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Data science in theory and practice : Techniques for big data analytics and complex data sets
Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Data science for economics and finance : Methodologies and applications
The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis.
Data Science for Civil Engineering : A Beginner's Guide
Explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation.
Data science and data analytics : Opportunities and challenges
Gives the concept of data science, tools, and algorithms that exist for many useful applications / Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems / Identifies many areas and uses of data science in the smart era / Applies data science to agriculture, healthcare, graph mining, education, security, etc.
Data Science and Classification
This volume provides new methodological developments in data analysis and classification. A wide range of topics is covered that includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Apart from structural and theoretical results the book shows how to apply the proposed to a variety of problems, for example in medicine, microarray analysis, social network structures, and music. The combination of new methodological advances with the wide range of real applications collected in this volume is of special value for researchers when choosing the appropriate among newly developed analytical tools for their research problems in classification and data analysis.
Data science and analytics ; 5th International conference on recent developments in science, engineering and technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part II
This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics
Data science and analytics ; 5th International conference on recent developments in science, engineering and technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part I
This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.
Data science ; 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020, Taiyuan, China, September 18-21, 2020, Proceedings, Part II
This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020. The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education.
Data Science ; 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020, Taiyuan, China, September 18-21, 2020, Proceedings, Part I
This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020. The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education.
Data Quality : Concepts, Methodologies and Techniques
Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art.
Data mining with computational intelligence
Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.
Data Mining for Biomedical Applications ; PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings
This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with 1 keynote talks were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections on protein-protein interactions, database and search, bio data clustering, and in-silico diagnosis.
Data mining and machine learning applications
Elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.
Data Management. Data, Data Everywhere ; 24th British National Conference on Databases, BNCOD 24, Glasgow, UK, July 3-5, 2007, Proceedings
One of the most pressing challenges is to ?nd ways of evolving database technology to cope with its new role in underpinning the massively distributed and heterogeneous applications built on top of the Internet. This has afiected both the ways in which data has been accessed and the ways in which it is represented, with XML data management becoming an important issue and, as such, heavily represented at this conference. It has also brought back issues of performance that might have been considered largely solved by the improvements in hardware, since data now has to be managed on devices of low power and small memory as well as on standard client and powerful server machines. We therefore invited papers on all aspects of data management, particularly related to how dataisused in the ubiquitous environment of the modern Internet by complex distributed and scientific applications.
Data Management Technologies and Applications ; 8th International Conference, DATA 2019, Prague, Czech Republic, July 26–28, 2019, Revised Selected Papers
This book constitutes the thoroughly refereed proceedings of the 8th International Conference on Data Management Technologies and Applications, DATA 2019, held in Prague, Czech Republic, in July 2019. The 8 revised full papers were carefully reviewed and selected from 90 submissions. The papers deal with the following topics: decision support systems, data analytics, data and information quality, digital rights management, big data, knowledge management, ontology engineering, digital libraries, mobile databases, object-oriented database systems, and data integrity.
Data Journeys in the Sciences
This groundbreaking, book analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced.
Data Integration in the Life Sciences ; Vol. 4075 ; 3rd International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006, Proceedings
Data management and data integration are fundamental problems in the life sciences. Advances in molecular biology and molecular medicine are almost u- versallyunderpinned by enormouse?orts in data management,data integration, automatic data quality assurance, and computational data analysis. Many hot topics in the life sciences, such as systems biology, personalized medicine, and pharmacogenomics, critically depend on integrating data sets and applications producedby di?erent experimentalmethods, in di?erent researchgroups,andat di?erent levels of granularity.
Data analytics, computational statistics, and operations research for engineers : Methodologies and applications
Presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information.
Data Analysis, Machine Learning and Applications ; Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007
This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl).
Dangerous Pollutants (Xenobiotics) in Urban Water Cycle
The book focused on evaluation of impact of xenobiotics in the whole Urban Water Cycle are an interdisciplinary task which has a rising concern these days. Xenobiotics includes both inorganic elements like heavy metals, metalloids and man-made organic compounds such as pesticides, surfactants, solvents, fragrances, flavours, and pharmaceuticals as well as endocrine dis- pters.
