Download or read book Predictive Analysis on Large Data for Actionable Knowledge Emerging Research and Opportunities written by Usman, Muhammad and published by IGI Global. This book was released on 2018-01-26 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analysis forms the basis of many modes of research ranging from scientific discoveries to governmental findings. With the advent of machine intelligence and neural networks, extracting and modeling, approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other. Predictive Analysis on Large Data for Actionable Knowledge: Emerging Research and Opportunities provides emerging information on extraction and prediction patterns in data mining along with knowledge discovery. While highlighting the current issues in data extraction, readers will learn new methodologies comprising of different algorithms that automate the multidimensional schema that remove the manual processes. This book is a vital resource for researchers, academics, and those seeking new information on data mining techniques and trends.

Download or read book Optimizing Data and New Methods for Efficient Knowledge Discovery and Information Resources Management Emerging Research and Opportunities written by Swayze, Susan and published by IGI Global. This book was released on 2020-06-26 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fast-paced world created by the accessibility of consumer information through internet-generated data requires improved information-management platforms. The continuous evaluation and evolution of these systems facilitate enhanced data reference and output. Optimizing Data and New Methods for Efficient Knowledge Discovery and Information Resources Management is a critical research publication that provides insight into the varied and rapidly changing fields of knowledge discovery and information resource management. Highlighting a range of topics such as datamining, artificial intelligence, and risk assessment, this book is essential for librarians, academicians, policymakers, information managers, professionals, and researchers in fields that include artificial intelligence, knowledge discovery, data visualization, big data, and information resources management.

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 335 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 Handbook of Research on Big Data and the IoT written by Kaur, Gurjit and published by IGI Global. This book was released on 2019-03-29 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increase in connected devices in the internet of things (IoT) is leading to an exponential increase in the data that an organization is required to manage. To successfully utilize IoT in businesses, big data analytics are necessary in order to efficiently sort through the increased data. The combination of big data and IoT can thus enable new monitoring services and powerful processing of sensory data streams. The Handbook of Research on Big Data and the IoT is a pivotal reference source that provides vital research on emerging trends and recent innovative applications of big data and IoT, challenges facing organizations and the implications of these technologies on society, and best practices for their implementation. While highlighting topics such as bootstrapping, data fusion, and graph mining, this publication is ideally designed for IT specialists, managers, policymakers, analysts, software engineers, academicians, and researchers.

Download or read book Intelligent Innovations in Multimedia Data Engineering and Management written by Bhattacharyya, Siddhartha and published by IGI Global. This book was released on 2018-09-07 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the ever-increasing volume of data, proper management of data is a challenging proposition to scientists and researchers, and given the vast storage space required, multimedia data is no exception in this regard. Scientists and researchers are investing great effort to discover new space-efficient methods for storage and archiving of this data. Intelligent Innovations in Multimedia Data Engineering and Management provides emerging research exploring the theoretical and practical aspects of storage systems and computing methods for large forms of data. Featuring coverage on a broad range of topics such as binary image, fuzzy logic, and metaheuristic algorithms, this book is ideally designed for computer engineers, IT professionals, technology developers, academicians, and researchers seeking current research on advancing strategies and computing techniques for various types of data.

Download or read book Data Analytics written by Arthur Zhang and published by . This book was released on 2017-03-10 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Ultimate Guide to Data Science and Analytics This practical guide is accessible for the reader who is relatively new to the field of data analytics, while still remaining robust and detailed enough to function as a helpful guide to those already experienced in the field. Data science is expanding in breadth and growing rapidly in importance as technology rapidly integrates ever deeper into business and our daily lives. The need for a succinct and informal guide to this important field has never been greater. RIGHT NOW you can get ahead of the pack! This coherent guide covers everything you need to know on the subject of data science, with numerous concrete examples, and invites the reader to dive further into this exciting field. Students from a variety of academic backgrounds, including computer science, business, engineering, statistics, anyone interested in discovering new ideas and insights derived from data can use this as a textbook. At the same time, professionals such as managers, executives, professors, analysts, doctors, developers, computer scientists, accountants, and others can use this book to make a quantum leap in their knowledge of big data in a matter of only a few hours. Learn how to understand this field and uncover actionable insights from data through analytics. UNDERSTAND the following key insights when you grab your copy today: WHY DATA IS IMPORTANT TO YOUR BUSINESS DATA SOURCES HOW DATA CAN IMPROVE YOUR BUSINESS HOW BIG DATA CREATES VALUE DEVELOPMENT OF BIG DATA CONSIDERING THE PROS AND CONS OF BIG DATA BIG DATA FOR SMALL BUSINESSES THE COST EFFECTIVENESS OF DATA ANALYTICS WHAT TO CONSIDER WHEN PREPARING FOR A NEW BIG DATA SOLUTION DATA GATHERING DATA SCRUBBING DESCRIPTIVE ANALYTICS INFERENTIAL STATISTICS PREDICTIVE ANALYTICS PREDICTIVE MODELS DESCRIPTIVE MODELING DECISION MODELING PREDICTIVE ANALYSIS METHODS MACHINE LEARNING TECHNIQUES DATA ANALYSIS WITH "R" ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT (CRM) THE USE OF PREDICTIVE ANALYTICS IN HEALTHCARE THE USE OF PREDICTIVE ANALYTICS IN THE FINANCIAL SECTOR PREDICTIVE ANALYTICS & BUSINESS MARKETING STRATEGIES FRAUD DETECTION SHIPPING BUSINESS CONTROLLING RISK FACTORS THE REVOLUTION OF PREDICTIVE ANALYSIS ACROSS A VARIETY OF INDUSTRIES DESCRIPTIVE AND PREDICTIVE ANALYSIS CRUCIAL FACTORS FOR DATA ANALYSIS RESOURCES AND FLEXIBLE TECHNICAL STRUCTURE BUSINESS INTELLIGENCE HYPER TARGETING WHAT IS DATA SCIENCE? DATA MUNGING DEMYSTIFYING DATA SCIENCE SECURITY RISKS TODAY BIG DATA AND IMPACTS ON EVERYDAY LIFE FINANCE AND BIG DATA APPLYING SENTIMENT ANALYSIS RISK EVALUATION AND THE DATA SCIENTIST THE FINANCE INDUSTRY AND REAL-TIME ANALYTICS HOW BIG DATA IS BENEFICIAL TO THE CUSTOMER CUSTOMER SEGMENTATION IS GOOD FOR BUSINESS USE OF BIG DATA BENEFITS IN MARKETING GOOGLE TRENDS THE PROFILE OF A PERFECT CUSTOMER LEAD SCORING IN PREDICTIVE ANALYSIS EVALUATING THE WORTH OF LIFETIME VALUE BIG DATA ADVANTAGES AND DISADVANTAGES MAKING COMPARISONS WITH COMPETITORS DATA SCIENCE IN THE TRAVEL SECTOR SAFETY ENHANCEMENTS THANKS TO BIG DATA BIG DATA AND AGRICULTURE BIG DATA AND LAW ENFORCEMENT THE USE OF BIG DATA IN THE PUBLIC SECTOR BIG DATA AND GAMING PRESCRIPTIVE ANALYTICS GOOGLE'S "SELF-DRIVING CAR" AND MUCH MORE! WANT MORE? Scroll up and grab this helpful guide toady!

Download or read book Big Data Analytics written by and published by Elsevier. This book was released on 2015-08-04 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions. Review of big data research challenges from diverse areas of scientific endeavor Rich perspective on a range of data science issues from leading researchers Insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domains

Download or read book Optimizing Big Data Management and Industrial Systems With Intelligent Techniques written by Öner, Sultan Ceren and published by IGI Global. This book was released on 2018-12-07 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to survive an increasingly competitive market, corporations must adopt and employ optimization techniques and big data analytics for more efficient product development and value creation. Understanding the strengths, weaknesses, opportunities, and threats of new techniques and manufacturing processes allows companies to succeed during the rise of Industry 4.0. Optimizing Big Data Management and Industrial Systems With Intelligent Techniques explores optimization techniques, recommendation systems, and manufacturing processes that support the evaluation of cyber-physical systems, end-to-end engineering, and digitalized control systems. Featuring coverage on a broad range of topics such as digital economy, fuzzy logic, and data linkage methods, this book is ideally designed for manufacturers, engineers, professionals, managers, academicians, and students.

Download or read book Extracting Knowledge From Opinion Mining written by Agrawal, Rashmi and published by IGI Global. This book was released on 2018-09-07 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining techniques are commonly used to extract meaningful information from the web, such as data from web documents, website usage logs, and hyperlinks. Building on this, modern organizations are focusing on running and improving their business methods and returns by using opinion mining. Extracting Knowledge From Opinion Mining is an essential resource that presents detailed information on web mining, business intelligence through opinion mining, and how to effectively use knowledge retrieved through mining operations. While highlighting relevant topics, including the differences between ontology-based opinion mining and feature-based opinion mining, this book is an ideal reference source for information technology professionals within research or business settings, graduate and post-graduate students, as well as scholars.

Download or read book Big Data Processing With Hadoop written by Revathi, T. and published by IGI Global. This book was released on 2018-11-16 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the increasing availability of affordable internet services, the number of users, and the need for a wider range of multimedia-based applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to the need for further advancements to information processing. Big Data Processing With Hadoop is an essential reference source that discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. Featuring research on topics such as market basket analytics, scheduler load simulator, and writing YARN applications, this book is ideally designed for IoT professionals, students, and engineers seeking coverage on many of the real-world challenges regarding big data.

Download or read book Analytics and Knowledge Management written by Suliman Hawamdeh and published by CRC Press. This book was released on 2018-08-06 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.

Download or read book Data Mining and Predictive Analytics written by Daniel T. Larose and published by John Wiley & Sons. This book was released on 2015-03-16 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Download or read book Data Science Thinking written by Longbing Cao and published by Springer. This book was released on 2018-08-17 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.

Download or read book Optimization Techniques for Problem Solving in Uncertainty written by Tilahun, Surafel Luleseged and published by IGI Global. This book was released on 2018-06-22 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: When it comes to optimization techniques, in some cases, the available information from real models may not be enough to construct either a probability distribution or a membership function for problem solving. In such cases, there are various theories that can be used to quantify the uncertain aspects. Optimization Techniques for Problem Solving in Uncertainty is a scholarly reference resource that looks at uncertain aspects involved in different disciplines and applications. Featuring coverage on a wide range of topics including uncertain preference, fuzzy multilevel programming, and metaheuristic applications, this book is geared towards engineers, managers, researchers, and post-graduate students seeking emerging research in the field of optimization.

Download or read book R Predictive Analysis written by Tony Fischetti and published by Packt Publishing Ltd. This book was released on 2017-03-31 with total page 1065 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Naive Bayes, decision trees, text mining and so on. We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling Who This Book Is For If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use. What You Will Learn Get to know the basics of R's syntax and major data structures Write functions, load data, and install packages Use different data sources in R and know how to interface with databases, and request and load JSON and XML Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data Predict the future with reasonably simple algorithms Understand key data visualization and predictive analytic skills using R Understand the language of models and the predictive modeling process In Detail Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Data Analysis with R, Tony Fischetti Learning Predictive Analytics with R, Eric Mayor Mastering Predictive Analytics with R, Rui Miguel Forte Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of predictive modeling.

Download or read book Real world Data Mining written by Dursun Delen and published by Pearson Education. This book was released on 2015 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: As business becomes increasingly complex and global, decision-makers must act more rapidly and accurately, based on the best available evidence. Modern data mining and analytics is indispensable for doing this. Real-World Data Mining demystifies current best practices, showing how to use data mining and analytics to uncover hidden patterns and correlations, and leverage these to improve all business decision-making. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, Delen provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: data mining processes, methods, and techniques; the role and management of data; tools and metrics; text and web mining; sentiment analysis; and integration with cutting-edge Big Data approaches. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials.

Download or read book Modeling Techniques in Predictive Analytics with Python and R written by Thomas W. Miller and published by FT Press. This book was released on 2014-09-29 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more