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Excel Data Analysis For Dummies
Turn jumbles of numbers into graphics, insights, and answers with Excel With Microsoft Excel, you can, well, excel at data analysis. And Excel Data Analysis For Dummies can help, with clear and easy explanations of the mountain of features for creating, visualizing, and analyzing data. PivotTables, charts, what-if analysis, statistical functions—it's all in here, with examples and ideas for Excel users of all skill levels. This latest edition covers the most recent updates to Excel and Microsoft 365. You'll beef up your data skills and learn powerful techniques for turning numbers into knowledge. For students, researchers, and business professionals, Excel is the spreadsheet and data application of choice—and Dummies is the best choice for learning how to make those numbers sing. Learn how to use Excel's built-in data analysis features and write your own functions to explore patterns in your dataCreate striking charts and visualizations, and discover multiple ways to tell the stories hidden in the numbersClean up large datasets and identify statistical operations that will answer your questionsPerform financial calculations, database operations, and more—without leaving Excel Excel Data Analysis For Dummies is the go-to resource for Excel users who are looking for better ways to crunch the numbers.
R 821.00
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Modern Methods in Complex Analysis (AM-137), Volume 137
The fifteen articles composing this volume focus on recent developments in complex analysis. Written by well-known researchers in complex analysis and related fields, they cover a wide spectrum of research using the methods of partial differential and algebraic geometry. The topics include invariants of manifolds, the complex Neumann problem, complex dynamics, Ricci flows, the Abel-Radon transforms, the action of the Ricci curvature operator, locally symmetric manifolds, the maximum principle, very ampleness criterion, integrability of elliptic systems, and contact geometry. Among the contributions are survey articles, which are especially suitable for readers looking for a comprehen-sive, well-presented introduction to the most recent important developments in the field. The contributors are R. Bott, M. Christ, J. P. D'Angelo, P. Eyssidieux, C. Fefferman, J. E. Fornaess, H. Grauert, R. S. Hamilton, G. M. Henkin, N. Mok, A. M. Nadel, L. Nirenberg, N. Sibony, Y.-T. Siu, F. Treves, and S. M. Webster.
R 4,395.05
Data Analysis in Astronomy IV
In this book are reported the main results presented at the "Fourth International Workshop on Data Analysis in Astronomy", held at the Ettore Majorana Center for Scientific Culture, Erice, Sicily, Italy, on April 12-19, 1991. The Workshop was preceded by three workshops on the same subject held in Erice in 1984, 1986 and 1988. The frrst workshop (Erice 1984) was dominated by presentations of "Systems for Data Analysis"; the main systems proposed were MIDAS, AlPS, RIAIP, and SAIA. Methodologies and image analysis topics were also presented with the emphasis on cluster analysis, multivariate analysis, bootstrap methods, time analysis, periodicity, 2D photometry, spectrometry, and data compression. A general presentation on "Parallel Processing" was made which encompassed new architectures, data structures and languages. The second workshop (Erice 1986) reviewed the "Data Handling Systems" planned for large major satellites and ground experiments (VLA, HST, ROSAT, COMPASS-COMPTEL). Data analysis methods applied to physical interpretation were mainly considered (cluster photometry, astronomical optical data compression, cluster analysis for pulsar light curves, coded aperture imaging). New parallel and vectorial machines were presented (cellular machines, PAPIA-machine, MPP-machine, vector computers in astronomy). Contributions in the field of artificial intelligence and planned applications to astronomy were also considered (expert systems, artificial intelligence in computer vision).
R 1,597.45
Data-Variant Kernel Analysis
Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA)Develops group kernel analysis with the distributed databases to compare speed and memory usagesExplores the possibility of real-time processes by synthesizing offline and online databasesApplies the assembled databases to compare cloud computing environmentsExamines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.
R 4,753.00
Methods for Multilevel Analysis and Visualisation of Geographical Networks
This leading-edge study focuses on the latest techniques in analysing and representing the complex, multi-layered data now available to geographers studying urban zones and their populations. The volume tracks the successful results of the SPANGEO Project, which was set up in 2005 to standardize, and share, the syncretic, multinational mapping techniques already developed by geographers and computer scientists. SPANGEO sought new and responsive ways of visualising urban geographical and social data that reflected the fine-grained detail of the inputs. It allowed for visual representation of the large and complex networks and flows which are such an integral feature of the dynamism of urban geography. SPANGEO developed through the ‘visual analytics loop’ in which geographers collaborated with computer scientists by feeding data into the design of visualisations that in turn spawned the urge to incorporate more varied data into the visualisation. This volume covers all the relevant aspects, from conceptual principles to the tools of network analysis and the actual results flowing from their deployment. Detailed case studies set out in this volume include spatial multi-level analyses of flows in airports and sea ports, as well as the fascinating scientific networks in European cities. The volume shows how the primary concern of geography—the interaction of society with physical space—has been revivified by the complexities of new cartographical and statistical methodologies, which allow for highly detailed mapping and far more powerful computer analysis of spatial relationships.
R 3,026.59
Grey Data Analysis
This book inclusively and systematically presents the fundamental methods, models and techniques of practical application of grey data analysis, bringing together the authors’ many years of theoretical exploration, real-life application, and teaching. It also reflects the majority of recent theoretical and applied advances in the theory achieved by scholars from across the world, providing readers a vivid overall picture of this new theory and its pioneering research activities. The book includes 12 chapters, covering the introduction to grey systems, a novel framework of grey system theory, grey numbers and their operations, sequence operators and grey data mining, grey incidence analysis models, grey clustering evaluation models, series of GM models, combined grey models, techniques for grey systems forecasting, grey models for decision-making, techniques for grey control, etc. It also includes a software package that allows practitioners to conveniently and practically employ the theory and methods presented in this book. All methods and models presented here were chosen for their practical applicability and have been widely employed in various research works.I still remember 1983, when I first participated in a course on Grey System Theory. The mimeographed teaching materials had a blue cover and were presented as a book. It was like finding a treasure: This fascinating book really inspired me as a young intellectual going through a period of confusion and lack of academic direction. It shone with pearls of wisdom and offered a beacon in the mist for a man trying to find his way in academic research. This book became the guiding light in my life journey, inspiring me to forge an indissoluble bond with Grey System Theory.——Sifeng Liu
R 4,233.17
Cluster analysis
During the last years the number of applications of cluster analysis in the social sciences has increased very rapidly. One of the reasons for this is the growing awareness that the assumption of homogeneity implicit in the application of such techniques as factor analysis and scaling is often violated by social science data; another is the increased interest in typolo gies and the construction of types. Dr. Bijnen has done an extremely useful job by putting together and evaluating attempts to arrive at better and more elegant techniques of cluster analysis from such diverse fields as the social sciences, biology and medicine. His presentation is very clear and concise, reflecting his intention not to write a 'cookery-book' but a text for scholars who need a reliable guide to pilot them through an extensive and widely scattered literature. Ph. C. Stouthard v Preface This book contains a survey of a number of techniques of clustering analysis. The merits and demerits of the procedures described are also discussed so that the research worker can make an informed choice be tween them. These techniques have been published in a very great number of journals which are not all easily accessible to the sociologist. This difficulty is com pounded because developments in the different disciplines have occurred almost entirely independently from each other; reference is made only sporadically in a piece of literature to the literature of other disciplines.
R 1,528.59
Introduction to the Statistical Analysis of Categorical Data
to the Statistical Analysis of Categorical Data With 16 Figures and 121 Tables , Springer Prof. Erling B. Andersen University of Copenhagen Department of Statistics 6 Studiestrrede DK-14SS Copenhagen Denmark ISBN 978-3-540-62399-1 CataJoging-in-Publication Data applied ror Oie Oeutsche Bibliothek - CIP-Einheitsaufnahme Andersen, Erling B. : Introduction to the statistical analysis of categorical data analysis: with 121 tables I Erling B. Andersen. -Berlin; Heidelberg; New York; Bucelona; Buda- pest; Hong Kong; London; Milan; Paris; Santa Clara; Singapore; Tokyo: Springer, 1997 ISBN 978-3-540~2399-1 ISBN 978-3~2-59123-5 (eBook) DOI10. 1007/978-3~2-59123-5 This work is subject to copyright. AII rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any o!her way, and storage in data banks. Ouplication of this publication or parts thereof IS permitted only under the provisions of !he German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag.Violations are liable for prosecution under !he German Copyright Law. O Springer-Verlag Berlin Heidelberg 1997 Originally published by Springer-Verlag Berlin Heidelberg New York in 1997 The use of general descriptive names, registered names, trademarks, etc. in this publi- cation does not imply, even in !he absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and !herefore free for general use.
R 2,015.00
Sequence Data Analysis Guidebook
Computers have revolutionized the analysis of sequencing data. It is unlikely that any sequencing projects have been performed in the last few years without the aid of computers. Recently their role has taken a further major step forward. Computers have become smaller and more powerful and the software has become simpler to use as it has grown in sophistication. This book reflects that change since the majority of packages described here are designed to be used on desktop computers. Computer software is now available that can run gels, collect data, and assess its accuracy. It can assemble, align, or compare multiple fragments, perform restriction analyses, identify coding regions and specific motifs, and even design the primers needed to extend the sequencing. Much of this soft ware may now be used on relatively inexpensive computers. It is now possible to progress from isolate d DNA to database submission without writing a single base down. To reflect this progression, the chapters in our Sequence Data Analysis Guidebook are arranged, not by software package, but by fimction. The early chapters deal with examining the data produced by modem automated sequenc ers, assessing its quality, and removing extraneous data. The following chap ters describe the process of aligning multiple sequences in order to assemble overlapping fragments into sequence contigs to compare similar sequences from different sources. Subsequent chapters describe procedures for compar ing the newly derived sequence to the massive amounts of information in the sequence databases.
R 4,660.02
R 597.31
Machine Learning for Emotion Analysis
Kickstart your emotion analysis journey with this hands-on, step-by-step guide to data science successKey Features* Discover the ins and outs of the end-to-end emotional analysis workflow* Explore the use of various ML models to derive meaningful insights from all sorts of data* Hone your craft by building and tweaking complex emotion analysis models in practical projectsBook DescriptionThe AI winter has long thawed, but many organizations are still failing to harness the power of machine learning (ML). If you want to tap that potential and add value to your own business with cutting-edge emotion analysis, you've found what you need in this trusty guide.In Machine Learning for Emotion Analysis, you'll take your foundational data science skills and grow them in the exciting realm of emotion analysis. With its practical approach, you'll be equipped with everything you need to give your company a clear insight into what your customers are thinking.This no-nonsense guide jumps right into the practicalities of emotion analysis, teaching you how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you're set up for success, we get hands-on with complex ML techniques. This is where you go from the intermediate to the advanced, covering deep neural networks, support vector machines, conditional probabilities, and more, as you experience the full breadth of possibilities with emotion analysis. The book finally rounds out with a couple of in-depth use cases – a sort of sandbox for you to experiment with your newly acquired skill set.By the end of this book, you'll be ready to present yourself as a valuable asset to any organization that takes data science seriously.What you will learn* Distinguish between sentiment analysis and emotion analysis* Master the art of data preprocessing and ensure high-quality input* Expand your use of data sources through data transformation* Build models that employ cutting-edge deep learning techniques* Discover how best to tune your models' hyperparameters* Explore the use of KNN, SVM, and DNNs for advanced use cases* Build APIs and integrate your models into existing solutions* Practice your new skills by working on real-world scenariosWho This Book Is ForThis book is for data scientists and Python developers who want to gain insights into what people are saying about their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming knowledge is necessary to grasp the concepts covered.
R 1,693.00
Design and Analysis of Ecological Experiments
The goal of this book is to make some underutilized but potentially very useful methods in experimental design and analysis available to ecologists, and to encourage better use of standard statistical techniques. Ecology has become more and more an experimental science in both basic and applied work,but experiments in the field and in the laboratory often present formidable statistical difficulties. Organized around providing solutions to ecological problems, this book offers ways to improve the statistical aspects of conducting manipulative ecological experiments, from setting them up to interpreting and reporting the results. An abundance of tools, including advanced approaches, are made available to ecologists in step-by-step examples, with computer code provided for common statistical packages. This is an essential how-to guide for the working ecologist and for graduate students preparing for research and teaching careers in the field of ecology.
R 3,735.61
Intelligent Data Analysis for Real-Life Applications
Intelligent Data Analysis for Real-Life Applications: Theory and Practice investigates the application of Intelligent Data Analysis (IDA) to these data sets through the design and development of algorithms and techniques to extract knowledge from databases. This pivotal reference explores practical applications of IDA, and it is essential for academic and research libraries as well as students, researchers, and educators in data analysis, application development, and database management.
R 7,589.40
Introduction to Environmental Data Analysis and Modeling
This book introduces numerical methods for processing datasets which may be of any form, illustrating adequately computational resolution of environmental alongside the use of open source libraries. This book solves the challenges of misrepresentation of datasets that are relevant directly or indirectly to the research. It illustrates new ways of screening datasets or images for maximum utilization. The adoption of various numerical methods in dataset treatment would certainly create a new scientific approach. The book enlightens researchers on how to analyse measurements to ensure 100% utilization. It introduces new ways of data treatment that are based on a sound mathematical and computational approach.
R 5,161.46
Cohort Analysis
Cohort Analysis, Second Edition covers the basics of the cohort approach to studying aging, social, and cultural change. This volume also critiques several commonly used (but flawed) methods of cohort analysis, illustrates appropriate methods, and describes the major sources of suitable data for cohort studies.
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Higher Education Policy Analysis Using Quantitative Techniques
This textbook introduces graduate students in education and policy research to data and statistical methods in state-level higher education policy analysis. It also serves as a methodological guide to students, practitioners, and researchers who want a clear approach to conducting higher education policy analysis that involves the use of institutional- and state-level secondary data and quantitative methods ranging from descriptive to advanced statistical techniques. This book is unique in that it introduces readers to various types of data sources and quantitative methods utilized in policy research and in that it demonstrates how results of statistical analyses should be presented to higher education policy makers. It helps to bridge the gap between researchers, policy makers, and practitioners both within education policy and between other fields.Coverage includes identifying pertinent data sources, the creation and management of customized data sets, teaching beginning and advanced statistical methods and analyses, and the presentation of analyses for different audiences (including higher education policy makers).
R 3,055.93
Analog and Digital Signal Analysis
This book provides comprehensive, graduate-level treatment of analog and digital signal analysis suitable for course use and self-guided learning. This expert text guides the reader from the basics of signal theory through a range of application tools for use in acoustic analysis, geophysics, and data compression. Each concept is introduced and explained step by step, and the necessary mathematical formulae are integrated in an accessible and intuitive way. The first part of the book explores how analog systems and signals form the basics of signal analysis. This section covers Fourier series and integral transforms of analog signals, Laplace and Hilbert transforms, the main analog filter classes, and signal modulations. Part II covers digital signals, demonstrating their key advantages. It presents z and Fourier transforms, digital filtering, inverse filters, deconvolution, and parametric modeling for deterministic signals. Wavelet decomposition and reconstruction of non-stationary signals are also discussed. The third part of the book is devoted to random signals, including spectral estimation, parametric modeling, and Tikhonov regularization. It covers statistics of one and two random variables and the principles and methods of spectral analysis. Estimation of signal properties is discussed in the context of ergodicity conditions and parameter estimations, including the use of Wiener and Kalman filters. Two appendices cover the basics of integration in the complex plane and linear algebra. A third appendix presents a basic Matlab toolkit for computer signal analysis. This expert text provides both a solid theoretical understanding and tools for real-world applications.
R 2,564.00
Ridges in Image and Data Analysis
The concept of ridges has appeared numerous times in the image processing liter ature. Sometimes the term is used in an intuitive sense. Other times a concrete definition is provided. In almost all cases the concept is used for very specific ap plications. When analyzing images or data sets, it is very natural for a scientist to measure critical behavior by considering maxima or minima of the data. These critical points are relatively easy to compute. Numerical packages always provide support for root finding or optimization, whether it be through bisection, Newton's method, conjugate gradient method, or other standard methods. It has not been natural for scientists to consider critical behavior in a higher-order sense. The con cept of ridge as a manifold of critical points is a natural extension of the concept of local maximum as an isolated critical point. However, almost no attention has been given to formalizing the concept. There is a need for a formal development. There is a need for understanding the computation issues that arise in the imple mentations. The purpose of this book is to address both needs by providing a formal mathematical foundation and a computational framework for ridges. The intended audience for this book includes anyone interested in exploring the use fulness of ridges in data analysis.
R 3,026.76
Survival Analysis in Medicine and Genetics
Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples.Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.
R 2,564.00
Fuzzy Surfaces in GIS and Geographical Analysis
Surfaces are a central to geographical analysis. Their generation and manipulation are a key component of geographical information systems (GISs). However, geographical surface data is often not precise. When surfaces are used to model geographical entities, the data inherently contains uncertainty in terms of both position and attribute. Fuzzy Surface in GIS and Geographical Analysis sets out a process to identify the uncertainty in geographic entities. It describes how to successfully obtain, model, analyze, and display data, as well as interpret results within the context of GIS.Focusing on uncertainty that arises from transitional boundaries, the book limits its study to three types of uncertainties: intervals, fuzzy sets, and possibility distributions. The book explains that uncertainty in geographical data typically stems from these three and it is only natural to incorporate them into the analysis and display of surface data. The book defines the mathematics associated with each method for analysis, then develops related algorithms, and moves on to illustrate various applications. Fuzzy Surface in GIS and Geographical Analysis clearly defines how to develop a routine that will adequately account for the uncertainties inherent in surface data.
R 3,967.53
Complex Analysis
This is a very successful textbook for undergraduate students of pure mathematics. Students often find the subject of complex analysis very difficult. Here the authors, who are experienced and well-known expositors, avoid many of such difficulties by using two principles: (1) generalising concepts familiar from real analysis; (2) adopting an approach which exhibits and makes use of the rich geometrical structure of the subject. An opening chapter provides a brief history of complex analysis which sets it in context and provides motivation.
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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.
R 4,722.50
Analysis of SAR Data of the Polar Oceans
This book is a collection of the most recent and significant research on algorithms for the analysis of polar sea-ice SAR data. All algorithms are implemented and tested. One chapter is from the Alaskan SAR Facility, the major NASA archive of polar SAR data and a source of many SAR analysis algorithms, including high-level results of such analyses. One chapter has been written jointly by the US and Canadian Ice Centers, which provide e.g., operational sea-ice products to the shipping and oil-drilling industries and to polar explorations. This book will be useful to all researchers in the polar sciences community.
R 3,038.63
Anonymization of Electronic Medical Records to Support Clinical Analysis
Anonymization of Electronic Medical Records to Support Clinical Analysis closely examines the privacy threats that may arise from medical data sharing, and surveys the state-of-the-art methods developed to safeguard data against these threats. To motivate the need for computational methods, the book first explores the main challenges facing the privacy-protection of medical data using the existing policies, practices and regulations. Then, it takes an in-depth look at the popular computational privacy-preserving methods that have been developed for demographic, clinical and genomic data sharing, and closely analyzes the privacy principles behind these methods, as well as the optimization and algorithmic strategies that they employ. Finally, through a series of in-depth case studies that highlight data from the US Census as well as the Vanderbilt University Medical Center, the book outlines a new, innovative class of privacy-preserving methods designed to ensure the integrity of transferred medical data for subsequent analysis, such as discovering or validating associations between clinical and genomic information. Anonymization of Electronic Medical Records to Support Clinical Analysis is intended for professionals as a reference guide for safeguarding the privacy and data integrity of sensitive medical records. Academics and other research scientists will also find the book invaluable.
R 1,511.95
Complex Analysis
An introduction to complex analysis for students with some knowledge of complex numbers from high school. It contains sixteen chapters, the first eleven of which are aimed at an upper division undergraduate audience. The remaining five chapters are designed to complete the coverage of all background necessary for passing PhD qualifying exams in complex analysis. Topics studied include Julia sets and the Mandelbrot set, Dirichlet series and the prime number theorem, and the uniformization theorem for Riemann surfaces, with emphasis placed on the three geometries: spherical, euclidean, and hyperbolic. Throughout, exercises range from the very simple to the challenging. The book is based on lectures given by the author at several universities, including UCLA, Brown University, La Plata, Buenos Aires, and the Universidad Autonomo de Valencia, Spain.
R 3,922.44
Ridges in Image and Data Analysis
The concept of ridges has appeared numerous times in the image processing liter ature. Sometimes the term is used in an intuitive sense. Other times a concrete definition is provided. In almost all cases the concept is used for very specific ap plications. When analyzing images or data sets, it is very natural for a scientist to measure critical behavior by considering maxima or minima of the data. These critical points are relatively easy to compute. Numerical packages always provide support for root finding or optimization, whether it be through bisection, Newton's method, conjugate gradient method, or other standard methods. It has not been natural for scientists to consider critical behavior in a higher-order sense. The con cept of ridge as a manifold of critical points is a natural extension of the concept of local maximum as an isolated critical point. However, almost no attention has been given to formalizing the concept. There is a need for a formal development. There is a need for understanding the computation issues that arise in the imple mentations. The purpose of this book is to address both needs by providing a formal mathematical foundation and a computational framework for ridges. The intended audience for this book includes anyone interested in exploring the use fulness of ridges in data analysis.
R 3,025.86
College Students in the United States
In this book, the authors bring together in one place essential information about college students in the US in the 21st century. Synthesizing existing research and theory, they present an introduction to studying student characteristics, college choice and enrollment patterns, institutional types and environments, student learning, persistence, and outcomes of college.Substantially revised and updated, this new edition addresses contemporary and anticipated student demographics and enrollment patterns, a wide variety of campus environments (such as residential, commuter, online, hybrid), and a range of outcomes including learning, development, and achievement.The book is organized around Alexander Astin’s Inputs-Environment-Outputs (I-E-O) framework. Student demographics, college preparation, and enrollment patterns are the ""inputs."" Transition to college and campus environments are the substance of the ""environment."" The ""outputs"" are student development, learning, and retention/persistence/completion.The authors build on this foundation by providing relevant contemporary information and analysis of students, environments, and outcomes. They also provide strategies for readers to project forward in anticipation of higher education trends in a world where understanding “college students in the United States” is an ongoing project. As a starting point for those who seek a foundational understanding of the diversity of students and institutions in the US, the book includes discussion points, learning activities, and further resources for exploring the topics in each chapter.
R 6,899.36
Statistical Design, Monitoring, and Analysis of Clinical Trials
Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters.Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 20 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, phase 2/3 seamless design and trials with predictive biomarkers, exploit multiple testing procedures, and explain the concept of estimand, intercurrent events, and different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics, epidemiology, medicine, pharmacy, and public health.
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R 766.14
Fuzzy Transforms for Image Processing and Data Analysis
This book analyzes techniques that use the direct and inverse fuzzy transform for image processing and data analysis. The book is divided into two parts, the first of which describes methods and techniques that use the bi-dimensional fuzzy transform method in image analysis. In turn, the second describes approaches that use the multidimensional fuzzy transform method in data analysis. An F-transform in one variable is defined as an operator which transforms a continuous function f on the real interval [a,b] in an n-dimensional vector by using n-assigned fuzzy sets A1, … , An which constitute a fuzzy partition of [a,b]. Then, an inverse F-transform is defined in order to convert the n-dimensional vector output in a continuous function that equals f up to an arbitrary quantity ε. We may limit this concept to the finite case by defining the discrete F-transform of a function f in one variable, even if it is not known a priori. A simple extension of this concept to functions in two variables allows it to be used for the coding/decoding and processing of images. Moreover, an extended version with multidimensional functions can be used to address a host of topics in data analysis, including the analysis of large and very large datasets. Over the past decade, many researchers have proposed applications of fuzzy transform techniques for various image processing topics, such as image coding/decoding, image reduction, image segmentation, image watermarking and image fusion; and for such data analysis problems as regression analysis, classification, association rule extraction, time series analysis, forecasting, and spatial data analysis. The robustness, ease of use, and low computational complexity of fuzzy transforms make them a powerful fuzzy approximation tool suitable for many computer science applications. This book presents methods and techniques based on the use of fuzzy transforms in various applications of image processing and data analysis, including image segmentation, image tamper detection, forecasting, and classification, highlighting the benefits they offer compared with traditional methods. Emphasis is placed on applications of fuzzy transforms to innovative problems, such as massive data mining, and image and video security in social networks based on the application of advanced fragile watermarking systems. This book is aimed at researchers, students, computer scientists and IT developers to acquire the knowledge and skills necessary to apply and implement fuzzy transforms-based techniques in image and data analysis applications.
R 7,033.20
Sequence Data Analysis Guidebook
Computers have revolutionized the analysis of sequencing data. It is unlikely that any sequencing projects have been performed in the last few years without the aid of computers. Recently their role has taken a further major step forward. Computers have become smaller and more powerful and the software has become simpler to use as it has grown in sophistication. This book reflects that change since the majority of packages described here are designed to be used on desktop computers. Computer software is now available that can run gels, collect data, and assess its accuracy. It can assemble, align, or compare multiple fragments, perform restriction analyses, identify coding regions and specific motifs, and even design the primers needed to extend the sequencing. Much of this soft ware may now be used on relatively inexpensive computers. It is now possible to progress from isolate d DNA to database submission without writing a single base down. To reflect this progression, the chapters in our Sequence Data Analysis Guidebook are arranged, not by software package, but by fimction. The early chapters deal with examining the data produced by modem automated sequenc ers, assessing its quality, and removing extraneous data. The following chap ters describe the process of aligning multiple sequences in order to assemble overlapping fragments into sequence contigs to compare similar sequences from different sources. Subsequent chapters describe procedures for compar ing the newly derived sequence to the massive amounts of information in the sequence databases.
R 4,659.87
R 6,701.00
Python for Geospatial Data Analysis
In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions.Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python.This book helps you:Understand the importance of applying spatial relationships in data scienceSelect and apply data layering of both raster and vector graphicsApply location data to leverage spatial analyticsDesign informative and accurate mapsAutomate geographic data with Python scriptsExplore Python packages for additional functionalityWork with atypical data types such as polygons, shape files, and projectionsUnderstand the graphical syntax of spatial data science to stimulate curiosity
R 1,824.00
Desertification and Risk Analysis Using High and Medium Resolution Satellite Data
This work becomes with methodological rigor a part of the innovative proposals for the characterization of the areas at risk of desertification. The complexity of the phenomenon of desertification, which involves extended surfaces in all continents, is one of the most alarming processes of the environmental degradation of our Planet and threatens the health and the living conditions of over a billion of persons. The food crisis, in continuous increase, ask for the world of research to urgently supply reassuring solutions concerning the acquisition of indicators, which are easy to monitor and concur to control the phenomenon in order to fight its acceleration. The constant but different combination that determines the diffusion of deserti- cation in the territories, attributable mostly to climatic changes and the activities of the man, makes our job challenging and complicated, since it varies from region to region. We are aware of all this and therefore we think that the methodological approach of survey of the data is an extremely important element to locate of the phenomenon and to monitor its course. This work, which is characterized for its multi-disciplinary approach, suggests solutions that we wish will quickly find concrete applications at international level. Prof. Bruno Dettori President CNLSD (Comitato Nazionale per la Lotta alla Siccità e alla Desertificazione) v
R 5,999.53
Getting Started with Python Data Analysis
Learn to use powerful Python libraries for effective data processing and analysisAbout This Book• Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib• Create, manipulate, and analyze your data to extract useful information to optimize your system• A hands-on guide to help you learn data analysis using PythonWho This Book Is ForIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.What You Will Learn• Understand the importance of data analysis and get familiar with its processing steps• Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis• Create effective visualizations to present your data using Matplotlib• Process and analyze data using the time series capabilities of Pandas• Interact with different kind of database systems, such as file, disk format, Mongo, and Redis• Apply the supported Python package to data analysis applications through examples• Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python libraryIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.With this book, we will get you started with Python data analysis and show you what its advantages are.The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn. Style and approachThis is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
R 1,335.00
Exact Statistical Methods for Data Analysis
This book covers some recent developments in statistical inference. The author's main aim is to develop a theory of generalized p-values and generalized confidence intervals and to show how these concepts may be used to make exact statistical inferences in a variety of practical applications. In particular, they provide methods applicable in problems involving nuisance parameters such as those encountered in comparing two exponential distributions or in ANOVA without the assumption of equal error variances. The generalized procedures are shown to be more powerful in detecting significant experimental results and in avoiding misleading conclusions.
R 2,748.33
Data Analysis in the Cloud
Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.
R 1,176.00
