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Author: Dr. Mainak Banerjee
Publisher: Academic Guru Publishing House
ISBN: 8119338804
Category : Study Aids
Languages : en
Pages : 206
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Book Description
Data Analytics - Principles, Tools and Practices is a comprehensive guide that explores the field of data analytics, providing a thorough understanding of its principles, methodologies, and tools. Written by experts in the field, this book serves as an invaluable resource for students, professionals, and researchers seeking to gain proficiency in data analytics and leverage its potential to drive business success. The book takes a systematic approach to data analytics, covering the entire analytics lifecycle from data collection and preprocessing to analysis, modelling, and interpretation of results. It offers insights into various analytical techniques, including descriptive, diagnostic, predictive, and prescriptive analytics, equipping readers with the necessary knowledge to extract meaningful insights from complex datasets. With a focus on practical applications, the authors present real-world case studies and examples, demonstrating how data analytics has revolutionized finance, healthcare, marketing, and manufacturing industries. The book also addresses ethical considerations and data security, emphasizing the responsible handling of data and privacy protection. Throughout the chapters, readers are introduced to various tools and technologies commonly used in data analytics, including statistical software, programming languages, data visualization tools, and machine learning algorithms. The authors provide step-by-step instructions and code snippets to facilitate hands-on learning and practical implementation. Data Analytics - Principles, Tools and Practices empowers readers with the necessary skills and knowledge to navigate the complex landscape of data analytics and make informed decisions based on data-driven insights. Whether you are a student, professional, or researcher, this book will be your comprehensive guide to mastering data analytics principles, tools, and practices.
Author: Dr. Mainak Banerjee
Publisher: Academic Guru Publishing House
ISBN: 8119338804
Category : Study Aids
Languages : en
Pages : 206
Get Book Here
Book Description
Data Analytics - Principles, Tools and Practices is a comprehensive guide that explores the field of data analytics, providing a thorough understanding of its principles, methodologies, and tools. Written by experts in the field, this book serves as an invaluable resource for students, professionals, and researchers seeking to gain proficiency in data analytics and leverage its potential to drive business success. The book takes a systematic approach to data analytics, covering the entire analytics lifecycle from data collection and preprocessing to analysis, modelling, and interpretation of results. It offers insights into various analytical techniques, including descriptive, diagnostic, predictive, and prescriptive analytics, equipping readers with the necessary knowledge to extract meaningful insights from complex datasets. With a focus on practical applications, the authors present real-world case studies and examples, demonstrating how data analytics has revolutionized finance, healthcare, marketing, and manufacturing industries. The book also addresses ethical considerations and data security, emphasizing the responsible handling of data and privacy protection. Throughout the chapters, readers are introduced to various tools and technologies commonly used in data analytics, including statistical software, programming languages, data visualization tools, and machine learning algorithms. The authors provide step-by-step instructions and code snippets to facilitate hands-on learning and practical implementation. Data Analytics - Principles, Tools and Practices empowers readers with the necessary skills and knowledge to navigate the complex landscape of data analytics and make informed decisions based on data-driven insights. Whether you are a student, professional, or researcher, this book will be your comprehensive guide to mastering data analytics principles, tools, and practices.
Author: Gaurav Aroraa
Publisher: BPB Publications
ISBN: 9388511956
Category : Antiques & Collectibles
Languages : en
Pages : 481
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Book Description
A Complete Data Analytics Guide for Learners and Professionals. KEY FEATURES ● Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. ● Dive into Machine Learning, its tools, and applications. ● Coverage of applications of Big Data, Data Analysis, and Business Intelligence. DESCRIPTION These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book “Data Analytics: Principles, Tools, and Practices” can be considered a handbook or a guide for professionals who want to start their journey in the field of data science. The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples. After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science. WHAT YOU WILL LEARN ● Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. ● Learn to manage data warehousing with real time transaction processing. ● Explore various machine learning techniques that apply to data analytics. ● Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry. ● Acquaint yourself with Big Data tools and statistical techniques for machine learning. WHO THIS BOOK IS FOR IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book. TABLE OF CONTENTS 1. Database Management System 2. Online Transaction Processing and Data Warehouse 3. Business Intelligence and its deeper dynamics 4. Introduction to Data Visualization 5. Advanced Data Visualization 6. Introduction to Big Data and Hadoop 7. Application of Big Data Real Use Cases 8. Application of Big Data 9. Introduction to Machine Learning 10. Advanced Concepts to Machine Learning 11. Application of Machine Learning
Author: Michael Watson
Publisher: Pearson Education
ISBN: 013340742X
Category : Business & Economics
Languages : en
Pages : 253
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Book Description
Analytics and Big Data Demystified, The up-to-the-minute introduction for every manager, Everything you need to know to get results!, Concepts, applications, tools, techniques, and pitfalls to avoid, How to derive more value from tools and data you already own, Want to start leveraging analytics and Big Data for profit? Managerial Analytics is your ideal first resource. Whatever your industry or management role, this up-to-date guide will help you get started fast, get started right, and quickly start driving value. Book jacket.
Author: James Warren
Publisher: Simon and Schuster
ISBN: 1638351104
Category : Computers
Languages : en
Pages : 481
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Book Description
Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth
Author: Dean Abbott
Publisher: John Wiley & Sons
ISBN: 1118727967
Category : Computers
Languages : en
Pages : 471
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Book Description
Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.
Author: Marc J. Schniederjans
Publisher: Pearson Education
ISBN: 0133989402
Category : Business & Economics
Languages : en
Pages : 353
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Book Description
Responding to a shortage of effective content for teaching business analytics, this text offers a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives. Business Analytics Principles, Concepts, and Applications with SAS offers a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making. Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning. Unlike most competitive guides, Business Analytics Principles, Concepts, and Applications with SAS demonstrates the use of SAS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself.
Author: Whig, Pawan
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 334
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Book Description
In today's data-driven era, the persistent gap between theoretical understanding and practical implementation in data science poses a formidable challenge. As we navigate through the complexities of harnessing data, deciphering algorithms, and unleashing the potential of modeling techniques, the need for a comprehensive guide becomes increasingly evident. This is the landscape explored in Practical Applications of Data Processing, Algorithms, and Modeling. This book is a solution to the pervasive problem faced by aspiring data scientists, seasoned professionals, and anyone fascinated by the power of data-driven insights. From the web of algorithms to the strategic role of modeling in decision-making, this book is an effective resource in a landscape where data, without proper guidance, risks becoming an untapped resource. The objective of Practical Applications of Data Processing, Algorithms, and Modeling is to address the pressing issue at the heart of data science – the divide between theory and practice. This book seeks to examine the complexities of data processing techniques, algorithms, and modeling methodologies, offering a practical understanding of these concepts. By focusing on real-world applications, the book provides readers with the tools and knowledge needed to bridge the gap effectively, allowing them to apply these techniques across diverse industries and domains. In the face of constant technological advancements, the book highlights the latest trends and innovative approaches, fostering a deeper comprehension of how these technologies can be leveraged to solve complex problems. As a practical guide, it empowers readers with hands-on examples, case studies, and problem-solving scenarios, aiming to instill confidence in navigating data challenges and making informed decisions using data-driven insights.
Author: Peter Ghavami, Ph.d.
Publisher: Createspace Independent Publishing Platform
ISBN: 9781519559722
Category :
Languages : en
Pages : 202
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Book Description
Data is the new Gold and Analytics is the machinery to mine, mold and mint it. Data analytics has become core to business and decision making. The rapid increase in data volume, velocity and variety, known as big data, offers both opportunities and challenges. While open source solutions to store big data, like Hadoop and NoSQL offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Organizations that are launching big data initiatives face significant challenges for managing this data effectively. In this book, the author has collected best practices from the world's leading organizations who have successfully implemented big data platforms. He offers the latest techniques and methods for managing big data effectively. The book offers numerous policies, strategies and recipes for managing big data. It addresses many issues that are prevalent with data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. Topics that cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and information technology leaders who are implementing big data platforms in their organizations.
Author: Sun, Zhaohao
Publisher: IGI Global
ISBN: 1668459612
Category : Business & Economics
Languages : en
Pages : 449
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Book Description
Socioeconomic development has drawn increasing attention in academia, industries, and governments. The relationship between big data and its technologies and socioeconomic development has drawn certain attention in academia. Socioeconomic development depends not only on big data, but also on big data technologies. However, the relationship between big data and socioeconomic development is not adequately covered in current research. The Handbook of Research on Driving Socioeconomic Development With Big Data provides an original and innovative understanding of and insight into how the proposed theories, technologies, and methodologies of big data can improve socioeconomic development and sustainable development in terms of business and services, healthcare, the internet of everything, sharing economy, and more. Covering topics such as corporate social responsibility, management applications, and process mining, this major reference work is an excellent resource for data scientists, business leaders and executives, IT professionals, government officials, economists, sociologists, librarians, students, researchers, and academicians.
Author: Peter Ghavami
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110664321
Category : Business & Economics
Languages : en
Pages : 185
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Book Description
Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.
