Search Results for "modeling-with-data-tools-and-techniques-for-scientific-computing"

Modeling with Data

Modeling with Data

Tools and Techniques for Scientific Computing

  • Author: Ben Klemens
  • Publisher: Princeton University Press
  • ISBN: 9781400828746
  • Category: Mathematics
  • Page: 472
  • View: 4036
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Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date. Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Mastering Scientific Computing with R

Mastering Scientific Computing with R

  • Author: Paul Gerrard,Radia M. Johnson
  • Publisher: Packt Publishing Ltd
  • ISBN: 1783555262
  • Category: Computers
  • Page: 432
  • View: 2659
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If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

Efficient Numerical Methods and Information-Processing Techniques for Modeling Hydro- and Environmental Systems

Efficient Numerical Methods and Information-Processing Techniques for Modeling Hydro- and Environmental Systems

  • Author: Reinhard Hinkelmann
  • Publisher: Springer Science & Business Media
  • ISBN: 9783540241461
  • Category: Science
  • Page: 306
  • View: 3805
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Numerical simulation models have become indispensable in hydro- and environmental sciences and engineering. This monograph presents a general introduction to numerical simulation in environment water, based on the solution of the equations for groundwater flow and transport processes, for multiphase and multicomponent flow and transport processes in the subsurface as well as for flow and transport processes in surface waters. It displays in detail the state of the art of discretization and stabilization methods (e.g. finite-difference, finite-element, and finite-volume methods), parallel methods, and adaptive methods as well as fast solvers, with particular focus on explaining the interactions of the different methods. The book gives a brief overview of various information-processing techniques and demonstrates the interactions of the numerical methods with the information-processing techniques, in order to achieve efficient numerical simulations for a wide range of applications in environment water.

Modern Software Tools for Scientific Computing

Modern Software Tools for Scientific Computing

  • Author: A. Bruaset,E. Arge,Hans Petter Langtangen
  • Publisher: Springer Science & Business Media
  • ISBN: 1461219868
  • Category: Computers
  • Page: 380
  • View: 3991
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Looking back at the years that have passed since the realization of the very first electronic, multi-purpose computers, one observes a tremendous growth in hardware and software performance. Today, researchers and engi neers have access to computing power and software that can solve numerical problems which are not fully understood in terms of existing mathemati cal theory. Thus, computational sciences must in many respects be viewed as experimental disciplines. As a consequence, there is a demand for high quality, flexible software that allows, and even encourages, experimentation with alternative numerical strategies and mathematical models. Extensibil ity is then a key issue; the software must provide an efficient environment for incorporation of new methods and models that will be required in fu ture problem scenarios. The development of such kind of flexible software is a challenging and expensive task. One way to achieve these goals is to in vest much work in the design and implementation of generic software tools which can be used in a wide range of application fields. In order to provide a forum where researchers could present and discuss their contributions to the described development, an International Work shop on Modern Software Tools for Scientific Computing was arranged in Oslo, Norway, September 16-18, 1996. This workshop, informally referred to as Sci Tools '96, was a collaboration between SINTEF Applied Mathe matics and the Departments of Informatics and Mathematics at the Uni versity of Oslo.

Introduction to Computational Modeling Using C and Open-Source Tools

Introduction to Computational Modeling Using C and Open-Source Tools

  • Author: Jose M. Garrido
  • Publisher: CRC Press
  • ISBN: 1482216795
  • Category: Computers
  • Page: 461
  • View: 7326
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Introduction to Computational Modeling Using C and Open-Source Tools presents the fundamental principles of computational models from a computer science perspective. It explains how to implement these models using the C programming language. The software tools used in the book include the Gnu Scientific Library (GSL), which is a free software library of C functions, and the versatile, open-source GnuPlot for visualizing the data. All source files, shell scripts, and additional notes are located at ksuweb.kennesaw.edu/~jgarrido/comp_models. The book first presents an overview of problem solving and the introductory concepts, principles, and development of computational models before covering the programming principles of the C programming language. The author then applies programming principles and basic numerical techniques, such as polynomial evaluation, regression, and other numerical methods, to implement computational models. He also discusses more advanced concepts needed for modeling dynamical systems and explains how to generate numerical solutions. The book concludes with the modeling of linear optimization problems. Emphasizing analytical skill development and problem solving, this book helps you understand how to reason about and conceptualize the problems, generate mathematical formulations, and computationally visualize and solve the problems. It provides you with the foundation to understand more advanced scientific computing, including parallel computing using MPI, grid computing, and other techniques in high-performance computing.

Introduction to Computational Models with Python

Introduction to Computational Models with Python

  • Author: Jose M. Garrido
  • Publisher: CRC Press
  • ISBN: 1498712045
  • Category: Computers
  • Page: 466
  • View: 8443
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Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website. The book’s five sections present: An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware tools Programming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithms Python lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under Linux Implementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from problem formulation to implementation of computational models This book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.

Data-Driven Modeling & Scientific Computation

Data-Driven Modeling & Scientific Computation

Methods for Complex Systems & Big Data

  • Author: J. Nathan Kutz
  • Publisher: OUP Oxford
  • ISBN: 019163588X
  • Category: Language Arts & Disciplines
  • Page: 608
  • View: 5792
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The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: · statistics, · time-frequency analysis, and · low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.

Computer Methods in Biomechanics and Biomedical Engineering 2

Computer Methods in Biomechanics and Biomedical Engineering 2

  • Author: J. Middleton,Gyan Pande,M. L. Jones
  • Publisher: CRC Press
  • ISBN: 9789056992064
  • Category: Medical
  • Page: 850
  • View: 4965
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Contains papers presented at the Third International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (1997), which provide evidence that computer-based models, and in particular numerical methods, are becoming essential tools for the solution of many problems encountered in the field of biomedical engineering. The range of subject areas presented include the modeling of hip and knee joint replacements, assessment of fatigue damage in cemented hip prostheses, nonlinear analysis of hard and soft tissue, methods for the simulation of bone adaptation, bone reconstruction using implants, and computational techniques to model human impact. Computer Methods in Biomechanics and Biomedical Engineering also details the application of numerical techniques applied to orthodontic treatment together with introducing new methods for modeling and assessing the behavior of dental implants, adhesives, and restorations. For more information, visit the "http://www.uwcm.ac.uk/biorome/international symposium on Computer Methods in Biomechanics and Biomedical Engineering/home page, or "http://www.gbhap.com/Computer_Methods_Biomechanic s_Biome dical_Engineering/" the home page for the journal.

Datenanalyse mit Python

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

  • Author: Wes McKinney
  • Publisher: O'Reilly
  • ISBN: 3960102143
  • Category: Computers
  • Page: 542
  • View: 3651
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Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

Statistik-Workshop für Programmierer

Statistik-Workshop für Programmierer

  • Author: Allen B. Downey
  • Publisher: O'Reilly Germany
  • ISBN: 3868993436
  • Category: Computers
  • Page: 160
  • View: 3718
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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Mastering Numerical Computing with NumPy

Mastering Numerical Computing with NumPy

Master scientific computing and perform complex operations with ease

  • Author: Mert Cuhadaroglu,Umit Mert Cakmak
  • Publisher: Packt Publishing Ltd
  • ISBN: 1788996844
  • Category: Computers
  • Page: 248
  • View: 7498
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Enhance the power of NumPy and start boosting your scientific computing capabilities Key Features Grasp all aspects of numerical computing and understand NumPy Explore examples to learn exploratory data analysis (EDA), regression, and clustering Access NumPy libraries and use performance benchmarking to select the right tool Book Description NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations. What you will learn Perform vector and matrix operations using NumPy Perform exploratory data analysis (EDA) on US housing data Develop a predictive model using simple and multiple linear regression Understand unsupervised learning and clustering algorithms with practical use cases Write better NumPy code and implement the algorithms from scratch Perform benchmark tests to choose the best configuration for your system Who this book is for Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book.

Become a Python Data Analyst

Become a Python Data Analyst

Perform exploratory data analysis and gain insight into scientific computing using Python

  • Author: Alan Fontaine
  • Publisher: Packt Publishing Ltd
  • ISBN: 1789534402
  • Category: Computers
  • Page: 178
  • View: 2529
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Enhance your data analysis and predictive modeling skills using popular Python tools Key Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis techniques and detailed code with scikit-learn and SciPy Book Description Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python. What you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build predictive models and understand the principles of predictive analytics Who this book is for Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book

Computational Methods for Optimizing Manufacturing Technology: Models and Techniques

Computational Methods for Optimizing Manufacturing Technology: Models and Techniques

Models and Techniques

  • Author: Davim, J. Paulo
  • Publisher: IGI Global
  • ISBN: 1466601299
  • Category: Technology & Engineering
  • Page: 395
  • View: 9687
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"This book contains the latest research developments in manufacturing technology and its optimization, and demonstrates the fundamentals of new computational approaches and the range of their potential application"--Provided by publisher.

Big Data

Big Data

Algorithms, Analytics, and Applications

  • Author: Kuan-Ching Li,Hai Jiang,Laurence T. Yang,Alfredo Cuzzocrea
  • Publisher: CRC Press
  • ISBN: 1482240564
  • Category: Computers
  • Page: 498
  • View: 7002
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As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management—considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing—addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms—explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy—focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications—illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Advances in Data-based Approaches for Hydrologic Modeling and Forecasting

Advances in Data-based Approaches for Hydrologic Modeling and Forecasting

  • Author: Bellie Sivakumar,Ronny Berndtsson
  • Publisher: World Scientific
  • ISBN: 9814307971
  • Category: Science
  • Page: 519
  • View: 8803
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This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.

Modeling Methods for Marine Science

Modeling Methods for Marine Science

  • Author: David M. Glover,William J. Jenkins,Scott C. Doney
  • Publisher: Cambridge University Press
  • ISBN: 1139500716
  • Category: Science
  • Page: N.A
  • View: 3879
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This advanced textbook on modeling, data analysis and numerical techniques for marine science has been developed from a course taught by the authors for many years at the Woods Hole Oceanographic Institute. The first part covers statistics: singular value decomposition, error propagation, least squares regression, principal component analysis, time series analysis and objective interpolation. The second part deals with modeling techniques: finite differences, stability analysis and optimization. The third part describes case studies of actual ocean models of ever increasing dimensionality and complexity, starting with zero-dimensional models and finishing with three-dimensional general circulation models. Throughout the book hands-on computational examples are introduced using the MATLAB programming language and the principles of scientific visualization are emphasised. Ideal as a textbook for advanced students of oceanography on courses in data analysis and numerical modeling, the book is also an invaluable resource for a broad range of scientists undertaking modeling in chemical, biological, geological and physical oceanography.

Intelligent Techniques and Tools for Novel System Architectures

Intelligent Techniques and Tools for Novel System Architectures

  • Author: Panagiotis Chountas,Ilias Petrounias
  • Publisher: Springer Science & Business Media
  • ISBN: 3540776214
  • Category: Mathematics
  • Page: 548
  • View: 2867
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This volume presents new directions and solutions in broadly perceived intelligent systems. An urgent need this volume has occurred as a result of vivid discussions and presentations at the "IEEE-IS’ 2006 – The 2006 Third International IEEE Conference on Intelligent Systems" held in London, UK, September, 2006. This book is a compilation of many valuable inspiring works written by both the conference participants and some other experts in this new and challenging field.

Proceedings of the 8th Python in Science Conference

Proceedings of the 8th Python in Science Conference

  • Author: GaeÌl Varoquaux,Stéfan van der Walt,K. Jarrod Millman
  • Publisher: Lulu.com
  • ISBN: 0557232120
  • Category:
  • Page: 92
  • View: 1046
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The proceedings of the 8th annual Python for Scientific Computing conference.

Quantitative Information Fusion for Hydrological Sciences

Quantitative Information Fusion for Hydrological Sciences

  • Author: Xing Cai,T.-C. Jim Yeh
  • Publisher: Springer Science & Business Media
  • ISBN: 3540753834
  • Category: Science
  • Page: 218
  • View: 7948
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In this rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? Here, two highly qualified scientists edit a volume that takes the angle of computational hydrology and envision one of the science’s future directions – namely, the quantitative integration of high-quality hydrologic field data with geologic, hydrologic, chemical, atmospheric, and biological information to characterize and predict natural systems in hydrological sciences.

Soft Methods for Data Science

Soft Methods for Data Science

  • Author: Maria Brigida Ferraro,Paolo Giordani,Barbara Vantaggi,Marek Gagolewski,María Ángeles Gil,Przemysław Grzegorzewski,Olgierd Hryniewicz
  • Publisher: Springer
  • ISBN: 3319429728
  • Category: Computers
  • Page: 535
  • View: 2348
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This proceedings volume is a collection of peer reviewed papers presented at the 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016) held in Rome (Italy). The book is dedicated to Data science which aims at developing automated methods to analyze massive amounts of data and to extract knowledge from them. It shows how Data science employs various programming techniques and methods of data wrangling, data visualization, machine learning, probability and statistics. The soft methods proposed in this volume represent a collection of tools in these fields that can also be useful for data science.