Making Sense of Data I - A Practical Guide to Exploratory Data Analysis and Data Mining 2e, Myatt
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Bradley Efron , Trevor Hastie Название: Computer Age Statistical Inference, Student Edition ISBN: 1108823416 ISBN-13(EAN): 9781108823418 Издательство: Cambridge Academ Рейтинг: Цена: 33790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.
Автор: Myatt Название: Making Sense of Data II - A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications ISBN: 0470222808 ISBN-13(EAN): 9780470222805 Издательство: Wiley Рейтинг: Цена: 94990.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a general end-to-end discussion concerning the process of translating raw data to scientific and business decisions. The reader`s ability to find patterns in data will be greatly enhanced due to the book`s combination of statistical learning with powerful visualization techniques.
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
Автор: Marc Potters, Jean-Philippe Bouchaud Название: A First Course in Random Matrix Theory ISBN: 1108488080 ISBN-13(EAN): 9781108488082 Издательство: Cambridge Academ Рейтинг: Цена: 61240.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Classical statistical tools that handled real-life data have become inadequate upon the emergence of Big Data. Random matrix theory and free calculus introduced here present valuable solutions to the complex challenges posed by large datasets. Real world applications make it an essential tool for physicists, engineers, data analysts and economists.
Автор: Jones Herbert Название: Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database ISBN: 1647483263 ISBN-13(EAN): 9781647483265 Издательство: Неизвестно Рейтинг: Цена: 27580.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Data science has a huge impact on how companies conduct business, and those who don`t learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.
Автор: Wang Dashun, Barabбsi Albert-Lбszlу Название: The Science of Science ISBN: 1108492665 ISBN-13(EAN): 9781108492669 Издательство: Cambridge Academ Рейтинг: Цена: 131580.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is the first comprehensive overview of the exciting field of the `science of science`. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.
Автор: Sharaff Aakanksha, Sinha G. R., Bhatia Surbhi Название: New Opportunities for Sentiment Analysis and Information Processing ISBN: 1799880621 ISBN-13(EAN): 9781799880622 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 196810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts.
New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.
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