Author | : Hakan Alyuruk |

Publisher | : Elsevier |

Release Date | : 2019-06-09 |

ISBN 10 | : 0128134925 |

Pages | : 186 pages |

R and Python for Oceanographers: A Practical Guide with Applications describes the uses of scientific Python packages and R in oceanographic data analysis, including both script codes and graphic outputs. Each chapter begins with theoretical background that is followed by step-by-step examples of software applications, including scripts, graphics, tables and practical exercises for better understanding of the subject. Examples include frequently used data analysis approaches in physical and chemical oceanography, but also contain topics on data import/export and GIS mapping. The examples seen in book provide uses of the latest versions of Python and R libraries. Presents much needed oceanographic data analysis approaches to chemical and physical oceanography Includes examples with software applications (based on Python and R), including free software for the analysis of oceanographic data Provides guidance on how to get started, along with guidance on example code and output

Author | : Chantal D. Larose,Daniel T. Larose |

Publisher | : Wiley |

Release Date | : 2019-04-09 |

ISBN 10 | : 1119526817 |

Pages | : 240 pages |

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Author | : Samuel S. P. Shen,Richard C. J. Somerville |

Publisher | : Cambridge University Press |

Release Date | : 2019-09-30 |

ISBN 10 | : 1108476872 |

Pages | : 456 pages |

Presents the core mathematics, statistics, and programming skills needed for modern climate science courses, with online teaching materials.

Author | : Dan E. Kelley |

Publisher | : Springer |

Release Date | : 2018-10-17 |

ISBN 10 | : 1493988441 |

Pages | : 290 pages |

This book presents the R software environment as a key tool for oceanographic computations and provides a rationale for using R over the more widely-used tools of the field such as MATLAB. Kelley provides a general introduction to R before introducing the ‘oce’ package. This package greatly simplifies oceanographic analysis by handling the details of discipline-specific file formats, calculations, and plots. Designed for real-world application and developed with open-source protocols, oce supports a broad range of practical work. Generic functions take care of general operations such as subsetting and plotting data, while specialized functions address more specific tasks such as tidal decomposition, hydrographic analysis, and ADCP coordinate transformation. In addition, the package makes it easy to document work, because its functions automatically update processing logs stored within its data objects. Kelley teaches key R functions using classic examples from the history of oceanography, specifically the work of Alfred Redfield, Gordon Riley, J. Tuzo Wilson, and Walter Munk. Acknowledging the pervasive popularity of MATLAB, the book provides advice to users who would like to switch to R. Including a suite of real-life applications and over 100 exercises and solutions, the treatment is ideal for oceanographers, technicians, and students who want to add R to their list of tools for oceanographic analysis.

Author | : Ajay Ohri |

Publisher | : John Wiley & Sons |

Release Date | : 2017-11-13 |

ISBN 10 | : 1119126762 |

Pages | : 368 pages |

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

Author | : John Paul Mueller,Luca Massaron |

Publisher | : John Wiley & Sons |

Release Date | : 2019-01-29 |

ISBN 10 | : 1119547660 |

Pages | : 496 pages |

The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Author | : Management Association, Information Resources |

Publisher | : IGI Global |

Release Date | : 2018-11-02 |

ISBN 10 | : 1522573097 |

Pages | : 469 pages |

To date, a vast amount of the world’s oceans remains uncharted. With water covering more than 70 percent of the Earth’s surface, maritime and oceanographic exploration and research is vital. Oceanography and Coastal Informatics: Breakthroughs in Research and Practice is a critical source of academic knowledge centered on technologies, methodologies, and practices related to the biological and physical aspects of the ocean and coastal environments. This publication is divided into four sections: climate change and environmental concerns; data analysis and management; fisheries management and ecology; and GIS, geospatial analysis, and localization. This publication is an ideal reference source for oceanographers, marine and maritime professionals, researchers, and scholars interested in current research on various aspects of oceanography and coastal informatics.

Author | : Thomas W. Miller |

Publisher | : FT Press |

Release Date | : 2015-05-02 |

ISBN 10 | : 0133887340 |

Pages | : 225 pages |

Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

Author | : H. Jean Thiebaux |

Publisher | : Elsevier |

Release Date | : 2013-10-22 |

ISBN 10 | : 0080926290 |

Pages | : 247 pages |

Studies of local and global phenomena generate descriptions which require statistical analysis. In this text, H. Jean Thiebaux presents a succinct yet comprehensive review of the fundamentals of statistics as they pertain to studies in oceanic and atmospheric sciences. The text includes an accompanying disk with compatible Minitab sample data. Together, this volume and the included data provide insights into the basics of statistical inference, data analysis, and distributional models of variability. Oceanographers, meteorologists, marine biologists, and other environmental scientists will find this book of great value as a statistical tool for their continuing studies. Key Features * Specifically designed for students of the ocean and atmospheric sciences * Contains a disk containing files of real ocean and atmospheric data, in universal ASCII format, on which many of the exercises are based * Provides succinct yet comprehensive coverage * Designed to teach students statistical methods with the scientific realism of computer analysis and statistical inference

Author | : Vyacheslav Lyubchich,Yulia Gel,K. Halimeda Kilbourne,Thomas James Miller,Nathaniel K. Newlands,A. Smith |

Publisher | : CRC Press |

Release Date | : 2020-10-07 |

ISBN 10 | : 1351190814 |

Pages | : 382 pages |

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation, climate modeling, and long-term prediction; approach the problems of increasing frequency of extreme events, sea level rise, and forest fires, as well as economic losses, analysis of climate impacts for insurance, agriculture, fisheries, and electric and transport infrastructures. The readers will be exposed to the current research using a variety of methods from physical modeling, statistics, and machine learning, including the global circulation models (GCM) and ocean models, statistical generalized additive models (GAM) and generalized linear models (GLM), state space and graphical models, causality networks, Bayesian ensembles, a variety of index methods and statistical tests, and machine learning methods. The reader will learn about data from various sources, including GCM and ocean model outputs, satellite observations, and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

Author | : Ajay Ohri |

Publisher | : John Wiley & Sons |

Release Date | : 2019-09-24 |

ISBN 10 | : 1119256410 |

Pages | : 208 pages |

BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.

Author | : Johnny Wei-Bing Lin |

Publisher | : Lulu.com |

Release Date | : 2012 |

ISBN 10 | : 130007616X |

Pages | : 186 pages |

This book is a mini-course for researchers in the atmospheric and oceanic sciences. "We assume readers will already know the basics of programming... in some other language." - Back cover.

Author | : Barry A. Klinger,Thomas W. N. Haine |

Publisher | : Cambridge University Press |

Release Date | : 2019-03-31 |

ISBN 10 | : 0521768438 |

Pages | : 494 pages |

An innovative survey of large-scale ocean circulation that links observations, conceptual models, numerical models, and theories.

Author | : John Paul Mueller,Luca Massaron |

Publisher | : John Wiley & Sons |

Release Date | : 2020-01-09 |

ISBN 10 | : 1119626110 |

Pages | : 768 pages |

Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

Author | : Cay S. Horstmann,Rance D. Necaise |

Publisher | : Wiley Global Education |

Release Date | : 2019-02-21 |

ISBN 10 | : 1119498619 |

Pages | : 761 pages |

Python for Everyone, 3rd Edition is an introduction to programming designed to serve a wide range of student interests and abilities, focused on the essentials, and on effective learning. It is suitable for a first course in programming for computer scientists, engineers, and students in other disciplines. This text requires no prior programming experience and only a modest amount of high school algebra. Objects are used where appropriate in early chapters and students start designing and implementing their own classes in Chapter 9. New to this edition are examples and exercises that focus on various aspects of data science.

Author | : Jay Jacobs,Bob Rudis |

Publisher | : John Wiley & Sons |

Release Date | : 2014-01-24 |

ISBN 10 | : 111879382X |

Pages | : 352 pages |

Uncover hidden patterns of data and respond withcountermeasures Security professionals need all the tools at their disposal toincrease their visibility in order to prevent security breaches andattacks. This careful guide explores two of the most powerful data analysis and visualization. You'll soon understand how toharness and wield data, from collection and storage to managementand analysis as well as visualization and presentation. Using ahands-on approach with real-world examples, this book shows you howto gather feedback, measure the effectiveness of your securitymethods, and make better decisions. Everything in this book will have practical application forinformation security professionals. Helps IT and security professionals understand and use data, sothey can thwart attacks and understand and visualizevulnerabilities in their networks Includes more than a dozen real-world examples and hands-onexercises that demonstrate how to analyze security data andintelligence and translate that information into visualizationsthat make plain how to prevent attacks Covers topics such as how to acquire and prepare security data,use simple statistical methods to detect malware, predict roguebehavior, correlate security events, and more Written by a team of well-known experts in the field ofsecurity and data analysis Lock down your networks, prevent hacks, and thwart malware byimproving visibility into the environment, all through the power ofdata and Security Using Data Analysis, Visualization, andDashboards.

Author | : Monin,Ozmidov |

Publisher | : Springer Science & Business Media |

Release Date | : 2012-12-06 |

ISBN 10 | : 9400952171 |

Pages | : 248 pages |

Four years have elapsed since the preparation of the original Russian version of this book. This is a long time when dealing with such actively expanding fields of oceanography as research into small-scale structures and the investigation of hydro physical processes. Over this period new quick-response devices have been developed and successfully used for measurements taken in various ocean areas. Improvements in high-frequency meters used to measure hydrophysical parameters has enabled workers to obtain more accurate absolute values of the fluctuations measured by such devices. In view of this scientific progress, some of the ideas presented in this book now require additional explanation. Great care should be used in dealing with the absolute fluctuation values of hydro physical fields, since the methods used for the determination of the accuracy of the high-frequency measuring devices have been imperfect in the past. Never theless, it would appear that the results of the investigations summarized in this book have not lost their importance, and that the established laws governing small-scale pro cesses in the ocean are of a sufficiently universal nature and, as such, have not been shattered with the qualitative and quantitative advances in devices used for measurements taken in oceans. The authors feel that their work is of interest to English-speaking readers. The appearance of the English translation of the book is, to a very large extent, due to the tremendous amount of editing work brilliantly done by Prof. H. Tennekes.

Author | : R.E. Zeebe,D. Wolf-Gladrow |

Publisher | : Elsevier |

Release Date | : 2001-10-15 |

ISBN 10 | : 9780080529226 |

Pages | : 360 pages |

Carbon dioxide is the most important greenhouse gas after water vapor in the atmosphere of the earth. More than 98% of the carbon of the atmosphere-ocean system is stored in the oceans as dissolved inorganic carbon. The key for understanding critical processes of the marine carbon cycle is a sound knowledge of the seawater carbonate chemistry, including equilibrium and nonequilibrium properties as well as stable isotope fractionation. Presenting the first coherent text describing equilibrium and nonequilibrium properties and stable isotope fractionation among the elements of the carbonate system. This volume presents an overview and a synthesis of these subjects which should be useful for graduate students and researchers in various fields such as biogeochemistry, chemical oceanography, paleoceanography, marine biology, marine chemistry, marine geology, and others. The volume includes an introduction to the equilibrium properties of the carbonate system in which basic concepts such as equilibrium constants, alkalinity, pH scales, and buffering are discussed. It also deals with the nonequilibrium properties of the seawater carbonate chemistry. Whereas principle of chemical kinetics are recapitulated, reaction rates and relaxation times of the carbonate system are considered in details. The book also provides a general introduction to stable isotope fractionation and describes the partitioning of carbon, oxygen, and boron isotopes between the species of the carbonate system. The appendix contains formulas for the equilibrium constants of the carbonate system, mathematical expressions to calculate carbonate system parameters, answers to exercises and more.

Author | : Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser |

Publisher | : Wiley Global Education |

Release Date | : 2013-03-08 |

ISBN 10 | : 1118476735 |

Pages | : 748 pages |

Based on the authors market leading data structures books in Java and C++, this textbook offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for the Python data structures course. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.

Author | : Rudolph W. Preisendorfer |

Publisher | : Elsevier Science Limited |

Release Date | : 1988 |

ISBN 10 | : 9780444430144 |

Pages | : 425 pages |

This book, the only one of its kind available, presents PCA from its simplest form through its abstract formalism, including applications. Furthermore, it extends the use of PCA far beyond its well-known applications to scalar (e.g. temperature) or vector (e.g. wind) fields. Much of the material is hitherto unpublished, thus greatly extending the realm of applicability of PCA, and many suggestions are made for its future application. The first half of the book provides a comprehensive discussion of PCA, including solved numerical examples, beginning with a simple bivariate data set and progressing to the PCA of multivariate fields. The use of selection rules to establish statistical significance is emphasized. The second half of the book compares PCA with other analysis techniques such as Factor Analysis, Linear Regression Analysis, and Canonical Factor Analysis. The book also discusses the use of PCA in construction of statistical-dynamical models, in the detection of moving patterns in data sets, and in studies of stationary random processes.