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Graphical Exploratory Data Analysis
Portraying data graphically certainly contributes toward a clearer and more penetrative understanding of data and also makes sophisticated statistical data analyses more marketable. This realization has emerged from many years of experience in teaching students, in research, and especially from engaging in statistical consulting work in a variety of subject fields. Consequently, we were somewhat surprised to discover that a comprehen sive, yet simple presentation of graphical exploratory techniques for the data analyst was not available. Generally books on the subject were either too incomplete, stopping at a histogram or pie chart, or were too technical and specialized and not linked to readily available computer programs. Many of these graphical techniques have furthermore only recently appeared in statis tical journals and are thus not easily accessible to the statistically unsophis ticated data analyst. This book, therefore, attempts to give a sound overview of most of the well-known and widely used methods of analyzing and portraying data graph ically. Throughout the book the emphasis is on exploratory techniques. Real izing the futility of presenting these methods without the necessary computer programs to actually perform them, we endeavored to provide working com puter programs in almost every case. Graphic representations are illustrated throughout by making use of real-life data. Two such data sets are frequently used throughout the text. In realizing the aims set out above we avoided intricate theoretical derivations and explanations but we nevertheless are convinced that this book will be of inestimable value even to a trained statistician.
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Studying Complex Interactions and Outcomes Through Qualitative Comparative Analysis
Studying Complex Interactions and Outcomes Through Qualitative Comparative Analysis: A Practical Guide to Comparative Case Studies and Ethnographic Data Analysis offers practical, methodological, and theoretically robust guidelines to systematically study the causalities, dynamics, and outcomes of complex social interactions in multiple source data sets. It demonstrates how to convert data from multisited ethnography of investment politics, mobilizations, and citizen struggles into a Qualitative Comparative Analysis (QCA). In this book, Markus Kröger focuses on how data collected primarily via multisited political ethnography, supplemented by other materials and verified by multiple forms of triangulation, can be systematically analyzed through QCA. The results of this QCA offer insight on how to study the political and economic outcomes in natural resource conflicts, across different contexts and political systems. This book applies the method in practice using examples from the author’s own research. With a focus on social movement studies, it shows how QCA can be used to analyze a multiple data source database, that includes results from multiple case studies.This book is a practical guide for researchers and students in social movement studies and other disciplines that produce ethnographic data from multiple sources on how to analyze complex databases through the QCA.
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Qualitative Analysis for Planning & Policy
The revised and updated second edition of Qualitative Analysis for Planning & Policy is a roadmap to help planners access qualitative data and integrate it into their planning investigations. Planning and policy decisions are not based solely on numbers, and this book equips planners with a how-to guide to see what has been missing "between the lines" of quantitative data and make good decisions using the best possible information.Each chapter offers step-by-step instruction on how to set up and enact diverse types of qualitative research, and case studies demonstrate how qualitative research techniques can be combined with quantitative methods to tackle complex real-world projects.For over a decade Qualitative Analysis for Planning & Policy has been an indispensable resource for students and researchers, experienced and novice planners. The revised second edition offers myriad tools to help twenty-first-century planners make intelligent decisions, including new qualitative research techniques, technological innovations, and contemporary case studies.
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Art of Data Analysis
A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning.
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Design and Analysis of Clinical Nursing Research Studies
This text on the design and analysis of clinical research studies, explains the basis of experimental design and statistics in a way that is sensitive to the clinical context in which nurses work. It uses data from actual studies to illustrate how to: design the study; use and select data; present research findings; and use a computer for statistical analysis. The scope of the study designs and associated statistical tecniques covered in the book allow both the beginning nurse researcher and the more seasoned professional nurse investigator to approach a research study with confidence and optimism. The authors show how qualitative data can be approached quantitatively, what the advantages of this are from the nursing viewpoint, and how quantitative methodology can help nurses to develop a common language with other disciplines invloved in patient care.
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Secondary Analysis of the TIMSS Data
Researchers who participate in IEA studies have a unique opportunity to work collaboratively with their counterparts from many different countries and disciplinary backgrounds over a period of several years on questions of shared academic interest. Once the data for a given study have been collected and the first round of international reports published, however, opportunities for that kind of collaboration tend to be much less frequent. A major strength of IEA studies compared to other large-scale, international studies is that they are classroom based, thereby making it possible for researchers and policy makers to investigate linkages between students’ achievement and a wide range of variables. Those variables could be related to instructional practices, to students’ and teachers’ background and attitudes, to school organizational patterns, or to opportunity to learn, to name a few. The research questions that TIMSS was designed to address make it clear that these kinds of relational, multi-variate analyses were among the major goals of the project. The international reports of the TIMSS–95 results that were published by the International Study Center at Boston College between 1996 and 1999 were intended to provide comprehensive coverage of the basic findings of the study. They were not intended to provide in-depth analyses of research and policy issues; instead, their main purpose was to make the basic findings of the study widely available in a timely manner. This they certainly did.
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Harmonic and Complex Analysis and its Applications
This volume highlights the main results of the research performed within the network “Harmonic and Complex Analysis and its Applications” (HCAA), which was a five-year (2007–2012) European Science Foundation Programme intended to explore and to strengthen the bridge between two scientific communities: analysts with broad backgrounds in complex and harmonic analysis and mathematical physics, and specialists in physics and applied sciences. It coordinated actions for advancing harmonic and complex analysis and for expanding its application to challenging scientific problems. Particular topics considered by this Programme included conformal and quasiconformal mappings, potential theory, Banach spaces of analytic functions and their applications to the problems of fluid mechanics, conformal field theory, Hamiltonian and Lagrangian mechanics, and signal processing. This book is a collection of surveys written as a result of activities of the Programme and will be interesting and useful for professionals and novices in analysis and mathematical physics, as well as for graduate students. Browsing the volume, the reader will undoubtedly notice that, as the scope of the Programme is rather broad, there are many interrelations between the various contributions, which can be regarded as different facets of a common theme.
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Meta-Analysis Using Structural Equation Modeling
Filling a gap in the contemporary literature, this practical guide shows how structural equation modeling (SEM) can be used to analyze results from multiple different studies on the same subject. Randall E. Schumacker presents programs using LISREL, Mplus, and R software, complete with worked-through examples, procedural steps, and sample annotated code. He provides an overview of meta-analysis and the basic SEM modeling steps before delving into meta-analysis in path models, confirmatory factor models, and structural equation models (MASEM). The book offers insights into why meta-analysis should be considered a multilevel method since data are collected over several years, making time a nested effect. It discusses key MASEM issues, such as publication bias, sample size differences between studies, and the heterogeneity of effect sizes across studies, and reviews PRISMA reporting guidelines aligned with APA style. Full code and output for the book's examples can be accessed at the companion website.
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Econometric Analysis of Count Data
The book provides an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed. Finally, applications are reviewed in various fields.
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Contrast Analysis
This book, first published in 1985, is for those seeking appropriate statistical approaches to research data that is devoted entirely to the topic of contrasts. Contrast analysis permits us to ask more focused questions of our data. In return for a small amount of simple computation, we get greater statistical power, and can make clearer substantive interpretations of the research results. Contrast analysis should be employed in the context of the analysis of variance whenever the numerator degrees of freedom are greater than one (which is probably most of the time). Unfortunately, it is employed relatively rarely by behavioural and social scientists. This book makes it possible for non-mathematical data analysts to avail themselves of contrasts, and thereby simply and efficiently to address the focused questions posed by their theories, hypotheses, and hunches. A wide range of researchers in the behavioural and social sciences, education and business will find this book an invaluable resource.
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Advances in Qualitative Research for the Health and Social Sciences
Written by an expert researcher and author, this text focuses on innovation in qualitative research, exploring new areas for qualitative research.Featuring an extended discussion of how to develop a coherent and robust qualitative research program, this book contains chapters on: •qualitative research synthesis approaches • secondary qualitative data analysis • the diverse landscape of qualitative internet research • advances in grounded theory • qualitatively driven mixed methods • different methods of qualitative content analysis.This is an essential text for researchers in the health and social sciences, especially those with an interest in qualitative research.
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Complex Analysis
The book comprises six chapters, each meticulously structured to build a comprehensive understanding of complex analysis. Chapter 1 covers the most fundamental concepts, providing the essential groundwork that permeates the entire text. Chapter 2 delves into normal families, the Riemann mapping theorem, and conformal mapping. Chapter 3 focuses on the zeros of analytic functions, while Chapter 4 explores the essential properties of harmonic and subharmonic functions. Chapter 5 introduces H^p spaces and the Fourier transform, highlighting their interconnections. The final chapter discusses uniform approximation by rational functions.Emphasizing foundational theories, modern methods, and key ideas in complex analysis, this book also presents some cutting-edge research problems and recent advancements. The topics are thoughtfully selected, and the exposition is clear and rigorous. This book is an excellent resource for graduate students and independent learners alike. It features many new and concise proofs of classical theorems, and offers numerous challenging exercises to deepen understanding.
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Statistical Data Analysis
This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the bookdescribes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions,and the problem of correcting distributions for the effects of measurement errors (unfolding).
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Data Analysis of Asymmetric Structures
Data Analysis of Asymmetric Structures provides a comprehensive presentation of a variety of models and theories for the analysis of asymmetry and its applications and provides a wealth of new approaches in every section. It meets both the practical and theoretical needs of research professionals across a wide range of disciplines and considers data analysis in fields such as psychology, sociology, social science, ecology, and marketing. In seven comprehensive chapters this guide details theories, methods, and models for the analysis of asymmetric structures in a variety of disciplines and presents future opportunities and challenges affecting research developments and business applications.
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Interaction Effects in Factorial Analysis of Variance
Although factorial analysis is widely used in the social sciences, there is some confusion as to how to use the techniqueÆs most powerful featureùthe evaluation of interaction effects. Written to remedy this situation, author James Jaccard clearly describes the issues underlying the effective analysis of interaction in factorial designs. The book begins by describing different ways of characterizing interactions in ANOVA, elucidating both moderator conceptualizations of interactions as well as that of residualized means. After discussing interaction effects using traditional hypothesis testing approaches, he then covers alternative analytic frameworks that focus on effect size methodology and interval estimation. Jaccard summarizes criticisms of classical null hypothesis testing and offers practical guidelines for pursuing magnitude estimation and interval estimation approaches. In addition, Jaccard shows applications of all three approaches to the analysis of interactions using a complete numerical example; discusses strategies for effectively exploring interactions in higher order designs and designs with more than two levels per factor; highlights the central role of single degree of freedom contrasts and provides numerous illustrations for formulating such contrasts; considers simplified approaches to statistical power analysis; describes approaches to consider when statistical assumptions are not met; explicates the case of unequal sample sizes; considers the impact of measurement error; and demonstrates computer applications. Readers who have wanted a book that fully discusses different conceptualizations of interactions as well as one that provides practical guidelines for analyzing complex interactions will find this volume the one that they have been seeking.
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Big Data Analysis on Global Community Formation and Isolation
In this book, the authors analyze big data on global interdependence caused by the flows of commodities, money, and people, using a network science approach to obtain differing views of globalization and to clarify the facts on isolation of communities.Globalization reduces international economic inequality, i.e., it allows emerging countries to catch up while it increases relative poverty in some advanced countries. How should this trade-off between international and domestic inequalities be resolved? At the same time, the reduction of biocultural diversity caused by globalization needs to be avoided. What kind of change is required in local communities to conserve biocultural diversity? On the issue of commodity flow, research results of the supply-chain network, isolation in industry, and resource flows and stocks are presented in this book. For monetary flow, ownership networks, value-added networks, and profit shifting were studied; and regarding the flow of people, linkage of ethnic groups, immigrant assimilation, and refugees were examined. Based on the resulting view of globalization and isolation, the development of the isolation index using machine learning is discussed. Finally, recommendations for evidence-based policymaking in the United Nations are considered.
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Computational Analysis of Communication
Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and othersIntegrates theory, methods, and tools to provide unified approach to the subjectIncludes sample code in Python and links to actual research questions and cases from social science and communication studiesDiscusses ethical and normative issues relevant to privacy, data ownership, and reproducible social scienceDeveloped in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
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Applied Regression Analysis
This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data.Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques.This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.
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Statistical Data Analysis and Entropy
This book reconsiders statistical methods from the point of view of entropy, and introduces entropy-based approaches for data analysis. Further, it interprets basic statistical methods, such as the chi-square statistic, t-statistic, F-statistic and the maximum likelihood estimation in the context of entropy. In terms of categorical data analysis, the book discusses the entropy correlation coefficient (ECC) and the entropy coefficient of determination (ECD) for measuring association and/or predictive powers in association models, and generalized linear models (GLMs). Through association and GLM frameworks, it also describes ECC and ECD in correlation and regression analyses for continuous random variables. In multivariate statistical analysis, canonical correlation analysis, T2-statistic, and discriminant analysis are discussed in terms of entropy. Moreover, the book explores the efficiency of test procedures in statistical tests of hypotheses using entropy. Lastly, it presents an entropy-based path analysis for structural GLMs, which is applied in factor analysis and latent structure models. Entropy is an important concept for dealing with the uncertainty of systems of random variables and can be applied in statistical methodologies. This book motivates readers, especially young researchers, to address the challenge of new approaches to statistical data analysis and behavior-metric studies.
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Data Analysis in Sport
Making sense of sports performance data can be a challenging task but is nevertheless an essential part of performance analysis investigations. Focusing on techniques used in the analysis of sport performance, this book introduces the fundamental principles of data analysis, explores the most important tools used in data analysis, and offers guidance on the presentation of results.The book covers key topics such as:The purpose of data analysis, from statistical analysis to algorithmic processingCommercial packages for performance and data analysis, including Focus, Sportscode, Dartfish, Prozone, Excel, SPSS and MatlabEffective use of statistical procedures in sport performance analysisAnalysing data from manual notation systems, player tracking systems and computerized match analysis systemsCreating visually appealing ‘dashboard’ interfaces for presenting dataAssessing reliability.The book includes worked examples from real sport, offering clear guidance to the reader and bringing the subject to life. This book is invaluable reading for any student, researcher or analyst working in sport performance or undertaking a sport-related research project or methods course
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Data Analysis Workshop
Learn how to analyze data using Python models with the help of real-world use cases and guidance from industry expertsKey FeaturesGet to grips with data analysis by studying use cases from different fieldsDevelop your critical thinking skills by following tried-and-true data analysisLearn how to use conclusions from data analyses to make better business decisionsBook DescriptionBusinesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you'll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.What you will learnGet to grips with the fundamental concepts and conventions of data analysisUnderstand how different algorithms help you to analyze the data effectivelyDetermine the variation between groups of data using hypothesis testingVisualize your data correctly using appropriate plotting pointsUse correlation techniques to uncover the relationship between variablesFind hidden patterns in data using advanced techniques and strategiesWho this book is forThe Data Analysis Workshop is for programmers who already know how to code in Python and want to use it to perform data analysis. If you are looking to gain practical experience in data science with Python, this book is for you.
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Dynamic Data Analysis
This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap.
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Analysis of Categorical Data
This volume presents a unified and up-to-date account of the theory and methods of applying one of the most useful and widely applicable techniques of data analysis, 'dual scaling.'
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Statistical Analysis in the Behavioral Sciences
Statistical Analysis in the Behavioral Sciences presents a basic understanding of statistical analysis, by incorporating real-world examples and exposing readers to current technology.Designed for students in all the disciplines of the behavioral sciences, Statistical Analysis in the Behavioral Sciences gives the reader a far better understanding of what statistics is, what the statistical procedures really mean, and just as importantly, what they do not mean.Statistical Analysis in the Behavioral Sciences:Is organized into four major sections: Some Basics, The Bridge to Inferential Statistics, The Bridge to Inferential Statistics, and Inferential Statistics.Features statistical procedures that can be performed in popular spreadsheet software or by software dedicated to statistical applications.Provides instructors with a data set consisting of an excerpt from the 2000 to 2012 General Social Survey and an ongoing project by the National Opinion Research Center of the University of Chicago.Incorporates suggested computer exercises in most chapters.Provides readers with directions and tips for those with access to Statistical Package for the Social Sciences (SPSS).
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Data Analysis in Management with SPSS Software
This book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. It strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. The book provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of research problems. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioural sciences by using SPSS.
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Topological Methods in Data Analysis and Visualization
Topology-based methods are of increasing importance in the analysis and visualization of datasets from a wide variety of scientific domains such as biology, physics, engineering, and medicine. Current challenges of topology-based techniques include the management of time-dependent data, the representation of large and complex datasets, the characterization of noise and uncertainty, the effective integration of numerical methods with robust combinatorial algorithms, etc. .The editors have brought together the most prominent and best recognized researchers in the field of topology-based data analysis and visualization for a joint discussion and scientific exchange of the latest results in the field. This book contains the best 20 peer-reviewed papers resulting from the discussions and presentations at the third workshop on "Topological Methods in Data Analysis and Visualization", held 2009 in Snowbird, Utah, US. The 2009 "TopoInVis" workshop follows the two successful workshops in 2005 (Slovakia) and 2007 (Germany).
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Applied Data Analysis for Urban Planning and Management
This book showcases the different ways in which contemporary forms of data analysis are being used in urban planning and management. It highlights the emerging possibilities that city-regional governance, technology and data have for better planning and urban management - and discusses how you can apply them to your research.Including perspectives from across the globe, it’s packed with examples of good practice and helps to demystify the process of using big and open data.Learn about different kinds of emergent data sources and how they are processed, visualised and presented.Understand how spatial analysis and GIS are used in city planning.See examples of how contemporary data analytics methods are being applied in a variety of contexts, such as ‘smart’ city management and megacities.Aimed at upper undergraduate and postgraduate students studying spatial analysis and planning, this timely text is the perfect companion to enable you to apply data analytics approaches in your research.
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Statistical Analysis Methods for Chemists
Many forms of chemical experimentation generate data needing analysis and interpretation in respect of the goals of the experiment and also the chemical factors which may influence the outcome. Statistical data analysis techniques provide the tools which enable a chemist to assess the information obtained from experiments. Statistical Analysis Methods for Chemists: A Software-based Approach aims to give a broad introduction to practical data analysis, and provides comprehensive coverage of basic statistical principles and reasoning. With practical examples, and integration of software output as the basis of data analysis, this useful book gives unique coverage of the statistical skills and techniques required in modern chemical experimentation. It will prove invaluable to students and researchers alike. Software update information is available from W Gardiner at w.gardiner@gcal.ac.uk or fax +44 (0)141 331 3608. Please accompany requests for information with details of the software version to be used.
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Understanding Clinical Data Analysis
This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.
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Hands-On Data Analysis with Pandas
Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discoveryKey FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook DescriptionData analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value.Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data.By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is forThis book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
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Analysis
Hauptthema dieses zweiten Bandes ist die Differential- und Integralrechnung für Funktionen von mehreren Veränderlichen. Dabei wird auch das Lebesguesche Integral im Rn behandelt. Dem erfolgreichen Konzept von "Analysis 1" folgend, wird viel Wert auf historische Zusammenhänge, Ausblicke und die Entwicklung der Analysis gelegt. Zu den Besonderheiten, die über den kanonischen Stoff des zweiten Semesters hinausgehen, gehören das Morsesche und das Sardsche Lemma, die C?-Approximation von Funktionen (Mollifiers) und die Theorie der absolutstetigen Funktionen. Zahlreiche Beispiele, Übungsaufgaben und Anwendungen, z.B. aus der Physik und Astronomie, runden dieses Lehrbuch ab. Der Abschnitt "Lösungen und Lösungshinweise" wurde für die Neuauflage wesentlich erweitert, so daß die überwiegende Zahl der Aufgaben im Buch nun besprochen oder vollständig gelöst wird.
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Big Data Analysis with Python
Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.Key FeaturesGet a hands-on, fast-paced introduction to the Python data science stackExplore ways to create useful metrics and statistics from large datasetsCreate detailed analysis reports with real-world dataBook DescriptionProcessing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.What you will learnUse Python to read and transform data into different formatsGenerate basic statistics and metrics using data on diskWork with computing tasks distributed over a clusterConvert data from various sources into storage or querying formatsPrepare data for statistical analysis, visualization, and machine learningPresent data in the form of effective visualsWho this book is forBig Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.
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Clinical Data Analysis on a Pocket Calculator
Inmedical and health care the scientific method is little used, and statisticalsoftware programs are experienced as black box programs producing lots ofp-values, but little answers to scientific questions. The pocket calculatoranalyses appears to be, particularly, appreciated, because they enable medicaland health professionals and students for the first time to understand thescientific methods of statistical reasoning and hypothesis testing. So much so,that it can start something like a new dimension in their professional world. Inaddition, anumber of statistical methods like power calculations and required sample sizecalculations can be performed more easily on a pocket calculator, than using asoftware program. Also, there are some specific advantages of thepocket calculator method. You better understand what you are doing. The pocketcalculator works faster, because far less steps have to be taken, averages canbe used. The current nonmathematical book is complementary to thenonmathematical "SPSS for Starters and 2nd Levelers" (Springer HeidelbergGermany 2015, from the same authors), and can very well be used as its dailycompanion.
R 2,778.22
Secondary Analysis of the TIMSS Data
Researchers who participate in IEA studies have a unique opportunity to work collaboratively with their counterparts from many different countries and disciplinary backgrounds over a period of several years on questions of shared academic interest. Once the data for a given study have been collected and the first round of international reports published, however, opportunities for that kind of collaboration tend to be much less frequent. A major strength of IEA studies compared to other large-scale, international studies is that they are classroom based, thereby making it possible for researchers and policy makers to investigate linkages between students’ achievement and a wide range of variables. Those variables could be related to instructional practices, to students’ and teachers’ background and attitudes, to school organizational patterns, or to opportunity to learn, to name a few. The research questions that TIMSS was designed to address make it clear that these kinds of relational, multi-variate analyses were among the major goals of the project. The international reports of the TIMSS–95 results that were published by the International Study Center at Boston College between 1996 and 1999 were intended to provide comprehensive coverage of the basic findings of the study. They were not intended to provide in-depth analyses of research and policy issues; instead, their main purpose was to make the basic findings of the study widely available in a timely manner. This they certainly did.
R 3,062.02
Deep Sequencing Data Analysis
This second edition provides new and updated chapters from expert researchers in the field detailing methods used to study the multi-facet deep sequencing data field. Chapters guide readers through techniques for processing RNA-seq data, microbiome analysis, deep learning methodologies, and various approaches for the identification of sequence variants. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Deep Sequencing Data Analysis: Methods and Protocols, Second Edition aims to ensure successful results in the further study of this vital field.
R 4,916.71
R 911.00
R 2,272.91
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