Big Data Con Herramientas de SAS. SAS Visual Analytics, SAS Visual Statistics y SAS High Performance Analytics

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Big Data - Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance John Wiley & Sons Inc

Książki / Literatura obcojęzyczna

Convert the promise of big data into real world results§§There is so much buzz around big data. We all need to know what it is and how it works - that much is obvious. But is a basic understanding of the theory enough to hold your own in strategy meetings? Probably. But what will set you apart from the rest is actually knowing how to USE big data to get solid, real-world business results - and putting that in place to improve performance. Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy. This is a well-needed practical introduction to actually putting the topic into practice. Illustrated with numerous real-world examples from a cross section of companies and organisations, Big Data will take you through the five steps of the SMART model: Start with Strategy, Measure Metrics and Data, Apply Analytics, Report Results, Transform.§Discusses how companies need to clearly define what it is they need to know§Outlines how companies can collect relevant data and measure the metrics that will help them answer their most important business questions§Addresses how the results of big data analytics can be visualised and communicated to ensure key decisions-makers understand them§Includes many high-profile case studies from the author's work with some of the world's best known brands

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Data Driven - How Performance Analytics Delivers Extraordinary Sales Results John Wiley & Sons Inc

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A "how-to" guide to boosting sales through predictive and prescriptive analytics§§Data Driven is a uniquely practical guide to increasing sales success, using the power of data analytics. Written by one of the world's leading authorities on the topic, this book shows you how to transform the corporate sales function by leveraging big data into better decision-making, more informed strategy, and increased effectiveness throughout the organization. Engaging and informative, this book tells the story of a newly hired sales chief under intense pressure to deliver higher performance from her team, and how data analytics becomes the ultimate driver behind the sales function turnaround. Each chapter features insightful commentary and practical notes on the points the story raises, and one entire chapter is devoted solely to laying out the Prescriptive Action Model step-by-step giving you the actionable guidance you need to put it into action in your own organization.§§Predictive and prescriptive analytics is poised to change corporate sales, and companies that fail to adapt to the new realities and adopt the new practices will be left behind. This book explains why the Prescriptive Action Model is the key corporate sales weapon of the 21st Century, and how you can implement this dynamic new resource to bring value to your business.§Exploit one of the last remaining sources of competitive advantage§Re-engineer the sales function to optimize success rates§Implement a more effective analytics model to drive efficient change§Boost operational effectiveness and decision making with big data§§There are fewer competitive edges to gain than ever before. The only thing that's left is to execute business with maximum efficiency and make the smartest business decisions possible. Predictive analytics is the essential method behind this new standard, and Data Driven is the practical guide to complete, efficient implementation.

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Foundations of Data Intensive Applications - Large Scale Data Analytics under the Hood John Wiley & Sons Inc

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PEEK "UNDER THE HOOD" OF BIG DATA ANALYTICSThe 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

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Big Data & High Performance Computing

Książki Obcojęzyczne>Angielskie>Technology, engineering, agriculture>Civil engineering, surveying & building

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High Performance Analytics With The R3 - Cache

Książki Obcojęzyczne>Angielskie>Computing & information technology>Information technology: general issues

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Data Science and Predictive Analytics Springer, Berlin

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This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

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Python Data Analytics Springer, Berlin

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Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulationAuthor Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.What You'll LearnUnderstand the core concepts of data analysis and the Python ecosystemGo in depth with pandas for reading, writing, and processing dataUse tools and techniques for data visualization and image analysisExamine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorchWho This Book Is ForExperienced Python developers who need to learn about Pythonic tools for data analysis

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Big R-Book - From Data Science to Learning Machines and Big Data John Wiley and Sons Ltd

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Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. * Provides a practical guide for non-experts with a focus on business users * Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting * Uses a practical tone and integrates multiple topics in a coherent framework * Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R * Shows readers how to visualize results in static and interactive reports * Supplementary materials includes PDF slides based on the book's content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

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Big Data Fundamentals Pearson Education

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Big Data Science Fundamentals offers a comprehensive, easy-to-understand, and up-to-date understanding of Big Data for all business professionals and technologists. Leading enterprise technology author Thomas Erl introduces key Big Data concepts, theory, terminology, technologies, key analysis/analytics techniques, and more - all logically organized, presented in plain English, and supported by easy-to-understand diagrams and case study examples. Erl provides a uniquely valuable methodology for Big Data analysis, and introduces the underlying analysis techniques and enabling technological constructs that constitute a Big Data solution environment. He presents vendor-neutral guidance on implementing Big Data for competitive advantage; and for successfully integrating Big Data with existing enterprise systems.Coverage includes: *Big Data's fundamental concepts and key business/technology drivers*"5 V" characteristics of data in Big Data environments: volume, velocity, variety, veracity, and value*Types of Big Data: structured, unstructured, semi-structured, and meta-data*Big Data's relationships with OLTP, OLAP, ETL, data warehouses, and data marts*Fundamental types of analysis, analytics, and machine learning*Requirements and tools for visualizing big data*Adoption and planning: business cases, privacy, security, provenance, performance, governance, and more*Big Data technologies, including clusters, NoSQL, distributed and parallel data processing, Hadoop, cloud computing, and storage*Big Data analysis and analytics across the full lifecycle*And much more

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Scalable Big Data Architecture APress

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Most people think that Big Data projects start directly with the deployment of large distributed clusters of heavy map reduce jobs, whereas reality shows that there isn't any unique/perfect solution to solving problems when dealing with large volumes of data.§§By knowing the different Big Data integration patterns, you will understand why most of the time you will have to deploy a heterogeneous architecture that fulfills different needs, and furthermore what limits each pattern that may lead you to choose effective alternates.§§We will go through real concrete industry use cases that leverage these patterns such as REST API which requests large amount of data stored in No-SQL like Couchbase and Elasticsearch. We will see how massive data processing can be done in such No-SQL databases without the need of diving deep into Big Data.§§But when the volume is too high and the data structures gets too complex, the kind of pattern being employed reaches its limits and that's when we can start thinking of delegating complex data processing jobs to, for example, a Hadoop based Big Data architecture.§§The difficulty is to then choose a relevant combination of big data technologies available within the Hadoop ecosystem. We will focus on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern will be illustrated with practical examples, which uses the different apache projects such as Avro, Spark, Kafka, and so on.§§Traditional Big Data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book will also help you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints implied by dealing with high throughput of Big data.§§

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Big Data Platforms and Applications Springer, Berlin

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This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation. The explosive growth in the volume, speed, and variety of data being produced every day requires a continuous increase in the processing speeds of servers and of entire network infrastructures, as well as new resource management models. This poses significant challenges (and provides striking development opportunities) for data intensive and high-performance computing, i.e., how to efficiently turn extremely large datasets into valuable information and meaningful knowledge. The task of context data management is further complicated by the variety of sources such data derives from, resulting in different data formats, with varying storage, transformation, delivery, and archiving requirements. At the same time rapid responses are needed for real-time applications. With the emergence of cloud infrastructures, achieving highly scalable data management in such contexts is a critical problem, as the overall application performance is highly dependent on the properties of the data management service.

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Practical Business Analytics Using R and Python APress

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This updated book builds on the basic foundations established in the first edition, illustrating how data can predict future outcomes, optimize the efficiency and performance of organizations, and detect relevant patterns and relationships. It will also teach you to analyze and understand data by applying concepts of probability theory, statistics, and machine learning. This new edition also uses Python coding examples in addition to R. It also features new chapters, including coverage of SQL, NLP, and optimization models in R and Python.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models, statistical learning models, and machine learning models. It discusses concepts like regression, classification, and neural networks. Part three covers the two popular unsupervised learning techniques used in text mining and natural language processing (NLP). It explains how to utilize clustering techniques and mine text to discover insights. The book concludes with an overview of big data analytics, R and Python for analytics, and explores Jupyter Notebook, pandas, and NumPy python libraries used in data analytics.

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Monetizing Your Data - A Guide to Turning Data into Profit-Driving Strategies and Solutions John Wiley & Sons Inc

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Transforming data into revenue generating strategies and actionsOrganizations are swamped with data--collected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it? Monetizing your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you through the process of developing value generating strategies that can translate into big returns. The companion website, www.monetizingyourdata.com, provides templates, checklists, and examples to help you apply the methodology in your environment, and the expert author team provides authoritative guidance every step of the way.This book shows you how to use your data to:Monetize your data to drive revenue and cut costsConnect your data to decisions that drive action and deliver valueDevelop analytic tools to guide managers up and down the ladder to better decisionsTurning data into action is key; data can be a valuable competitive advantage, but only if you understand how to organize it, structure it, and uncover the actionable information hidden within it through decision architecture and guided analytics. From multinational corporations to single-owner small businesses, companies of every size and structure stand to benefit from these tools, methods, and techniques; Monetizing your Data walks you through the translation and transformation to help you leverage your data into value creating strategies.

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High Performance Spark O'Reilly Media, Inc, USA

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If you've successfully used Apache Spark to solve medium sized-problems, but still struggle to realize the "Spark promise" of unparalleled performance on big data, this book is for you. High Performance Spark shows you how take advantage of Spark at scale, so you can grow beyond the novice-level.

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