Big Data

All Indian Reprints of O'Reilly are Printed in Grayscale. If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis

All Indian Reprints of O'Reilly are Printed in Grayscale. Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show...

All Indian Reprints of O'Reilly are printed in Grayscale Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.

All Indian Reprints of O'Reilly are printed in Grayscale The world's leading nations are standing at the precipice of the next great manufacturing revolution—one in which the Industrial Internet of Things (IIoT) and big data analytics are already making a major impact. In this O'Reilly report, author Li Ping Chu shares insight from industry experts and explores recent manufacturing initiatives in China, Germany, and the US to provide a succinct, hype-free overview of related technologies and applications.

All Indian Reprints of O'Reilly are printed in Grayscale Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.

All Indian Reprints of O'Reilly are printed in Grayscale. Gain hands-on experience with SPARQL, the RDFquery language that’s bringing new possibilities to semantic web, linked data,and big data projects. This updated and expanded edition shows you how to useSPARQL 1.1 with a variety of tools to retrieve, manipulate, and federate datafrom the public web as well as from private sources.

Five or six years ago, analysts working with big datasets made queries and got the results back overnight. The data world was revolutionized a few years ago when Hadoop and other tools made it possible to get the results from queries in minutes. But the revolution continues. Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.