Download or read book Open Source Software for Statistical Analysis of Big Data Emerging Research and Opportunities written by Segall, Richard S. and published by IGI Global. This book was released on 2020-02-21 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.
Download or read book Data Analysis with Open Source Tools written by Philipp K. Janert and published by "O'Reilly Media, Inc.". This book was released on 2010-11-11 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Download or read book Open Source Software in Life Science Research written by Lee Harland and published by Elsevier. This book was released on 2012-10-31 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: The free/open source approach has grown from a minor activity to become a significant producer of robust, task-orientated software for a wide variety of situations and applications. To life science informatics groups, these systems present an appealing proposition - high quality software at a very attractive price. Open source software in life science research considers how industry and applied research groups have embraced these resources, discussing practical implementations that address real-world business problems.The book is divided into four parts. Part one looks at laboratory data management and chemical informatics, covering software such as Bioclipse, OpenTox, ImageJ and KNIME. In part two, the focus turns to genomics and bioinformatics tools, with chapters examining GenomicsTools and EBI Atlas software, as well as the practicalities of setting up an 'omics' platform and managing large volumes of data. Chapters in part three examine information and knowledge management, covering a range of topics including software for web-based collaboration, open source search and visualisation technologies for scientific business applications, and specific software such as DesignTracker and Utopia Documents. Part four looks at semantic technologies such as Semantic MediaWiki, TripleMap and Chem2Bio2RDF, before part five examines clinical analytics, and validation and regulatory compliance of free/open source software. Finally, the book concludes by looking at future perspectives and the economics and free/open source software in industry. - Discusses a broad range of applications from a variety of sectors - Provides a unique perspective on work normally performed behind closed doors - Highlights the criteria used to compare and assess different approaches to solving problems
Download or read book Data Mining with Rattle and R written by Graham Williams and published by Springer Science & Business Media. This book was released on 2011-08-04 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.
Download or read book Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning written by Segall, Richard S. and published by IGI Global. This book was released on 2022-01-07 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.
Download or read book Managerial Perspectives on Intelligent Big Data Analytics written by Sun, Zhaohao and published by IGI Global. This book was released on 2019-02-22 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce.
Download or read book Practical Data Analysis written by Hector Cuesta and published by Packt Publishing Ltd. This book was released on 2016-09-30 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.
Download or read book Open Source Software for Statistical Analysis of Big Data written by Richard Segall and published by Engineering Science Reference. This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book explores topics in the field of open source software for big data"--
Download or read book Big Data and Analytics written by Vincenzo Morabito and published by Springer. This book was released on 2015-01-31 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics.
Download or read book Networking for Big Data written by Shui Yu and published by CRC Press. This book was released on 2015-07-28 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications.The book is divided into four sections: introduction to Big Data,
Download or read book Mobility Patterns Big Data and Transport Analytics written by Constantinos Antoniou and published by Elsevier. This book was released on 2018-11-27 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility 'structural' analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data's impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility 'structural' analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data's impact on mobility, and an introduction to the tools necessary to apply new techniques. - Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics - Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends - Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field - Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach - Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
Download or read book BIG DATA ANALYTICS TECHNIQUES TOOLS AND APPLICATIONS written by HEMANT SINGH SENGAR SAURABH ASHWINIKUMAR DAVE BALACHANDAR RAMALINGAM OM GOEL and published by DeepMisti Publication. This book was released on 2024-10-18 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Big Data Analytics: Techniques, Tools, and Applications, is conceived to bridge the gap between emerging technological advancements in data analytics and their strategic application across industries. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of big data technologies and their integration into business practices. From foundational theories to advanced applications, we delve into the critical aspects that drive successful innovation in data-driven environments. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and big data adoption to the strategic management of innovation. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that big data analytics and management play in shaping the future of businesses. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how big data technologies can be harnessed to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will define the future of data-driven enterprises. Thank you for joining us on this journey. Authors
Download or read book Data Science in Education Using R written by Ryan A. Estrellado and published by Routledge. This book was released on 2020-10-26 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
Download or read book Effective Big Data Management and Opportunities for Implementation written by Singh, Manoj Kumar and published by IGI Global. This book was released on 2016-06-20 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.
Download or read book Big Data Analytics From Data to Discovery written by Dr.K.Varada Rajkumar and published by Leilani Katie Publication. This book was released on 2024-07-13 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr.K.Varada Rajkumar, Associate Professor, Department of Computer Science and Engineering (AIML), MLR Institute of Technology, Hyderabad, Telangana, India. Vikram Pasupuleti, Software Developer, MS in Computer Technology, School of Technology, Eastern Illinois University, Charleston, Illinois, USA. Bharadwaj Thuraka, Software Developer, Master of Science, Information Systems, Northwest Missouri State University, Maryville, Missouri, USA. Dr.Saiteja Malisetty, Ph.D in Computing & Information Science, College of Information Science and Technology, University of Nebraska, Omaha, Nebraska, USA. Chandra Shikhi Kodete, Software Engineer, MS in Computer Technology, School of Technology, Eastern Illinois University, Charleston, Illinois, USA.
