Applied Data Analytics Principles And Applications

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Applied Data Analytics - Principles and Applications

Author : Johnson I. Agbinya
Publisher : CRC Press
Page : 369 pages
File Size : 52,8 Mb
Release : 2022-09-01
Category : Computers
ISBN : 9781000795530

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Applied Data Analytics - Principles and Applications by Johnson I. Agbinya Pdf

The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.

Applied Predictive Analytics

Author : Dean Abbott
Publisher : John Wiley & Sons
Page : 456 pages
File Size : 54,8 Mb
Release : 2014-03-31
Category : Computers
ISBN : 9781118727690

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Applied Predictive Analytics by Dean Abbott Pdf

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.

Data Analytics: Principles, Tools, and Practices

Author : Gaurav Aroraa,Chitra Lele,Dr. Munish Jindal
Publisher : BPB Publications
Page : 481 pages
File Size : 40,9 Mb
Release : 2022-01-24
Category : Antiques & Collectibles
ISBN : 9789388511957

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Data Analytics: Principles, Tools, and Practices by Gaurav Aroraa,Chitra Lele,Dr. Munish Jindal Pdf

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

Data Science for Business

Author : Foster Provost,Tom Fawcett
Publisher : "O'Reilly Media, Inc."
Page : 414 pages
File Size : 47,5 Mb
Release : 2013-07-27
Category : Computers
ISBN : 9781449374280

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Data Science for Business by Foster Provost,Tom Fawcett Pdf

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Handbook of Statistical Analysis and Data Mining Applications

Author : Robert Nisbet,Gary Miner,Ken Yale
Publisher : Elsevier
Page : 822 pages
File Size : 55,9 Mb
Release : 2017-11-09
Category : Mathematics
ISBN : 9780124166455

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Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet,Gary Miner,Ken Yale Pdf

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Business Analytics Principles, Concepts, and Applications with SAS

Author : Marc J. Schniederjans,Dara G. Schniederjans,Christopher M. Starkey
Publisher : Pearson Education
Page : 353 pages
File Size : 51,5 Mb
Release : 2014-09-12
Category : Business & Economics
ISBN : 9780133989571

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Business Analytics Principles, Concepts, and Applications with SAS by Marc J. Schniederjans,Dara G. Schniederjans,Christopher M. Starkey Pdf

Learn everything you need to know to start using business analytics and integrating it throughout your organization. Business Analytics Principles, Concepts, and Applications with SAS brings together 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. They offer 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, this text demonstrates the use of SAS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself. Business Analytics Principles, Concepts, and Applications with SAS will be a valuable resource for all beginning-to-intermediate level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.

Intelligent Systems and Applications

Author : Kohei Arai
Publisher : Springer Nature
Page : 897 pages
File Size : 42,7 Mb
Release : 2024-02-10
Category : Technology & Engineering
ISBN : 9783031477218

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Intelligent Systems and Applications by Kohei Arai Pdf

The book is a unique collection of studies involving intelligent systems and applications of artificial intelligence in the real world to provide solutions to most vexing problems. IntelliSys received an overwhelming 605 papers which were put under strict double-blind peer-review for their novelty, originality and exhaustive research. Finally, 227 papers were sieved and chosen to be published in the proceedings. This book is a valuable collection of all the latest research in the field of artificial intelligence and smart systems. It provides a ready-made resource to all the readers keen on gaining information regarding the latest trends in intelligent systems. It also renders a sneak peek into the future world governed by artificial intelligence.

Applied Data Science

Author : Martin Braschler,Thilo Stadelmann,Kurt Stockinger
Publisher : Springer
Page : 465 pages
File Size : 45,8 Mb
Release : 2019-06-13
Category : Computers
ISBN : 9783030118211

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Applied Data Science by Martin Braschler,Thilo Stadelmann,Kurt Stockinger Pdf

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Information Technology for Education, Science, and Technics

Author : Emil Faure,Olena Danchenko,Maksym Bondarenko,Yurii Tryus,Constantine Bazilo,Grygoriy Zaspa
Publisher : Springer Nature
Page : 772 pages
File Size : 48,9 Mb
Release : 2023-06-17
Category : Technology & Engineering
ISBN : 9783031354670

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Information Technology for Education, Science, and Technics by Emil Faure,Olena Danchenko,Maksym Bondarenko,Yurii Tryus,Constantine Bazilo,Grygoriy Zaspa Pdf

This book gathers selected high-quality full-text papers presented at the VI International Scientific and Practical Conference on Information Technology for Education, Science and Technics (ITEST 2022). The book deals with issues related to mathematical and computer modeling of physical, chemical, and economic processes, with information security, as well as the use of information and communication technology in scientific research, automation of technological processes, and management of complex systems. In this book, the authors explore various aspects of the development of information technology and systems and its application in education, science, engineering, economics, and management. A part of the book is devoted to the application of information and communication technology in higher education, in particular, the creation and implementation of scientific and educational resources in higher education institutions as part of the process of education digital transformation.

Applying Data Science

Author : Arthur K. Kordon
Publisher : Springer Nature
Page : 511 pages
File Size : 44,8 Mb
Release : 2020-09-12
Category : Computers
ISBN : 9783030363758

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Applying Data Science by Arthur K. Kordon Pdf

This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.

Distributed Computing in Big Data Analytics

Author : Sourav Mazumder,Robin Singh Bhadoria,Ganesh Chandra Deka
Publisher : Springer
Page : 162 pages
File Size : 43,9 Mb
Release : 2017-08-29
Category : Computers
ISBN : 9783319598345

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Distributed Computing in Big Data Analytics by Sourav Mazumder,Robin Singh Bhadoria,Ganesh Chandra Deka Pdf

Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use. This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principles of distributed computing are used in those situations. Practitioners and researchers alike will find this book a valuable tool for their work, helping them to select the appropriate technologies, while understanding the inherent strengths and drawbacks of those technologies.

Foundations of Data Intensive Applications

Author : Supun Kamburugamuve,Saliya Ekanayake
Publisher : John Wiley & Sons
Page : 416 pages
File Size : 40,5 Mb
Release : 2021-08-11
Category : Computers
ISBN : 9781119713012

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Foundations of Data Intensive Applications by Supun Kamburugamuve,Saliya Ekanayake Pdf

PEEK “UNDER THE HOOD” OF BIG DATA ANALYTICS The world of big data analytics grows ever more complex. And while many people can work superficially with specific frameworks, far fewer understand the fundamental principles of large-scale, distributed data processing systems and how they operate. In Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood, renowned big-data experts and computer scientists Drs. Supun Kamburugamuve and Saliya Ekanayake deliver a practical guide to applying the principles of big data to software development for optimal performance. The authors discuss foundational components of large-scale data systems and walk readers through the major software design decisions that define performance, application type, and usability. You???ll learn how to recognize problems in your applications resulting in performance and distributed operation issues, diagnose them, and effectively eliminate them by relying on the bedrock big data principles explained within. Moving beyond individual frameworks and APIs for data processing, this book unlocks the theoretical ideas that operate under the hood of every big data processing system. Ideal for data scientists, data architects, dev-ops engineers, and developers, Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood shows readers how to: Identify the foundations of large-scale, distributed data processing systems Make major software design decisions that optimize performance Diagnose performance problems and distributed operation issues Understand state-of-the-art research in big data Explain and use the major big data frameworks and understand what underpins them Use big data analytics in the real world to solve practical problems

Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022

Author : Aboul Ella Hassanien,Václav Snášel,Mincong Tang,Tien-Wen Sung,Kuo-Chi Chang
Publisher : Springer Nature
Page : 844 pages
File Size : 49,7 Mb
Release : 2022-11-17
Category : Technology & Engineering
ISBN : 9783031206016

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Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022 by Aboul Ella Hassanien,Václav Snášel,Mincong Tang,Tien-Wen Sung,Kuo-Chi Chang Pdf

This proceedings book constitutes the refereed proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics (AISI 2021), which took place in Cairo, Egypt, during November 20–22, 2022, and is an international interdisciplinary conference that presents a spectrum of scientific research on all aspects of informatics and intelligent systems, technologies, and applications.

Technology, Business, Innovation, and Entrepreneurship in Industry 4.0

Author : Teresa Guarda,Cristina Fernandes,Maria Fernanda Augusto
Publisher : Springer Nature
Page : 412 pages
File Size : 45,6 Mb
Release : 2022-12-15
Category : Technology & Engineering
ISBN : 9783031179600

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Technology, Business, Innovation, and Entrepreneurship in Industry 4.0 by Teresa Guarda,Cristina Fernandes,Maria Fernanda Augusto Pdf

This book presents the most recent innovations, trends, and challenges in several aspects of Industry 4.0, including the key technologies and business impacts. The book is relevant to a variety of stakeholders due to Industry 4.0’s broad impact in many fields. Topics include digital workplace solutions for employee engagement, entrepreneurship and innovation, and Blockchain for business security. The authors cover Industry 4.0 both from a theoretical and applicable standpoint.

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry

Author : Chkoniya, Valentina
Publisher : IGI Global
Page : 653 pages
File Size : 44,7 Mb
Release : 2021-06-25
Category : Computers
ISBN : 9781799869863

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Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry by Chkoniya, Valentina Pdf

The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.