Here’s a compilation of 10 analytics/ big data books by Indian authors that we believe have made their mark worldwide. We do celebrate the authors but more than that we celebrate their creations in these fabulous books.
Also included is their amazon web link and the synopsis.
Web Analytics 2.0 is the perfect follow-up to the bestseller Web Analytics: An Hour a Day as it expands upon the lessons learned, delves into more advanced techniques and covers the absolute latest web analytics tools and methods. Because it is agnostic regarding web analytics tools and will cover such hot topics as measuring video content, blogs, Flash content and social media, it will have exceptionally wide appeal and be a must-read for all web analytics practitioners, including those who read the first book.
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If you’re an R developer looking to harness the power of big data analytics with Hadoop, then this book tells you everything you need to integrate the two. You’ll end up capable of building a data analytics engine with huge potential. Approach Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop. Who this book is written for This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R.
This book is a comprehensive coverage on the concepts and practice of Big Data, Hadoop and Analytics. From the Do It Yourself steps and guidelines to set up a Hadoop Cluster to the deeper understanding of concepts and ample time-tested hands-on practice exercises on the concepts learned, this one book has it all! Big Data and Analytics is a term used for massive mounds of structured, semi-structured and unstructured data that has the potential to be mined for information.
Bringing a practitioner’s view to big data analytics, this work examines the drivers behind big data, postulates a set of use cases, identifies sets of solution components, and recommends various implementation approaches. This work also addresses and thoroughly answers key questions on this emerging topic, including What is big data and how is it being used? How can strategic plans for big data analytics be generated? and How does big data change analytics architecture? The author, who has more than 20 years of experience in information management architecture and delivery, has drawn the material from a large breadth of workshops and interviews with business and information technology leaders, providing readers with the latest in evolutionary, revolutionary, and hybrid methodologies of moving forward to the brave new world of big data.
When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn’t well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to introduce these technologies and demonstrate their use in detail. An indispensable resource for data scientists and others who must scale traditional analytics tools and applications to Big Data, it illuminates these new alternatives at every level, from architecture all the way down to code. Dr. Vijay Srinivas Agneeswaran shows how to evaluate and choose the right tools and then reengineer your solutions and products to work far more effectively in Big Data environments. Agneeswaran explains the Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management and the analysis of both performance and accuracy.
Business Analytics: An Application Focus Business Analytics refers to various categories of analytical approaches for modelling different business situations and arriving at solutions and strategies for optimal decision making in marketing, finance, operations, organizational behaviour and other managerial processes.
This book, Applied Big Data Analytics is unique among those big-data books because of its great depth and technical approach. It consists of four themes: i)Basics of big data analytics, ii)Tools, techniques and software for analytics iii) Big data analytics in health care sector iv) Big data analytics in industries, bioinformatics and life sciences. With 26 chapters spread over four sections the book demonstrates further the emerging issues and approaches in various areas of Big Data application The aim of this book is to be accessible to researchers, graduate students, and to application-driven practitioners who work in data science and related fields. It is a timely and urgently needed publication, and it provides the most up-to-date, crucial, and practical information for big data management, technologies, and applications. Contributed by experts, it is a must-read book for statisticians, data analysts, IT students, researchers, scholars, and workers with big data in mind. Specialty of this book is that it covers important issues of big data from fundamental knowledge to application in various sectors. With an emphasis on real-life implementation of Big Data technologies, this book will provide bold vision from leading innovators across the data-driven spectrum. The latest tools and trends may help gaining fresh insights and strategic momentum to grow and respond to the analytical requirements.
The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations.
The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life.
Increased customer focus and the exponential increase in the volume, velocity and variety of data and the unrelenting need to stay one step ahead of competition has sharpened focus on using analytics within organizations. With business pressing more on the need to enhance their analytics capability for predicting and optimizing outcomes, this book offers practical guidance on the application of analytics for driving business decisions by adopting a customer centric approach to marketing.
As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of “Web intelligence”, as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected.
Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.