Big Data in Astronomy

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

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author: Petr Skoda,Fathalrahman Adam
Publsiher: Anonim
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

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

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

Advances in Self-Organizing Maps and Learning Vector Quantization

Advances in Self-Organizing Maps and Learning Vector Quantization
Author: Erzsébet Merényi,Michael J. Mendenhall,Patrick O'Driscoll
Publsiher: Springer
Total Pages: 370
Release: 2016-01-07
ISBN 10: 3319285181
ISBN 13: 9783319285184
Language: EN, FR, DE, ES & NL

Advances in Self-Organizing Maps and Learning Vector Quantization Book Review:

This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.

Statistics, Data Mining, and Machine Learning in Astronomy

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

Astrostatistics

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.

The Last Stargazers

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.

Big Data, Little Data, No Data

Big Data, Little Data, No Data
Author: Christine L. Borgman
Publsiher: MIT Press
Total Pages: 416
Release: 2015-01-02
ISBN 10: 0262327872
ISBN 13: 9780262327879
Language: EN, FR, DE, ES & NL

Big Data, Little Data, No Data Book Review:

An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities. “Big Data” is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data—because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure—an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation—six “provocations” meant to inspire discussion about the uses of data in scholarship—Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

Data Science and Big Data Analytics

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 at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Doing Data Science

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.

The Intelligent Enterprise in the Era of Big Data

The Intelligent Enterprise in the Era of Big Data
Author: Venkat Srinivasan
Publsiher: John Wiley & Sons
Total Pages: 216
Release: 2016-10-10
ISBN 10: 1118834623
ISBN 13: 9781118834626
Language: EN, FR, DE, ES & NL

The Intelligent Enterprise in the Era of Big Data Book Review:

“ … the enterprise of today has changed … wherever you sit in this new corporation … Srinivasan gives us a practical and provocative guide for rethinking our business process … calling us all to action around rapid development of our old, hierarchical structures into flexible customer centric competitive force …. A must read for today’s business leader.” Mark Nunnelly, Executive Director, MassIT, Commonwealth of Massachusetts and Managing Director, Bain Capital “’Efficiency,’ ‘agile,’ and ‘analytics’ used to be the rage. Venkat Srinivasan explains in this provocative book why organizations can no longer afford to stop there. They need to move beyond – to be ‘intelligent.’ It isn’t just theory. He’s done it.” Bharat Anand, Henry R. Byers Professor of Business Administration, Harvard Business School In the era of big data and automation, the book presents a cutting-edge approach to how enterprises should organize and function. Striking a practical balance between theory and practice, The Intelligent Enterprise in the Era of Big Data presents the enterprise architecture that identifies the power of the emerging technology environment. Beginning with an introduction to the key challenges that enterprises face, the book systematically outlines modern enterprise architecture through a detailed discussion of the inseparable elements of such architecture: efficiency, flexibility, and intelligence. This architecture enables rapid responses to market needs by sensing important developments in internal and external environments in real time. Illustrating all of these elements in an integrated fashion, The Intelligent Enterprise in the Era of Big Data also features: • A detailed discussion on issues of time-to-market and flexibility with respect to enterprise application technology • Novel analyses illustrated through extensive real-world case studies to help readers better understand the applicability of the architecture and concepts • Various applications of natural language processing to real-world business transactions • Practical approaches for designing and building intelligent enterprises The Intelligent Enterprise in the Era of Big Data is an appropriate reference for business executives, information technology professionals, data scientists, and management consultants. The book is also an excellent supplementary textbook for upper-undergraduate and graduate-level courses in business intelligence, data mining, big data, and business process automation. “a compelling vision of the next generation of organization—the intelligent enterprise—which will leverage not just big data but also unstructured text and artificial intelligence to optimize internal processes in real time … a must-read book for CEOs and CTOs in all industries.” Ravi Ramamurti, D”Amore-McKim Distinguished Professor of International Business and Strategy, and Director, Center for Emerging Markets, Northeastern University “It is about the brave new world that narrows the gap between technology and business …. The book has practical advice from a thoughtful practitioner. Intelligent automation will be a competitive strength in the future. Will your company be ready?” Victor J. Menezes, Retired Senior Vice Chairman, Citigroup Venkat Srinivasan, PhD, is Chairman and Chief Executive Officer of RAGE Frameworks, Inc., which supports the creation of intelligent business process automation solutions and cognitive intelligence solutions for global corporations. He is an entrepreneur and holds several patents in the area of knowledge-based technology architectures. He is the author of two edited volumes and over 30 peer-reviewed publications. He has served as an associate professor in the College of Business Administration at Northeastern University.

Spatial Big Data Science

Spatial Big Data Science
Author: Zhe Jiang,Shashi Shekhar
Publsiher: Springer
Total Pages: 131
Release: 2017-07-13
ISBN 10: 3319601954
ISBN 13: 9783319601953
Language: EN, FR, DE, ES & NL

Spatial Big Data Science Book Review:

Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.

Big Data Meets Survey Science

Big Data Meets Survey Science
Author: Craig A. Hill,Paul P. Biemer,Trent D. Buskirk,Lilli Japec,Antje Kirchner,Stas Kolenikov,Lars E. Lyberg
Publsiher: John Wiley & Sons
Total Pages: 800
Release: 2020-09-29
ISBN 10: 1118976320
ISBN 13: 9781118976326
Language: EN, FR, DE, ES & NL

Big Data Meets Survey Science Book Review:

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

Astronomical Data Analysis Software and Systems XXVI

Astronomical Data Analysis Software and Systems XXVI
Author: Marco Molinaro,Keith Shortridge,Fabio Pasian
Publsiher: Anonim
Total Pages: 788
Release: 2019
ISBN 10: 9781583819302
ISBN 13: 1583819304
Language: EN, FR, DE, ES & NL

Astronomical Data Analysis Software and Systems XXVI Book Review:

Big Data Analytics in Genomics

Big Data Analytics in Genomics
Author: Ka-Chun Wong
Publsiher: Springer
Total Pages: 428
Release: 2016-10-24
ISBN 10: 3319412795
ISBN 13: 9783319412795
Language: EN, FR, DE, ES & NL

Big Data Analytics in Genomics Book Review:

This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.

Data Science Landscape

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.

Machine Learning for Planetary Science

Machine Learning for Planetary Science
Author: Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publsiher: Elsevier
Total Pages: 400
Release: 2021-03-01
ISBN 10: 0128187220
ISBN 13: 9780128187227
Language: EN, FR, DE, ES & NL

Machine Learning for Planetary Science Book Review:

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Working Papers

Working Papers
Author: National Research Council,Division on Engineering and Physical Sciences,Commission on Physical Sciences, Mathematics, and Applications,Board on Physics and Astronomy,Astronomy and Astrophysics Survey Committee
Publsiher: National Academies Press
Total Pages: 356
Release: 1991-02-01
ISBN 10: 0309043832
ISBN 13: 9780309043830
Language: EN, FR, DE, ES & NL

Working Papers Book Review:

This volume contains working papers on astronomy and astrophysics prepared by 15 non-National Research Council panels in areas ranging from radio astronomy to the status of the profession.

The Astronomy Book

The Astronomy Book
Author: DK
Publsiher: Dorling Kindersley Ltd
Total Pages: 352
Release: 2017-09-07
ISBN 10: 0241322758
ISBN 13: 9780241322758
Language: EN, FR, DE, ES & NL

The Astronomy Book Book Review:

Explore the world of astronomy with key quotes and bold graphics to illustrate over 100 of the universe's biggest ideas. The Astronomy Book is an exciting voyage of discovery through the cosmos. Venture from ancient speculations about the nature of the universe, to the mind-boggling theories of recent science, including those of Albert Einstein and Stephen Hawking. Learn about the incredible histories of Halley's Comet, the Hubble telescope, and NASA's modern-day trailblazing, as well as the discoveries of famous figures including Ptolemy, Isaac Newton, Walter Adams, Carl Sagan, and Alan Stern. The Astronomy Book, part of DK's popular "Big Ideas" series, is is the perfect introduction to our ideas about space, time, and the physics of the cosmos.