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Book Synopsis Principles of Database Management by : Wilfried Lemahieu
Download or read book Principles of Database Management written by Wilfried Lemahieu and published by Cambridge University Press. This book was released on 2018-07-12 with total page 817 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
Book Synopsis Principles of Data Science by : Hamid R. Arabnia
Download or read book Principles of Data Science written by Hamid R. Arabnia and published by Springer Nature. This book was released on 2020-07-08 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice
Author :Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma Publisher :BPB Publications ISBN 13 :938984567X Total Pages :580 pages Book Rating :4.3/5 (898 download)
Book Synopsis Data Science Fundamentals and Practical Approaches by : Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma
Download or read book Data Science Fundamentals and Practical Approaches written by Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma and published by BPB Publications. This book was released on 2020-09-03 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development.
Download or read book Data Mesh written by Zhamak Dehghani and published by "O'Reilly Media, Inc.". This book was released on 2022-03-08 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.
Book Synopsis Principles of Data Mining and Knowledge Discovery by : Djamel A. Zighed
Download or read book Principles of Data Mining and Knowledge Discovery written by Djamel A. Zighed and published by Springer. This book was released on 2003-07-31 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000, held in Lyon, France in September 2000. The 86 revised papers included in the book correspond to the 29 oral presentations and 57 posters presented at the conference. They were carefully reviewed and selected from 147 submissions. The book offers topical sections on new directions, rules and trees, databases and reward-based learning, classification, association rules and exceptions, instance-based discovery, clustering, and time series analysis.
Book Synopsis Principles of Data Integration by : AnHai Doan
Download or read book Principles of Data Integration written by AnHai Doan and published by Elsevier. This book was released on 2012-06-25 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: How do you approach answering queries when your data is stored in multiple databases that were designed independently by different people? This is first comprehensive book on data integration and is written by three of the most respected experts in the field. This book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. Data integration is the problem of answering queries that span multiple data sources (e.g., databases, web pages). Data integration problems surface in multiple contexts, including enterprise information integration, query processing on the Web, coordination between government agencies and collaboration between scientists. In some cases, data integration is the key bottleneck to making progress in a field. The authors provide a working knowledge of data integration concepts and techniques, giving you the tools you need to develop a complete and concise package of algorithms and applications.
Book Synopsis Principles of Data Mining and Knowledge Discovery by : Tapio Elomaa
Download or read book Principles of Data Mining and Knowledge Discovery written by Tapio Elomaa and published by Springer. This book was released on 2003-08-02 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2002, held in Helsinki, Finland in August 2002. The 39 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are kernel methods, probabilistic methods, association rule mining, rough sets, sampling algorithms, pattern discovery, web text mining, meta data clustering, rule induction, information extraction, dependency detection, rare class prediction, classifier systems, text classification, temporal sequence analysis, unsupervised learning, time series analysis, medical data mining, etc.
Book Synopsis Principles of Data Mining and Knowledge Discovery by : Jan Zytkow
Download or read book Principles of Data Mining and Knowledge Discovery written by Jan Zytkow and published by Springer. This book was released on 2004-06-08 with total page 593 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.
Book Synopsis Principles of Data Integration by : AnHai Doan
Download or read book Principles of Data Integration written by AnHai Doan and published by Elsevier. This book was released on 2012-06-25 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand Enables you to build your own algorithms and implement your own data integration applications
Book Synopsis Non-Invasive Data Governance by : Robert S. Seiner
Download or read book Non-Invasive Data Governance written by Robert S. Seiner and published by Technics Publications. This book was released on 2014-09-01 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve.
Book Synopsis Principles and Theories of Data Mining With RapidMiner by : Ramjan, Sarawut
Download or read book Principles and Theories of Data Mining With RapidMiner written by Ramjan, Sarawut and published by IGI Global. This book was released on 2023-05-09 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software.
Book Synopsis Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses by : Wenzhong Shi
Download or read book Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses written by Wenzhong Shi and published by CRC Press. This book was released on 2009-09-30 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: When compared to classical sciences such as math, with roots in prehistory, and physics, with roots in antiquity, geographical information science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is an important branch of t
Book Synopsis Data Science and Analytics with Python by : Jesus Rogel-Salazar
Download or read book Data Science and Analytics with Python written by Jesus Rogel-Salazar and published by CRC Press. This book was released on 2018-02-05 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.
Book Synopsis Python Data Science Analyzing and Visualizing Data with Python by : Sunil Kumar Saini
Download or read book Python Data Science Analyzing and Visualizing Data with Python written by Sunil Kumar Saini and published by Sunil Kumar Saini. This book was released on 2023-04-27 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Python Data Science: Analyzing and Visualizing Data with Python" is a book that covers the fundamentals of data analysis and visualization using the Python programming language. The book starts with an introduction to Python programming and data analysis, and then covers the main libraries used in data analysis, such as NumPy, Pandas, and Matplotlib. The book then goes into more advanced topics, such as statistical analysis, machine learning, and deep learning. It covers how to use Python to clean and preprocess data, perform exploratory data analysis, and visualize data using different types of plots and charts. The book also includes examples of real-world data analysis projects, such as analyzing financial data and building predictive models for healthcare data. It is a comprehensive guide for anyone looking to learn data analysis and visualization using Python, regardless of their level of expertise.
Book Synopsis Principles and Methods for Data Science by :
Download or read book Principles and Methods for Data Science written by and published by Elsevier. This book was released on 2020-05-28 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Principles and Methods for Data Science
Book Synopsis The Software Principles of Design for Data Modeling by : Samanta, Debabrata
Download or read book The Software Principles of Design for Data Modeling written by Samanta, Debabrata and published by IGI Global. This book was released on 2023-09-12 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Software Principles of Design for Data Modeling, written by Debabrata Samanta of Rochester Institute of Technology in Kosovo, offers a practical and comprehensive solution to the challenges of designing effective software architecture for data modeling. This book covers key topics such as gathering requirements, modeling requirements with use cases, testing the system, building entity-relationship models, building class models in UML with patterns of data modeling and software quality attributes, and use case modeling. It also includes case studies of relational and object-relational database schema design. The unique approach of this book lies in its unifying method for designing software architecture for data modeling. It addresses specific design issues for various types of software systems, including object-oriented, client/server, service-oriented, component-based, real-time, and software product line architectures. With its practical guidance, standard method for modeling requirements and analysis, and comprehensive coverage of key topics and case studies, this book is a must-read for anyone interested in designing effective software architecture for data modeling, whether you are an academic scholar or a professional in the field.
Book Synopsis Principles of Data Mining and Knowledge Discovery by : Jan Komorowski
Download or read book Principles of Data Mining and Knowledge Discovery written by Jan Komorowski and published by Springer Science & Business Media. This book was released on 1997-06-13 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '97, held in Trondheim, Norway, in June 1997. The volume presents a total of 38 revised full papers together with abstracts of one invited talk and four tutorials. Among the topics covered are data and knowledge representation, statistical and probabilistic methods, logic-based approaches, man-machine interaction aspects, AI contributions, high performance computing support, machine learning, automated scientific discovery, quality assessment, and applications.
