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Automate The Boring Stuff With Python, 2nd Edition
In this second edition of Automate the Boring Stuff with Python, you'll learn the basics of programming in Python, the fastest growing programming language today, before moving on to create Python programs that effortlessly perform useful and impressive feats of automation. This updated edition is full of step-by-step instructions that walk through each programme. Practice projects at the end of each chapter challenge you to improve those programmes and use your newfound skills to automate similar tasks.
R 1,391.00
R 763.00
Mathematica Data Analysis
Learn and explore the fundamentals of data analysis with power of MathematicaAbout This Book• Use the power of Mathematica to analyze data in your applications• Discover the capabilities of data classification and pattern recognition offered by Mathematica• Use hundreds of algorithms for time series analysis to predict the futureWho This Book Is ForThe book is for those who want to learn to use the power of Mathematica to analyze and process data. Perhaps you are already familiar with data analysis but have never used Mathematica, or you know Mathematica but you are new to data analysis. With the help of this book, you will be able to quickly catch up on the key points for a successful start.What You Will Learn• Import data from different sources to Mathematica• Link external libraries with programs written in Mathematica• Classify data and partition them into clusters• Recognize faces, objects, text, and barcodes• Use Mathematica functions for time series analysis• Use algorithms for statistical data processing• Predict the result based on the observationsIn DetailThere are many algorithms for data analysis and it's not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis.If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure.With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems.With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.Style and approachThis book takes a step-by-step approach, accompanied by examples, so you get a better understanding of the logic of writing algorithms for data analysis in Mathematica. We provide a detailed explanation of all the nuances of the Mathematica language, no matter what your level of experience is.
R 1,335.00
Just Plain Data Analysis
Just Plain Data Analysis teaches students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social conditions; how to assess the reliability and validity of specific indicators; how to present data efficiently in charts and tables; how to avoid common misinterpretations and misrepresentations of data; and how to evaluate causal arguments based on numerical data. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way.
R 4,946.00
Eco-Stats: Data Analysis in Ecology
This book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis.For biologists with relatively little prior knowledge of statistics, it introduces a modern, advanced approach to data analysis in an intuitive and accessible way. The book begins by reviewing some core principles in statistics, and relates common methods to the linear model, a general framework for modeling data where the response is continuous. This is then extended to discrete data using generalized linear models, to designs with multiple sampling levels via mixed models, and to situations where there are multiple response variables via model-based approaches to multivariate analysis. Along the way there is an introduction to: important principles in model selection; adaptations of the model to handle non-linearity and cyclical variables; dependence due to structured correlation in time, space or phylogeny; and design-based techniques for inference that can relax some of the modelling assumptions. It concludes with a range of advanced topics in model-based multivariate analysis relevant to the modern ecologist, including fourth corner, latent variable and copula models.Examples span a variety of applications including environmental monitoring, species distribution modeling, global-scale surveys of plant traits, and small field experiments on biological controls. Math Boxes throughout the book explain some of the core ideas mathematically for readers who want to delve deeper, and R code is used throughout. Accompanying code, data, and solutions to exercises can be found in the ecostats R package on CRAN.
R 5,861.00
Personal Finance with Python
Deal with data, build up financial formulas in code from scratch, and evaluate and think about money in your day-to-day life. This book is about Python and personal finance and how you can effectively mix the two together. In Personal Finance with Python you will learn Python and finance at the same time by creating a profit calculator, a currency converter, an amortization schedule, a budget, a portfolio rebalancer, and a purchase forecaster. Many of the examples use pandas, the main data manipulation tool in Python. Each chapter is hands-on, self-contained, and motivated by fun and interesting examples.Although this book assumes a minimal familiarity with programming and the Python language, if you don't have any, don't worry. Everything is built up piece-by-piece and the first chapters are conducted at a relaxed pace. You'll need Python 3.6 (or above) and all of the setup details are included.What You'll LearnWork with data in pandasCalculate Net Present Value and Internal Rate ReturnQuery a third-party API with RequestsManage secretsBuild efficient loopsParse English sentences with RecurrentWork with the YAML file formatFetch stock quotes and use Prophet to forecast the futureWho This Book Is ForAnyone interested in Python, personal finance, and/or both! This book is geared towards those who want to manage their money more effectively and to those who just want to learn or improve their Python.
R 1,260.23
Computer Programs for Qualitative Data Analysis
For those interested in finding a computer application well-suited for their own qualitative research or just learning more about the capabilities of the latest generation of computer software designed with text, Computer Programs for Qualitative Data Analysis by Eben A. Weitzman and Matthew B. Miles probably represents the single investment they can make. . . . In Computer Programs for Qualitative Data Analysis, Weitzman and Miles . . . provide a critical, in-depth look at 24 separate applications. The authors make an impressive team: Weitzman is a professor of social and organizational psychology with an extensive computer background, and Miles is a social psychologist who is well-known in the field of qualitative research for co-authoring a popular book on qualitative data analysis with A. Michael Huberman. Together, the two researchers have produced an informative, user-friendly sourcebook that can save readers a significant amount of time and money when shopping for a software program for qualitative data analysis. Weitzman and Miles clearly put a tremendous amount of work into Computer Programs for Qualitative Data Analysis; they write their reviews of each application in remarkably lucid and jargon-free language in a format reminiscent of Consumer Reports. The level of detail in the reviews reflects their careful and thoughtful field-testing of 24 software programs. Reviews average about 10 pages each and actually show you what each application can do, and every review includes a series of realistic visuals (complete with helpful captions) that display what the computer screen looks like when performing various functions with that specific computer program. In addition to describing the special features of each computer program, Weitzman and Miles discuss the strengths and weaknesses of every application and make explicit comparisons with other applications in the same ′family.′ "Sage Publications deserves special credit for their role in publishing Computer Programs for Qualitative Data Analysis. Instead of issuing this book in hardcover and attaching a hefty price tag, they released it as an oversized (81/2-by-11-inch) paperback and made this valuable information available at a modest cost. Computer Programs for Qualitative Data Analysis is the most comprehensive resource on its subject currently available, and is an excellent starting point for qualitative researchers interested in integrating computer technology more fully into their own data analysis strategies." --Harvard Educational Review "The book by Weitzman & Miles is one of many new books on computers and qualitative software and indeed a good one. . . . The book is truly a user′s book--one of the useful ones. . . . After this first feeling of self-confidence, I just kept on reading the book and found very thorough and illuminative reviews of no less that 24 computer programs for qualitative analysis. . . . Renata Tesch initiated the work of making it easier for us to surv
R 7,135.96
Spatial Data Analysis in the Social and Environmental Sciences
Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. Methods described include data description, map interpolation, and exploratory and explanatory analyses. The book also examines spatial referencing, and methods for detecting problems, assessing their seriousness and taking appropriate action are discussed. This is an important text for any discipline requiring a broad overview of current theoretical and applied work for the analysis of spatial data sets. It will be of particular use to research workers and final year undergraduates in the fields of geography, environmental sciences and social sciences.
R 2,646.45
Next-Generation Sequencing Data Analysis
Next-generation DNA and RNA sequencing has revolutionized biology and medicine. With sequencing cost continuously dropping and our ability to generate large datasets rising, data analysis becomes more important than ever. Next-Generation Sequencing Data Analysis walks readers through NGS data analysis step-by-step for a wide range of NGS applications. For each NGS application, this book covers topics from experimental design, sample processing, sequencing strategy formulation, to sequencing reads quality control, data preprocessing, reads mapping or assembly, and more advanced stages that are specific to each application. Major applications include: RNA-seq: both bulk and single-cell (separate chapters) Genotyping and variant discovery through whole genome/exome sequencing Clinical sequencing and detection of actionable variants De novo genome assembly ChIP-seq to map protein-DNA interactions Epigenomics through DNA methylation sequencing Metagenome sequencing for microbiome analysis Before detailing the analytic steps for each of these applications, the book presents introductory cellular and molecular biology as a refresher mostly for data scientists, the ins and outs of widely used NGS platforms, and an overview of computing needs for NGS data management and analysis. The book concludes with a chapter on the changing landscape of NGS technologies and data analytics. The second edition of this book builds on the well-received first edition by providing updates to each chapter. Two brand new chapters are added to meet rising data analysis demands on single-cell RNA-seq and clinical sequencing. The increasing use of long-reads sequencing has also been reflected in all NGS applications. This book discusses concepts and principles that underlie each analytic step, along with software tools for implementation. It highlights key features of the tools while omitting tedious details to provide an easy-to-follow guide for practitioners in life sciences, bioinformatics, biostatistics, and data science. Tools introduced in this book are open-source and freely available.
R 5,165.00
Data Analysis in Medicine and Health using R
In medicine and health, data are analysed to guide treatment plan, patients care as well as the control and prevention policies. However, in doing so, researchers in medicine and health often lack the understanding of data and statistical concepts and the skills in programming. In addition, there is also increasing demands for data analyses to be reproducible along with more complex data that requires cutting-edge analysis. This book provides readers with both the fundamental concepts of data and statistical analysis and modelling. It also the skills to perform the analysis using the R programming language which is the lingua-franca for statisticians. The topics in the book are presented in a sequence to minimize the time to help readers understand the objectives of data and statistical analysis, learn the concepts of statistical modelling and acquire to skills to perform the analysis. The R codes and datasets used in the book will be made available on GitHub for easy access. The book will also be live on the website bookdown.org, a service provided by RStudio, PBC to host books written using the bookdown package in R programming language.
R 9,321.00
Concept Data Analysis
With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information. Concept Data Analysis: Theory & Applications is the first book that provides a comprehensive treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices. The accompanying website allows you to gain a greater understanding of the principles covered in the book through actively working on the topics discussed. The three main areas explored are interactive mining of documents or collections of documents (including Web documents), automatic text ranking, and rule mining from structured data. The potentials of conceptual data analysis in the application areas being considered are further illustrated by two detailed case studies. Concept Data Analysis: Theory & Applications is essential for researchers active in information processing and management and industry practitioners who are interested in creating a commercial product for conceptual data analysis or developing content management applications.
R 4,557.00
Statistical Analysis of Microbiome Data with R
This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.
R 7,326.00
Behavioral Research Data Analysis with R
This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool. The authors aim primarily to help practitioners of behavioral research make the transition to R. The focus is to provide practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples. The book will also help beginners learn more about statistics and behavioral research. These are statistical techniques used by psychologists who do research on human subjects, but of course they are also relevant to researchers in others fields that do similar kinds of research. The authors emphasize practical data analytic skills so that they can be quickly incorporated into readers’ own research.
R 3,339.01
Statistical Analysis of Financial Data
Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data.Features* Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis.* Driven by real, current financial data, not just stale data deposited on some static website.* Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.
R 6,960.00
Data Analysis in Medicine and Health using R
In medicine and health, data are analysed to guide treatment plan, patients care as well as the control and prevention policies. However, in doing so, researchers in medicine and health often lack the understanding of data and statistical concepts and the skills in programming. In addition, there is also increasing demands for data analyses to be reproducible along with more complex data that requires cutting-edge analysis. This book provides readers with both the fundamental concepts of data and statistical analysis and modelling. It also the skills to perform the analysis using the R programming language which is the lingua-franca for statisticians. The topics in the book are presented in a sequence to minimize the time to help readers understand the objectives of data and statistical analysis, learn the concepts of statistical modelling and acquire to skills to perform the analysis. The R codes and datasets used in the book will be made available on GitHub for easy access. The book will also be live on the website bookdown.org, a service provided by RStudio, PBC to host books written using the bookdown package in R programming language.
R 2,723.44
Data Analysis for the Social Sciences
′This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.′ —Ruth Horry, Psychology, Swansea University ′This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.′ —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbersCalculate probability and value distributionsTest a priori and post hoc hypothesesConduct Chi-squared tests and observational studiesStructure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
R 3,590.00
Problem Solving and Data Analysis Using Minitab
Six Sigma statistical methodology using Minitab Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving. Each example and exercise is broken down into the exact steps that must be followed in order to take the reader through key learning points and work through complex analyses. Exercises are featured at the end of each example so that the reader can be assured that they have understood the key learning points. Key features: Provides readers with a step by step guide to problem solving and statistical analysis using Minitab 16 which is also compatible with version 15.Includes fully worked examples with graphics showing menu selections and Minitab outputs.Uses example based learning that the reader can work through at their pace.Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results.Presents the core statistical techniques used by Six Sigma Black Belts. Contains examples, exercises and solutions throughout, and is supported by an accompanying website featuring the numerous example data sets. Making Six Sigma statistical methodology accessible to beginners, this book is aimed at numerical professionals, students or academics who wish to learn and apply statistical techniques for problem solving, process improvement or data analysis whilst keeping mathematical theory to a minimum.
R 3,190.00
Introduction to Political and Social Data Analysis (With R)
An Introduction to Political and Social Data Analysis (With R) provides students with an accessible overview of practical data analysis while also providing a gentle introduction to the R programming environment. Author Thomas M. Holbrook patiently explains each step in statistical analysis with R, avoiding complicated tools or packages.
R 5,499.00
R 885.00
Data Analysis and Approximate Models
The First Detailed Account of Statistical Analysis That Treats Models as ApproximationsThe idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating the "true but unknown" parameter value that generated the data.Forgoing any concept of truth, Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth.The author develops a concept of approximation for probability models with applications to: Discrete dataLocation scaleAnalysis of variance (ANOVA)Nonparametric regression, image analysis, and densitiesTime seriesModel choiceThe book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback–Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter assesses a range of statistical topics, from the likelihood principle to asymptotics and model choice.
R 9,954.94
R 702.00
Data Analysis in Astronomy
Systems and Archives in Astronomy: Astrophysics Data Systems (F. Giovane). MIDAS OnLine at the Telescopes (K. Banse et al.). Astronomical Databases (A. Heck). The HST Mission: The Hubble Space Telescope: Year One (R.J. Hanisch). HST Image Restoration (H.M. Adorf). Detecting Cosmic Rays Hits on HST WF/PC Images Using Neural Networks and Other Discriminant Analysis Approaches (F.D. Murtagh, H.M. Adorf). The ROSAT Mission: Mission Planning with ROSAT (S.L. Snowden, J.H.M.M. Schmitt). The Standard Automatic Analysis Software System (R. Gruber). The GRO Mission: Response Determination of COMPTEL from Calibration Measurements, Models, and Simulators (R. Diehl et al.). The OSSE Data Analysis System (M.S. Strickman). Future Missions: The Extreme Ultraviolet Explorer Mission (C.A. Christian). The SAX Mission (R.C. Butler, L. Scarsi). 24 additional articles. Index.
R 2,797.52
Applied Multiway Data Analysis
From a preeminent authority—a modern and applied treatment of multiway data analysis This groundbreaking book is the first of its kind to present methods for analyzing multiway data by applying multiway component techniques. Multiway analysis is a specialized branch of the larger field of multivariate statistics that extends the standard methods for two-way data, such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data. Applied Multiway Data Analysis presents a unique, thorough, and authoritative treatment of this relatively new and emerging approach to data analysis that is applicable across a range of fields, from the social and behavioral sciences to agriculture, environmental sciences, and chemistry. General introductions to multiway data types, methods, and estimation procedures are provided in addition to detailed explanations and advice for readers who would like to learn more about applying multiway methods. Using carefully laid out examples and engaging applications, the book begins with an introductory chapter that serves as a general overview of multiway analysis, including the types of problems it can address. Next, the process of setting up, carrying out, and evaluating multiway analyses is discussed along with commonly encountered issues, such as preprocessing, missing data, model and dimensionality selection, postprocessing, and transformation, as well as robustness and stability issues. Extensive examples are presented within a unified framework consisting of a five-step structure: objectives; data description and design; model and dimensionality selection; results and their interpretation; and validation. Procedures featured in the book are conducted using 3WayPack, which is software developed by the author, and analyses can also be carried out within the R and MATLAB systems. Several data sets and 3WayPack can be downloaded via the book's related Web site. The author presents the material in a clear, accessible style without unnecessary or complex formalism, assuring a smooth transition from well-known standard two-analysis to multiway analysis for readers from a wide range of backgrounds. An understanding of linear algebra, statistics, and principal component analyses and related techniques is assumed, though the author makes an effort to keep the presentation at a conceptual, rather than mathematical, level wherever possible. Applied Multiway Data Analysis is an excellent supplement for component analysis and statistical multivariate analysis courses at the upper-undergraduate and beginning graduate levels. The book can also serve as a primary reference for statisticians, data analysts, methodologists, applied mathematicians, and social science researchers working in academia or industry. Visit the Related Website: http://three-mode.leidenuniv.nl/, to view data from the book.
R 6,186.00
Introduction to Computational Models with Python
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website.The book’s five sections present: An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware toolsProgramming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithmsPython lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under LinuxImplementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from problem formulation to implementation of computational modelsThis book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.
R 6,704.27
Graphical Data Analysis with R
See How Graphics Reveal InformationGraphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.
R 4,066.00
Computer Programs for Qualitative Data Analysis
For those interested in finding a computer application well-suited for their own qualitative research or just learning more about the capabilities of the latest generation of computer software designed with text, Computer Programs for Qualitative Data Analysis by Eben A. Weitzman and Matthew B. Miles probably represents the single investment they can make. . . . In Computer Programs for Qualitative Data Analysis, Weitzman and Miles . . . provide a critical, in-depth look at 24 separate applications. The authors make an impressive team: Weitzman is a professor of social and organizational psychology with an extensive computer background, and Miles is a social psychologist who is well-known in the field of qualitative research for co-authoring a popular book on qualitative data analysis with A. Michael Huberman. Together, the two researchers have produced an informative, user-friendly sourcebook that can save readers a significant amount of time and money when shopping for a software program for qualitative data analysis. Weitzman and Miles clearly put a tremendous amount of work into Computer Programs for Qualitative Data Analysis; they write their reviews of each application in remarkably lucid and jargon-free language in a format reminiscent of Consumer Reports. The level of detail in the reviews reflects their careful and thoughtful field-testing of 24 software programs. Reviews average about 10 pages each and actually show you what each application can do, and every review includes a series of realistic visuals (complete with helpful captions) that display what the computer screen looks like when performing various functions with that specific computer program. In addition to describing the special features of each computer program, Weitzman and Miles discuss the strengths and weaknesses of every application and make explicit comparisons with other applications in the same ′family.′ "Sage Publications deserves special credit for their role in publishing Computer Programs for Qualitative Data Analysis. Instead of issuing this book in hardcover and attaching a hefty price tag, they released it as an oversized (81/2-by-11-inch) paperback and made this valuable information available at a modest cost. Computer Programs for Qualitative Data Analysis is the most comprehensive resource on its subject currently available, and is an excellent starting point for qualitative researchers interested in integrating computer technology more fully into their own data analysis strategies." --Harvard Educational Review "The book by Weitzman & Miles is one of many new books on computers and qualitative software and indeed a good one. . . . The book is truly a user′s book--one of the useful ones. . . . After this first feeling of self-confidence, I just kept on reading the book and found very thorough and illuminative reviews of no less that 24 computer programs for qualitative analysis. . . . Renata Tesch initiated the work of making it easier for us to surv
R 5,866.78
Web Scraping with Python
If programming is magic, then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. This thoroughly updated third edition not only introduces you to web scraping but also serves as a comprehensive guide to scraping almost every type of data from the modern web.Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter.Parse complicated HTML pagesDevelop crawlers with the Scrapy frameworkLearn methods to store the data you scrapeRead and extract data from documentsClean and normalize badly formatted dataRead and write natural languagesCrawl through forms and loginsScrape JavaScript and crawl through APIsUse and write image-to-text softwareAvoid scraping traps and bot blockersUse scrapers to test your website
R 1,638.00
Applying Math with Python
Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to utilize Python's libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionPython, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What you will learnGet familiar with basic packages, tools, and libraries in Python for solving mathematical problemsExplore various techniques that will help you to solve computational mathematical problemsUnderstand the core concepts of applied mathematics and how you can apply them in computer scienceDiscover how to choose the most suitable package, tool, or technique to solve a certain problemImplement basic mathematical plotting, change plot styles, and add labels to the plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is forThis book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
R 1,335.00
Exploratory Causal Analysis with Time Series Data
Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics.A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
R 1,844.44
Qualitative Data Analysis with ATLAS.ti
Now updated to cover latest versions and featuring instructions for both Mac and Windows users, this book is still the go-to source of support for getting to grips with qualitative data analysis using ATLAS.ti.With over twenty years of experience using, developing, and teaching ATLAS.ti, Susanne Friese is the perfect data analysis tour guide. Aware of common challenges and sticking points, she eases readers from readying and organizing data into coding and querying it, providing not only tips on how to prepare for analysis, but also the tools and technical know-how needed to observe, examine, and discuss data. Placing quick software ‘skills training’ tutorials alongside different stages of the data analysis process, she gives readers the opportunity to integrate software training with their actual analysis.
R 5,714.52
R 420.69
Introduction to Data Analysis
Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques - Data management - The big data phenomenon - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.
R 5,545.34
Performing Data Analysis Using IBM SPSS
Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS® Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical outputDescriptive statistics proceduresScore distribution assumption evaluationsBivariate correlationRegressing (predicting) quantitative and categorical variablesSurvival analysist TestANOVA and ANCOVAMultivariate group differencesMultidimensional scalingCluster analysisNonparametric procedures for frequency data Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.
R 3,471.00
Practical Data Analysis
Each chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis.Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle.
R 1,889.00
Multivariate Data Analysis
For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today’s world of analytics.Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.
R 3,645.00
Qualitative Data Analysis
With numerous approaches to choose from, knowing where to start when doing qualitative data analysis (QDA) can be a challenge. This book gives you direction with an accessible and thorough introduction to nine different approaches to QDA, written by a multi-disciplinary team with years of experience teaching and analysing data using these methods. With a clear focus on the ‘how to’ of QDA, each chapter includes: •Step-by-step descriptions of how to apply each approach in your research •Online and in-text activities to help you practice your skills •Annotated reading lists so you can dig deeper into key topics •Case studies from a range of disciplines so you can see how each approach works in practice. The perfect companion on your journey through QDA, the book also offers a comprehensive introduction to the use of NVivo QDA software, helping both new and experienced researchers get to grips with the essentials.
R 4,779.01
Text Analytics with Python
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques.There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release.What You'll Learn• Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLPWho This Book Is ForIT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
R 1,227.11
Qualitative Data Analysis with ATLAS.ti
Now updated to cover latest versions and featuring instructions for both Mac and Windows users, this book is still the go-to source of support for getting to grips with qualitative data analysis using ATLAS.ti.With over twenty years of experience using, developing, and teaching ATLAS.ti, Susanne Friese is the perfect data analysis tour guide. Aware of common challenges and sticking points, she eases readers from readying and organizing data into coding and querying it, providing not only tips on how to prepare for analysis, but also the tools and technical know-how needed to observe, examine, and discuss data. Placing quick software ‘skills training’ tutorials alongside different stages of the data analysis process, she gives readers the opportunity to integrate software training with their actual analysis.
R 3,236.84
Introduction to Data Analysis Using IBM SPSS
This textbook book elaborates on the basic understanding and application of statistical tests and data analysis using hypothetical datasets and SPSS version 22.0. It presents step-by-step processes and to-the-point interpretation for quick assimilation and comprehension.Data Analysis Using SPSS,- Begins from the stages of data entry and goes on till editing and data visualization.- Takes the readers through descriptive statistics, frequency, univariate, bivariate and regression analysis, cross-tabulation, linear models, and non-parametric test procedures.- Presents information through dialog boxes and tabular charts, it also incorporates important formulae, calculations, and illustrations of sample data for an in-depth understanding of concepts and results.- Can be used as a textbook as well as a reference book as it helps to build conceptual understanding, gaining software skills, communicate data and its insights to accomplish research assignments, research/ capstone projects and PhD research work.With comprehensive coverage, this book would be useful to the students, researchers, and teacher of the various social sciences disciplines like Psychology, Sociology, Education, Social Work, Agriculture Management, and other allied subjects. It would also be an invaluable companion to professionals and data scientist working in the field of analytics.
R 2,015.00
Eco-Stats: Data Analysis in Ecology
This book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis.For biologists with relatively little prior knowledge of statistics, it introduces a modern, advanced approach to data analysis in an intuitive and accessible way. The book begins by reviewing some core principles in statistics, and relates common methods to the linear model, a general framework for modeling data where the response is continuous. This is then extended to discrete data using generalized linear models, to designs with multiple sampling levels via mixed models, and to situations where there are multiple response variables via model-based approaches to multivariate analysis. Along the way there is an introduction to: important principles in model selection; adaptations of the model to handle non-linearity and cyclical variables; dependence due to structured correlation in time, space or phylogeny; and design-based techniques for inference that can relax some of the modelling assumptions. It concludes with a range of advanced topics in model-based multivariate analysis relevant to the modern ecologist, including fourth corner, latent variable and copula models.Examples span a variety of applications including environmental monitoring, species distribution modeling, global-scale surveys of plant traits, and small field experiments on biological controls. Math Boxes throughout the book explain some of the core ideas mathematically for readers who want to delve deeper, and R code is used throughout. Accompanying code, data, and solutions to exercises can be found in the ecostats R package on CRAN.
R 5,861.00
Quantitative Data Analysis
This book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a basic understanding of the steps that each method involves, and of the facets of the analysis that require special attention. Rather than presenting an exhaustive overview of the methods or explaining them in detail, the book serves as a starting point for developing data analysis skills: it provides hands-on guidelines for conducting the most common analyses and reporting results, and includes pointers to more extensive resources. Comprehensive yet succinct, the book is brief and written in a language that everyone can understand - from students to those employed by organizations wanting to study the context in which they work. It also serves as a refresher for researchers who have learned data analysis techniques previously but who need a reminder for the specific study they are involved in.
R 2,747.47
Deep Learning with Python
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to productionWho This Book Is ForSoftware developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
R 1,965.56
Microarray Data Analysis
This meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data. The book includes examples and code in R, requiring only an introductory computer science understanding, and the structure and the presentation of the chapters make it suitable for use in bioinformatics courses. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of key detail and expert implementation advice that ensures successful results and reproducibility. Authoritative and practical, Microarray Data Analysis is an ideal guide for students or researchers who need to learn the main research topics and practitioners who continue to work with microarray datasets.
R 4,758.14
Introduction to GIS Programming and Fundamentals with Python and ArcGIS®
Combining GIS concepts and fundamental spatial thinking methodology with real programming examples, this book introduces popular Python-based tools and their application to solving real-world problems. It elucidates the programming constructs of Python with its high-level toolkits and demonstrates its integration with ArcGIS Theory. Filled with hands-on computer exercises in a logical learning workflow this book promotes increased interactivity between instructors and students while also benefiting professionals in the field with vital knowledge to sharpen their programming skills. Readers receive expert guidance on modules, package management, and handling shapefile formats needed to build their own mini-GIS. Comprehensive and engaging commentary, robust contents, accompanying datasets, and classroom-tested exercises are all housed here to permit users to become competitive in the GIS/IT job market and industry.
R 6,118.00
