Products related to Data-analysis:
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Learn Data Analysis with Python : Lessons in Coding
Get started using Python in data analysis with this compact practical guide.This book includes three exercises and a case study on getting data in and out of Python code in the right format.Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate.If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python.You can then take these techniques and apply them directly to your own projects. If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic.As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished. What You Will LearnGet data into and out of Python codePrepare the data and its formatFind the meaning of the dataVisualize the data using iPythonWho This Book Is For Those who want to learn data analysis using Python.Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.
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Multivariate Data Analysis
For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis.The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today’s world of analytics. Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to multivariate analysis for the non-statistician.By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.
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Bayesian Data Analysis
Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems.Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods.The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods.Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modelingCoverage of weakly informative priors and boundary-avoiding priorsUpdated discussion of cross-validation and predictive information criteriaImproved convergence monitoring and effective sample size calculations for iterative simulationPresentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagationNew and revised software codeThe book can be used in three different ways.For undergraduate students, it introduces Bayesian inference starting from first principles.For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields.For researchers, it provides an assortment of Bayesian methods in applied statistics.Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
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Dyadic Data Analysis
Interpersonal phenomena such as attachment, conflict, person perception, learning, and influence have traditionally been studied by examining individuals in isolation, which falls short of capturing their truly interpersonal nature.This book offers state-of-the-art solutions to this age-old problem by presenting methodological and data-analytic approaches useful in investigating processes that take place among dyads: couples, coworkers, parent and child, teacher and student, or doctor and patient, to name just a few.Rich examples from psychology and across the behavioral and social sciences help build the researcher's ability to conceptualize relationship processes; model and test for actor effects, partner effects, and relationship effects; and model and control for the statistical interdependence that can exist between partners.The companion website provides clarifications, elaborations, corrections, and data and files for each chapter.
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How can one study data analysis and data management?
One can study data analysis and data management through a variety of methods. This can include enrolling in formal education programs such as a degree in data science, statistics, or computer science. Additionally, there are many online courses and tutorials available that cover data analysis and data management. Practical experience can also be gained through internships or working on real-world projects. It's important to continuously practice and apply the concepts learned through these methods to gain a deeper understanding of data analysis and data management.
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What exactly does a specialist in information technology for data and process analysis do?
A specialist in information technology for data and process analysis is responsible for analyzing and interpreting data to identify trends, patterns, and insights that can be used to improve business processes and decision-making. They use various tools and techniques to collect, clean, and analyze data, and then present their findings to stakeholders in a clear and understandable manner. They also work to identify areas for process improvement and develop strategies to optimize workflows and increase efficiency. Overall, their role is to leverage technology and data to drive informed decision-making and improve business operations.
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Does anyone have experience with further education in data analysis?
Yes, I have experience with further education in data analysis. I have completed a certification program in data analysis and have also taken advanced courses in statistics, programming, and data visualization. This education has provided me with the skills and knowledge necessary to work with large datasets, perform complex analysis, and communicate insights effectively. I have also gained practical experience through projects and internships, which have further solidified my understanding of data analysis concepts and techniques.
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What is the difference between web development and software development?
Web development primarily focuses on creating websites and web applications that are accessed through a web browser, using technologies such as HTML, CSS, and JavaScript. Software development, on the other hand, involves creating applications that can run on various platforms, including desktop computers, mobile devices, and servers. While web development is a subset of software development, software development encompasses a broader range of applications beyond just web-based ones.
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Categorical Data Analysis
Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries.Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses.Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data.This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects modelsTwo new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysisNew sections introducing the Bayesian approach for methods in that chapterMore than 100 analyses of data sets and over 600 exercisesNotes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sourcesA supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
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Paleontological Data Analysis
PALEONTOLOGICAL DATA ANALYSIS An up-to-date edition of the indispensable guide to analysing paleontological data Paleontology has developed in recent decades into an increasingly data-driven discipline, which brings to bear a huge variety of statistical tools.Applying statistical methods to paleontological data requires a discipline-specific understanding of which methods and parameters are the most appropriate ones, and how to account for statistical bias inherent in the fossil record.By guiding the reader to these and other fundamental questions in the statistical analysis of fossilized specimens, Paleontological Data Analysis has become the standard text for anyone with an interest in quantitative analysis of the fossil record.Now fully updated to reflect the latest statistical methods and disciplinary advances, it is an essential tool for practitioners and students alike. Readers of the second edition of Paleontological Data Analysis readers will also find: New sections on machine learning, Bayesian inference, phylogenetic comparative methods, analysis of CT data, and much more New use cases and examples using PAST, R, and Python software packages Full color illustrations throughout Paleontological Data Analysis is ideal for paleontologists, evolutionary biologists, taxonomists, and students in any of these fields.
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Genome Data Analysis
This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills.Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses.In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field.However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
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Longitudinal Data Analysis
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature.Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications.It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data.Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters.Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature.They seamlessly blend theory with applications and include examples and case studies from various disciplines.Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future.Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.
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What exactly does a specialist in data and process analysis do?
A specialist in data and process analysis is responsible for examining and interpreting data to identify trends, patterns, and insights that can help improve business processes and decision-making. They use various analytical tools and techniques to gather, clean, and analyze data sets to extract valuable information. Additionally, they work closely with stakeholders to understand business requirements and develop strategies for optimizing processes based on data-driven insights. Overall, their role is crucial in helping organizations make informed decisions and drive continuous improvement.
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Can someone give me tips for the statistical analysis of test data?
When conducting statistical analysis of test data, it's important to start by clearly defining the research question or hypothesis you want to test. Then, choose the appropriate statistical tests based on the type of data you have (e.g., categorical, continuous) and the specific research question. Ensure that your data is clean and free from errors before conducting any analysis. Finally, interpret the results in the context of your research question and consider the practical significance of the findings. It's also helpful to consult with a statistician or data analysis expert if you're unsure about the best approach for your specific data and research question.
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What training opportunities are available for IT specialists to specialize in web development and general programming, such as software development?
There are various training opportunities available for IT specialists looking to specialize in web development and general programming. Online platforms like Coursera, Udemy, and Codecademy offer courses and certifications in programming languages such as Python, Java, and JavaScript. Additionally, coding bootcamps like General Assembly and Flatiron School provide intensive, hands-on training in software development. IT specialists can also pursue advanced degrees in computer science or related fields to deepen their knowledge and skills in web development and programming.
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What training options are available for IT specialists to specialize in web development and general programming, such as software development?
IT specialists looking to specialize in web development and general programming have a variety of training options available to them. They can pursue formal education through degree programs in computer science or related fields, attend coding bootcamps that offer intensive training in programming languages and frameworks, or take online courses and tutorials to learn specific skills. Additionally, IT specialists can participate in workshops, seminars, and conferences to stay updated on the latest trends and technologies in web development and software development. Continuous learning and practice are essential for IT specialists to excel in these specialized areas.
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