Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book Get to the grips with the latest version of Apache Spark Utilize Spark's machine learning library to implement predictive analytics Leverage Spark's powerful tools to load, analyze,...

Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide About This Book Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development Develop a set of practical...

This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages...