Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining.
As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code.
After reading, readers will understand:
· the growing importance of data science
· the role of the information professional in data science
· some of the most important tools and methods that information professionals can use.
Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.
Автор: Shalin Hai-Jew Название: Social Media Data Extraction and Content Analysis ISBN: 1522506489 ISBN-13(EAN): 9781522506485 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 222690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Explores various social networking platforms and the technologies being utilized to gather and analyse information being posted to these venues. Highlighting emergent research, analytical techniques, and best practices in data extraction in global electronic culture, this publication is an essential reference source for researchers, academics, and professionals.
Автор: Sam Freed Название: AI and Human Thought and Emotion ISBN: 0367029294 ISBN-13(EAN): 9780367029296 Издательство: Taylor&Francis Рейтинг: Цена: 107190.00 T Наличие на складе: Нет в наличии. Описание: This reference work examines how human thought processes and emotion can be captured by artificial intelligence (AI) algorithms and code. It provides a theoretical framework and demonstrates how code can be generate on the basis of the framework.
Автор: Thomas Cleff Название: Applied Statistics and Multivariate Data Analysis ISBN: 3030177661 ISBN-13(EAN): 9783030177669 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Нет в наличии. Описание: This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis.
Автор: N. N. R. Ranga Suri; Narasimha Murty M; G. Athitha Название: Outlier Detection: Techniques and Applications ISBN: 3030051250 ISBN-13(EAN): 9783030051259 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.
Автор: Wang Xiaochun, Wang Xiali, Wilkes Mitch Название: New Developments in Unsupervised Outlier Detection: Algorithms and Applications ISBN: 9811595186 ISBN-13(EAN): 9789811595189 Издательство: Springer Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Overview and Contributions.- Developments in Unsupervised Outlier Detection Research.- A Fast Distance-Based Outlier Detection Technique Using A Divisive Hierarchical Clustering Algorithm.- A k-Nearest Neighbour Centroid Based Outlier Detection Method.- A Minimum Spanning Tree Clustering Inspired Outlier Detection Technique.- A k-Nearest Neighbour Spectral Clustering Based Outlier Detection Technique.- Enhancing Outlier Detection by Filtering Out Core Points and Border Points.- An Effective Boundary Point Detection Algorithm via k-Nearest Neighbours Based Centroid.- A Nearest Neighbour Classifier Based Automated On-Line Novel Visual Percept Detection Method.- Unsupervised Fraud Detection in Environmental Time Series Data.
Автор: Steven L. Brunton, J. Nathan Kutz Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control ISBN: 1108422098 ISBN-13(EAN): 9781108422093 Издательство: Amazon Internet Рейтинг: Цена: 0.00 T Наличие на складе: Невозможна поставка. Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.
Автор: Charu C. Aggarwal Название: Outlier Analysis ISBN: 3319475770 ISBN-13(EAN): 9783319475776 Издательство: Springer Рейтинг: Цена: 62410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories:
Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner.
The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Автор: Charu C. Aggarwal; Saket Sathe Название: Outlier Ensembles ISBN: 331954764X ISBN-13(EAN): 9783319547640 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification.
Автор: Manish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han Название: Outlier Detection for Temporal Data ISBN: 1627053751 ISBN-13(EAN): 9781627053754 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 41580.00 T Наличие на складе: Невозможна поставка. Описание: Compared to general outlier detection, techniques for temporal outlier detection are very different. This book presents an organised picture of both recent and past research in temporal outlier detection. It starts with the basics before moving on to the main ideas in state-of-the-art outlier detection techniques.
Автор: Jin Cheqing, Zhou Aoying, Mao Jiali Название: Clustering And Outlier Detection For Trajectory Stream Data ISBN: 9811210454 ISBN-13(EAN): 9789811210457 Издательство: World Scientific Publishing Рейтинг: Цена: 95040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.
Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.
This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.
Автор: Aggarwal Charu C., Sathe Saket Название: Outlier Ensembles: An Introduction ISBN: 3319854747 ISBN-13(EAN): 9783319854748 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification.
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