# Big Data in Astronomy

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## Big Data in Astronomy

Author | : Chris Broekema,Linghe Kong,Tian Huang,Yongxin Zhu,Shenghua Yu |

Publsiher | : Elsevier |

Total Pages | : 438 |

Release | : 2020-06-13 |

ISBN 10 | : 012819085X |

ISBN 13 | : 9780128190852 |

Language | : EN, FR, DE, ES & NL |

**Big Data in Astronomy Book Review:**

Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world’s largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. Bridges the gap between radio astronomy and computer science Includes coverage of the observation lifecycle as well as data collection, processing and analysis Presents state-of-the-art research and techniques in big data related to radio astronomy Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)

## Knowledge Discovery in Big Data from Astronomy and Earth Observation

Author | : Petr Skoda,Fathalrahman Adam |

Publsiher | : Unknown |

Total Pages | : 400 |

Release | : 2020-03 |

ISBN 10 | : 0128191546 |

ISBN 13 | : 9780128191545 |

Language | : EN, FR, DE, ES & NL |

**Knowledge Discovery in Big Data from Astronomy and Earth Observation Book Review:**

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.

## Astronomy and Big Data

Author | : Kieran Jay Edwards,Mohamed Medhat Gaber |

Publsiher | : Springer Science & Business Media |

Total Pages | : 105 |

Release | : 2014-04-12 |

ISBN 10 | : 3319065998 |

ISBN 13 | : 9783319065991 |

Language | : EN, FR, DE, ES & NL |

**Astronomy and Big Data Book Review:**

With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.

## Advances in Machine Learning and Data Mining for Astronomy

Author | : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava |

Publsiher | : CRC Press |

Total Pages | : 744 |

Release | : 2012-03-29 |

ISBN 10 | : 1439841748 |

ISBN 13 | : 9781439841747 |

Language | : EN, FR, DE, ES & NL |

**Advances in Machine Learning and Data Mining for Astronomy Book Review:**

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

## Data Science and Big Data Analytics

Author | : EMC Education Services |

Publsiher | : John Wiley & Sons |

Total Pages | : 432 |

Release | : 2015-01-05 |

ISBN 10 | : 1118876059 |

ISBN 13 | : 9781118876053 |

Language | : EN, FR, DE, ES & NL |

**Data Science and Big Data Analytics Book Review:**

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available from the book’s page at Wiley which you can find on the Wiley site by searching for the ISBN 9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

## Data Science Landscape

Author | : Usha Mujoo Munshi,Neeta Verma |

Publsiher | : Springer |

Total Pages | : 339 |

Release | : 2018-03-01 |

ISBN 10 | : 9811075158 |

ISBN 13 | : 9789811075155 |

Language | : EN, FR, DE, ES & NL |

**Data Science Landscape Book Review:**

The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.

## Astrostatistics

Author | : Gutti Jogesh Babu,E.D. Feigelson |

Publsiher | : CRC Press |

Total Pages | : 224 |

Release | : 1996-08-01 |

ISBN 10 | : 9780412983917 |

ISBN 13 | : 0412983915 |

Language | : EN, FR, DE, ES & NL |

**Astrostatistics Book Review:**

Modern astronomers encounter a vast range of challenging statistical problems, yet few are familiar with the wealth of techniques developed by statisticians. Conversely, few statisticians deal with the compelling problems confronted in astronomy. Astrostatistics bridges this gap. Authored by a statistician-astronomer team, it provides professionals and advanced students in both fields with exposure to issues of mutual interest. In the first half of the book the authors introduce statisticians to stellar, galactic, and cosmological astronomy and discuss the complex character of astronomical data. For astronomers, they introduce the statistical principles of nonparametrics, multivariate analysis, time series analysis, density estimation, and resampling methods. The second half of the book is organized by statistical topic. Each chapter contains examples of problems encountered astronomical research and highlights methodological issues. The final chapter explores some controversial issues in astronomy that have a strong statistical component. The authors provide an extensive bibliography and references to software for implementing statistical methods. The "marriage" of astronomy and statistics is a natural one and benefits both disciplines. Astronomers need the tools and methods of statistics to interpret the vast amount of data they generate, and the issues related to astronomical data pose intriguing challenges for statisticians. Astrostatistics paves the way to improved statistical analysis of astronomical data and provides a common ground for future collaboration between the two fields.

## Big Data

Author | : Viktor Mayer-Schönberger,Kenneth Cukier |

Publsiher | : Houghton Mifflin Harcourt |

Total Pages | : 240 |

Release | : 2013-03-05 |

ISBN 10 | : 0544002938 |

ISBN 13 | : 9780544002937 |

Language | : EN, FR, DE, ES & NL |

**Big Data Book Review:**

A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large. Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak? The key to answering these questions, and many more, is big data. “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior. In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing. www.big-data-book.com

## Statistics Data Mining and Machine Learning in Astronomy

Author | : Željko Ivezić,Andrew J. Connolly,Jacob T VanderPlas,Alexander Gray |

Publsiher | : Princeton University Press |

Total Pages | : 560 |

Release | : 2014-01-12 |

ISBN 10 | : 0691151687 |

ISBN 13 | : 9780691151687 |

Language | : EN, FR, DE, ES & NL |

**Statistics Data Mining and Machine Learning in Astronomy Book Review:**

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

## Nostradamus 2013 Prediction Modeling and Analysis of Complex Systems

Author | : Ivan Zelinka,Guanrong Chen,Otto E. Rössler,Vaclav Snasel,Ajith Abraham |

Publsiher | : Springer Science & Business Media |

Total Pages | : 550 |

Release | : 2013-11-13 |

ISBN 10 | : 3319005421 |

ISBN 13 | : 9783319005423 |

Language | : EN, FR, DE, ES & NL |

**Nostradamus 2013 Prediction Modeling and Analysis of Complex Systems Book Review:**

Prediction of behavior of the dynamical systems, analysis and modeling of its structure is vitally important problem in engineering, economy and science today. Examples of such systems can be seen in the world around us and of course in almost every scientific discipline including such “exotic” domains like the earth’s atmosphere, turbulent fluids, economies (exchange rate and stock markets), population growth, physics (control of plasma), information flow in social networks and its dynamics, chemistry and complex networks. To understand such dynamics and to use it in research or industrial applications, it is important to create its models. For this purpose there is rich spectra of methods, from classical like ARMA models or Box Jenkins method to such modern ones like evolutionary computation, neural networks, fuzzy logic, fractal geometry, deterministic chaos and more. This proceeding book is a collection of the accepted papers to conference Nostradamus that has been held in Ostrava, Czech Republic. Proceeding also comprises of outstanding keynote speeches by distinguished guest speakers: Guanrong Chen (Hong Kong), Miguel A. F. Sanjuan (Spain), Gennady Leonov and Nikolay Kuznetsov (Russia), Petr Škoda (Czech Republic). The main aim of the conference is to create periodical possibility for students, academics and researchers to exchange their ideas and novel methods. This conference will establish forum for presentation and discussion of recent trends in the area of applications of various predictive methods for researchers, students and academics.

## Big Data Analysis New Algorithms for a New Society

Author | : Nathalie Japkowicz,Jerzy Stefanowski |

Publsiher | : Springer |

Total Pages | : 329 |

Release | : 2015-12-16 |

ISBN 10 | : 3319269895 |

ISBN 13 | : 9783319269894 |

Language | : EN, FR, DE, ES & NL |

**Big Data Analysis New Algorithms for a New Society Book Review:**

This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.

## The Last Stargazers

Author | : Emily Levesque |

Publsiher | : Sourcebooks, Inc. |

Total Pages | : 336 |

Release | : 2020-08-04 |

ISBN 10 | : 1492681083 |

ISBN 13 | : 9781492681083 |

Language | : EN, FR, DE, ES & NL |

**The Last Stargazers Book Review:**

The story of the people who see beyond the stars Humans from the earliest civilizations were spellbound by the night sky-craning their necks each night, they used the stars to orient themselves in the large, strange world around them. Stargazing is a pursuit that continues to fascinate us: from Copernicus to Carl Sagan, astronomers throughout history have spent their lives trying to answer the biggest questions in the universe. Now, award-winning astronomer Emily Levesque shares the stories of modern-day stargazers, the people willing to adventure across high mountaintops and to some of the most remote corners of the planet, all in the name of science. From the lonely quiet of midnight stargazing to tall tales of wild bears loose in the observatory, The Last Stargazers is a love letter to astronomy and an affirmation of the crucial role that humans can and must play in the future of scientific discovery. In this sweeping work of narrative science, Levesque shows how astronomers in this scrappy and evolving field are going beyond the machines to infuse creativity and passion into the stars and inspires us all to peer skyward in pursuit of the universe's secrets.

## Cognitive Computing and Big Data Analytics

Author | : Judith S. Hurwitz,Marcia Kaufman,Adrian Bowles |

Publsiher | : John Wiley & Sons |

Total Pages | : 288 |

Release | : 2015-04-08 |

ISBN 10 | : 1118896785 |

ISBN 13 | : 9781118896785 |

Language | : EN, FR, DE, ES & NL |

**Cognitive Computing and Big Data Analytics Book Review:**

A comprehensive guide to learning technologies that unlock thevalue in big data Cognitive Computing provides detailed guidance towardbuilding a new class of systems that learn from experience andderive insights to unlock the value of big data. This book helpstechnologists understand cognitive computing's underlyingtechnologies, from knowledge representation techniques and naturallanguage processing algorithms to dynamic learning approaches basedon accumulated evidence, rather than reprogramming. Detailed caseexamples from the financial, healthcare, and manufacturing walkreaders step-by-step through the design and testing of cognitivesystems, and expert perspectives from organizations such asCleveland Clinic, Memorial Sloan-Kettering, as well as commercialvendors that are creating solutions. These organizations provideinsight into the real-world implementation of cognitive computingsystems. The IBM Watson cognitive computing platform is describedin a detailed chapter because of its significance in helping todefine this emerging market. In addition, the book includesimplementations of emerging projects from Qualcomm, Hitachi, Googleand Amazon. Today's cognitive computing solutions build on establishedconcepts from artificial intelligence, natural language processing,ontologies, and leverage advances in big data management andanalytics. They foreshadow an intelligent infrastructure thatenables a new generation of customer and context-aware smartapplications in all industries. Cognitive Computing is a comprehensive guide to thesubject, providing both the theoretical and practical guidancetechnologists need. Discover how cognitive computing evolved from promise toreality Learn the elements that make up a cognitive computingsystem Understand the groundbreaking hardware and softwaretechnologies behind cognitive computing Learn to evaluate your own application portfolio to find thebest candidates for pilot projects Leverage cognitive computing capabilities to transform theorganization Cognitive systems are rightly being hailed as the new era ofcomputing. Learn how these technologies enable emerging firms tocompete with entrenched giants, and forward-thinking establishedfirms to disrupt their industries. Professionals who currently workwith big data and analytics will see how cognitive computing buildson their foundation, and creates new opportunities. CognitiveComputing provides complete guidance to this new level ofhuman-machine interaction.

## The Big R Book

Author | : Philippe J. S. De Brouwer |

Publsiher | : John Wiley & Sons |

Total Pages | : 928 |

Release | : 2020-09-29 |

ISBN 10 | : 1119632765 |

ISBN 13 | : 9781119632764 |

Language | : EN, FR, DE, ES & NL |

**The Big R Book Book Review:**

Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

## Big Data Analytics

Author | : Saumyadipta Pyne,B.L.S. Prakasa Rao,S.B. Rao |

Publsiher | : Springer |

Total Pages | : 276 |

Release | : 2016-10-12 |

ISBN 10 | : 8132236289 |

ISBN 13 | : 9788132236283 |

Language | : EN, FR, DE, ES & NL |

**Big Data Analytics Book Review:**

This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

## Doing Data Science

Author | : Cathy O'Neil,Rachel Schutt |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 408 |

Release | : 2013-10-09 |

ISBN 10 | : 144936389X |

ISBN 13 | : 9781449363895 |

Language | : EN, FR, DE, ES & NL |

**Doing Data Science Book Review:**

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

## Big Data Science in Finance

Author | : Irene Aldridge,Marco Avellaneda |

Publsiher | : John Wiley & Sons |

Total Pages | : 336 |

Release | : 2021-01-08 |

ISBN 10 | : 1119602971 |

ISBN 13 | : 9781119602972 |

Language | : EN, FR, DE, ES & NL |

**Big Data Science in Finance Book Review:**

Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

## Statistical Methods for Astronomical Data Analysis

Author | : Asis Kumar Chattopadhyay,Tanuka Chattopadhyay |

Publsiher | : Springer |

Total Pages | : 349 |

Release | : 2014-10-01 |

ISBN 10 | : 149391507X |

ISBN 13 | : 9781493915071 |

Language | : EN, FR, DE, ES & NL |

**Statistical Methods for Astronomical Data Analysis Book Review:**

This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for astronomical problems, including regression techniques, along with their usefulness for data set problems related to size and dimension. Analysis of missing data is an important part of the book because of its significance for work with astronomical data. Both existing and new techniques related to dimension reduction and clustering are illustrated through examples. There is detailed coverage of applications useful for classification, discrimination, data mining and time series analysis. Later chapters explain simulation techniques useful for the development of physical models where it is difficult or impossible to collect data. Finally, coverage of the many R programs for techniques discussed makes this book a fantastic practical reference. Readers may apply what they learn directly to their data sets in addition to the data sets included by the authors.

## Big Data Data Mining and Machine Learning

Author | : Jared Dean |

Publsiher | : John Wiley & Sons |

Total Pages | : 288 |

Release | : 2014-05-07 |

ISBN 10 | : 1118920708 |

ISBN 13 | : 9781118920701 |

Language | : EN, FR, DE, ES & NL |

**Big Data Data Mining and Machine Learning Book Review:**

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

## Astronomical Image and Data Analysis

Author | : J.-L. Starck,F. Murtagh |

Publsiher | : Springer Science & Business Media |

Total Pages | : 338 |

Release | : 2007-06-21 |

ISBN 10 | : 3540330259 |

ISBN 13 | : 9783540330257 |

Language | : EN, FR, DE, ES & NL |

**Astronomical Image and Data Analysis Book Review:**

With information and scale as central themes, this comprehensive survey explains how to handle real problems in astronomical data analysis using a modern arsenal of powerful techniques. It treats those innovative methods of image, signal, and data processing that are proving to be both effective and widely relevant. The authors are leaders in this rapidly developing field and draw upon decades of experience. They have been playing leading roles in international projects such as the Virtual Observatory and the Grid. The book addresses not only students and professional astronomers and astrophysicists, but also serious amateur astronomers and specialists in earth observation, medical imaging, and data mining. The coverage includes chapters or appendices on: detection and filtering; image compression; multichannel, multiscale, and catalog data analytical methods; wavelets transforms, Picard iteration, and software tools. This second edition of Starck and Murtagh's highly appreciated reference again deals with topics that are at or beyond the state of the art. It presents material which is more algorithmically oriented than most alternatives and broaches new areas like ridgelet and curvelet transforms. Throughout the book various additions and updates have been made.