Probability, Random Variables, Statistics, and Random Processes: Fundamentals & Applications is a comprehensive undergraduate-level textbook. With its excellent topical coverage, the focus of this book is on the basic principles and practical applications of the fundamental concepts that are extensively used in various Engineering disciplines as well as in a variety of programs in Life and Social Sciences. The text provides students with the requisite building blocks of knowledge they require to understand and progress in their areas of interest. With a simple, clear-cut style of writing, the intuitive explanations, insightful examples, and practical applications are the hallmarks of this book. The text consists of twelve chapters divided into four parts. Part-I, Probability (Chapters 1 – 3), lays a solid groundwork for probability theory, and introduces applications in counting, gambling, reliability, and security. Part-II, Random Variables (Chapters 4 – 7), discusses in detail multiple random variables, along with a multitude of frequently-encountered probability distributions. Part-III, Statistics (Chapters 8 – 10), highlights estimation and hypothesis testing. Part-IV, Random Processes (Chapters 11 – 12), delves into the characterization and processing of random processes. Other notable features include: Most of the text assumes no knowledge of subject matter past first year calculus and linear algebra With its independent chapter structure and rich choice of topics, a variety of syllabi for different courses at the junior, senior, and graduate levels can be supported A supplemental website includes solutions to about 250 practice problems, lecture slides, and figures and tables from the text Given its engaging tone, grounded approach, methodically-paced flow, thorough coverage, and flexible structure, Probability, Random Variables, Statistics, and Random Processes: Fundamentals & Applications clearly serves as a must textbook for courses not only in Electrical Engineering, but also in Computer Engineering, Software Engineering, and Computer Science.

Comprehensively teaches the basics of testing statistical assumptions in research and the importance in doing so This book facilitates researchers in checking the assumptions of statistical tests used in their research by focusing on the importance of checking assumptions in using statistical methods, showing them how to check assumptions, and explaining what to do if assumptions are not met. Testing Statistical Assumptions in Research discusses the concepts of hypothesis testing and statistical errors in detail, as well as the concepts of power, sample size, and effect size. It introduces SPSS functionality and shows how to segregate data, draw random samples, file split, and create variables automatically. It then goes on to cover different assumptions required in survey studies, and the importance of designing surveys in reporting the efficient findings. The book provides various parametric tests and the related assumptions and shows the procedures for testing these assumptions using SPSS software. To motivate readers to use assumptions, it includes many situations where violation of assumptions affects the findings. Assumptions required for different non-parametric tests such as Chi-square, Mann-Whitney, Kruskal Wallis, and Wilcoxon signed-rank test are also discussed. Finally, it looks at assumptions in non-parametric correlations, such as bi-serial correlation, tetrachoric correlation, and phi coefficient. An excellent reference for graduate students and research scholars of any discipline in testing assumptions of statistical tests before using them in their research study Shows readers the adverse effect of violating the assumptions on findings by means of various illustrations Describes different assumptions associated with different statistical tests commonly used by research scholars Contains examples using SPSS, which helps facilitate readers to understand the procedure involved in testing assumptions Looks at commonly used assumptions in statistical tests, such as z, t and F tests, ANOVA, correlation, and regression analysis Testing Statistical Assumptions in Research is a valuable resource for graduate students of any discipline who write thesis or dissertation for empirical studies in their course works, as well as for data analysts.

Discover how to harness the power of Tableau 2019 to transform raw data into insightful, actionable business intelligence. This book serves as a comprehensive guide to mastering Tableau's features-from creating stunning visualizations to managing complex datasets with Tableau Prep. By the end, you'll be well-equipped to use Tableau for informed decision-making. What this Book will help me do Master the essential features of Tableau 2019 to become proficient in data visualization. Learn to prepare and integrate data effectively using Tableau Prep. Develop advanced visual analytics skills, including calculations and table calculations. Understand how to craft compelling dashboards and data stories for impactful communication. Leverage new Tableau features like set actions and transparent views for enhanced analytics. Author(s) Joshua N. Milligan is a Tableau-certified professional and Tableau Zen Master with extensive industry experience in data analytics. Known for his clarity in teaching, Joshua takes a practical and comprehensive approach to help users navigate Tableau effectively. His passion for empowering data-driven decisions is evident in his writing. Who is it for? This book is ideal for data professionals, analysts, or anyone new to Tableau who seeks to gain proficiency in data visualization and analysis. It is suitable for beginners, as it walks the reader through foundational concepts before introducing complex topics. Readers looking to enhance their skills in advanced Tableau techniques will also find value here. Familiarity with databases is helpful but not mandatory.

International Futures: Building and Using Global Models extensively covers one of the most advanced systems for integrated, long-term, global and large-scale forecasting analysis available today, the International Futures (IFs) system. Key elements of a strong, long-term global forecasting system are described, i.e. the formulations for the driving variables in separate major models and the manner in which these separate models are integrated. The heavy use of algorithmic and rule-based elements and the use of elements of control theory is also explained. Furthermore, the IFs system is compared and contrasted with all other major modeling efforts, also outlining the major benefits of the IFs system. Finally, the book provides suggestions on how the development of forecasting systems might most productively proceed in the coming years. Helps readers understand the IFs system, not at a detailed equation and technical level, but in terms of the important decisions made that dominate the structure and long-term behavior Presents information on the universe of long-term global forecasting systems, key decisions made, and the range of similarities and differences in the systems Covers the relationship between long-term forecasts in a variety of global issues and the forecasting systems and assumptions that underly them (essential information for forecast consumers)

Introduces the latest developments in forecasting in advanced quantitative data analysis This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable. Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers. Presents models that are all classroom tested Contains real-life data samples Contains over 350 equation specifications of various time series models Contains over 200 illustrative examples with special notes and comments Applicable for time series data of all quantitative studies Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.

Offers timely and comprehensive coverage of dynamic system reliability theory This book focuses on hot issues of dynamic system reliability, systematically introducing the reliability modeling and analysis methods for systems with imperfect fault coverage, systems with function dependence, systems subject to deterministic or probabilistic common-cause failures, systems subject to deterministic or probabilistic competing failures, and dynamic standby sparing systems. It presents recent developments of such extensions involving reliability modelling theory, reliability evaluation methods, and features numerous case studies based on real-world examples. The presented dynamic reliability theory can enable a more accurate representation of actual complex system behavior, thus more effectively guiding the reliable design of real-world critical systems. Dynamic System Reliability: Modelling and Analysis of Dynamic and Dependent Behaviors begins by describing the evolution from the traditional static reliability theory to the dynamic system reliability theory, and provides a detailed investigation of dynamic and dependent behaviors in subsequent chapters. Although written for those with a background in basic probability theory and stochastic processes, the book includes a chapter reviewing the fundamentals that readers need to know in order to understand contents of other chapters which cover advanced topics in reliability theory and case studies. The first book systematically focusing on dynamic system reliability modelling and analysis theory Provides a comprehensive treatment on imperfect fault coverage (single-level/multi-level or modular), function dependence, common cause failures (deterministic and probabilistic), competing failures (deterministic and probabilistic), and dynamic standby sparing Includes abundant illustrative examples and case studies based on real-world systems Covers recent advances in combinatorial models and algorithms for dynamic system reliability analysis Offers a rich set of references, providing helpful resources for readers to pursue further research and study of the topics Dynamic System Reliability: Modelling and Analysis of Dynamic and Dependent Behaviors is an excellent book for undergraduate and graduate students, and engineers and researchers in reliability and related disciplines.

The first book to be published on the Theta method, outlining under what conditions the method outperforms other forecasting methods This book is the first to detail the Theta method of forecasting – one of the most difficult-to-beat forecasting benchmarks, which topped the biggest forecasting competition in the world in 2000: the M3 competition. Written by two of the leading experts in the forecasting field, it illuminates the exact replication of the method and under what conditions the method outperforms other forecasting methods. Recent developments such as multivariate models are also included, as are a series of practical applications in finance, economics, and healthcare. The book also offers practical tools in MS Excel and guidance, as well as provisional access, for the use of R source code and respective packages. Forecasting with the Theta Method: Theory and Applications includes three main parts. The first part, titled Theory, Methods, Models & Applications details the new theory about the method. The second part, Applications & Performance in Forecasting Competitions, describes empirical results and simulations on the method. The last part roadmaps future research and also include contributions from another leading scholar of the method – Dr. Fotios Petropoulos. First ever book to be published on the Theta Method Explores new theory and exact conditions under which methods would outperform most forecasting benchmarks Clearly written with practical applications Employs R – open source code with all included implementations Forecasting with the Theta Method: Theory and Applications is a valuable tool for both academics and practitioners involved in forecasting and respective software development.

An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases. Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection Contains an analysis of medical databases to provide diagnostic expert systems Addresses the integration of intelligent data analysis techniques within biomedical information systems

With this book, aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhere to. Practising data analysts can explore useful data analysis tools, methods and techniques, brush up on best practices and look at how they can advance their career.

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts. Provides comprehensive and systematic coverage of machine learning-based data analysis tasks Enables rapid progress towards competency in data analysis techniques Gives exhaustive and widely applicable patterns for use by data scientists Covers hybrid or ‘meta’ approaches, along with general analytics Lays out information and practical guidance on data analysis for practitioners working across all sectors

This book, 'Learn Chart.js', serves as a comprehensive guide to mastering Chart.js for creating stunning web-based data visualizations. By combining JavaScript, HTML5 Canvas, and Chart.js, you will understand how to turn raw data into interactive visual stories. What this Book will help me do Develop skills to create interactive and engaging data visualizations using the Chart.js library. Learn to efficiently load, parse, and handle data from external formats like CSV and JSON. Understand different chart types offered by Chart.js and learn when to best use each one. Gain the ability to customize Chart.js charts, such as adjusting properties for styling or animations. Acquire hands-on experience with practical examples, equipping you to apply what you learn in real-world scenarios. Author(s) Helder da Rocha brings his extensive experience in programming and software development to this book, offering readers a clear and practical approach to mastering Chart.js. With a deep understanding of data visualization and web technologies, he conveys complex concepts in a straightforward way. Who is it for? This book is ideal for web developers, data analysts, and designers who have basic proficiency in HTML, CSS, and JavaScript. It is particularly suited for professionals looking to create impactful web-based data visualizations using open-source tools. Additionally, the book assumes no prior knowledge of the Canvas element, making it accessible for Chart.js beginners.

Mastering Tableau 2019.1 is your essential guide for becoming an expert in Tableau's advanced features and functionalities. This book will teach you how to use Tableau Prep for data preparation, create complex visualizations and dashboards, and leverage Tableau's integration with R, Python, and MATLAB. You'll be equipped with the skills to solve both common and advanced BI challenges. What this Book will help me do Gain expertise in preparing and blending data using Tableau Prep and other data handling tools. Create advanced data visualizations and designs that effectively communicate insights. Implement narrative storytelling in BI with advanced presentation designs in Tableau. Integrate Tableau with programming tools like R, Python, and MATLAB for extended functionalities. Optimize performance and improve dashboard interactivity for user-friendly analytics solutions. Author(s) Marleen Meier, with extensive experience in business intelligence and analytics, and None Baldwin, an expert in data visualization, collaboratively bring this advanced Tableau guide to life. Their passion for empowering users with practical BI solutions reflects in the hands-on approach employed throughout the book. Who is it for? This book is perfectly suited for business analysts, BI professionals, and data analysts who already have foundational knowledge of Tableau and seek to advance their skills for tackling more complex BI challenges. It's ideal for individuals aiming to master Tableau's premium features for impactful analytics solutions.

Stata is one of the most popular statistical software in the world and suited for all kinds of users, from absolute beginners to experienced veterans. This book offers a clear and concise introduction to the usage and the workflow of Stata. Included topics are importing and managing datasets, cleaning and preparing data, creating and manipulating variables, producing descriptive statistics and meaningful graphs as well as central quantitative methods, like linear (OLS) and binary logistic regressions and matching. Additional information about diagnostical tests ensures that these methods yield valid and correct results that live up to academic standards. Furthermore, users are instructed how to export results that can be directly used in popular software like Microsoft Word for seminar papers and publications. Lastly, the book offers a short yet focussed introduction to scientific writing, which should guide readers through the process of writing a first quantitative seminar paper or research report. The book underlines correct usage of the software and a productive workflow which also introduces aspects like replicability and general standards for academic writing. While absolute beginners will enjoy the easy to follow point-and-click interface, more experienced users will benefit from the information about do-files and syntax which makes Stata so popular. Lastly, a wide range of user-contributed software („Ados") is introduced which further improves the general workflow and guarantees the availability of state of the art statistical methods.

A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.

Dive into the world of Kibana 7 with this hands-on guide that simplifies the process of visualizing and analyzing data using Elasticsearch. From fundamental concepts to advanced tools, this book enables you to create intuitive dashboards and leverage powerful machine learning capabilities effectively. Discover how to transform your data into actionable insights with ease. What this Book will help me do Configure Logstash to fetch and process CSV data for visualization. Master creating and managing index patterns within Kibana for efficient data navigation. Effectively apply filters to refine data presentations and insights. Develop and utilize machine learning jobs in Kibana to identify trends and anomalies. Create, customize, and share impactful visualizations and dashboards to drive data-driven decisions. Author(s) None Srivastava is a technical expert in data visualization and Elasticsearch tools, with practical experience implementing and teaching about the Elastic Stack. The author brings a hands-on approach to this book, simplifying complex concepts for ease of understanding. Their expertise ensures that the book serves both as a learning guide and a practical reference. Who is it for? This book is ideal for developers and IT professionals who are either new to Kibana or looking to deepen their understanding of its visualization capabilities. It is suitable for individuals working with the Elastic Stack or seeking to leverage Kibana for data analysis purposes. Even if you are progressing from a novice to an intermediate level, this guide will provide future-proof skills to optimize your workflow.

Discover the ultimate guide to Tableau 2019.x that offers over 115 practical recipes to tackle business intelligence and data analysis challenges. This book takes you from the basics to advanced techniques, empowering you to create insightful dashboards, leverage powerful analytics, and seamlessly integrate with modern cloud data platforms. What this Book will help me do Master both basic and advanced functionalities of Tableau Desktop to effectively analyze and visualize data. Understand how to create impactful dashboards and compelling data stories for drive decision-making. Deploy advanced analytical tools including R-based forecasting and statistical techniques with Tableau. Set up and utilize Tableau Server in multi-node environments on Linux and Windows. Utilize Tableau Prep to efficiently clean, shape, and transform data for seamless integration into Tableau workflows. Author(s) The authors of the Tableau 2019.x Cookbook are recognized industry professionals with rich expertise in business intelligence, data analytics, and Tableau's ecosystem. Dmitry Anoshin and his co-authors bring hands-on experience from various industries to provide actionable insights. They focus on delivering practical solutions through structured learning paths. Who is it for? This book is tailored for data analysts, BI developers, and professionals equipped with some knowledge of Tableau wanting to enhance their skills. If you're aiming to solve complex analytics challenges or want to fully utilize the capabilities of Tableau products, this book offers the guidance and knowledge you need.

In "Go Web Scraping Quick Start Guide", you'll learn how to harness the power of the Go programming language to scrape and crawl data from websites effectively. This book covers fundamental techniques and essential libraries such as Colly and Goquery, helping you efficiently extract useful data while understanding best practices and avoiding common pitfalls. What this Book will help me do Master web scraping techniques using Go and libraries like Colly and Goquery. Understand HTTP request and response handling in the context of web scraping. Explore web scraping navigation strategies to retrieve the data you need efficiently and effectively. Learn to use Go's concurrency model for parallelized and scalable web scraping. Protect your scrapers from being blocked by implementing proxies and best practices. Author(s) None Smith is an experienced Go developer with a passion for teaching and simplifying technical concepts. With a strong background in software development and web technologies, they bring a practical approach to mastering Go and web scraping. Their clear writing style helps readers gain hands-on knowledge in applying technology effectively. Who is it for? This book is perfect for data scientists and web developers who have some prior knowledge of Go and want to extend their skills to include effective web scraping. Whether you're looking to extract data for analysis or develop solutions for web crawling tasks, this book provides a step-by-step approach tailored to practical applications. It's especially suited for professionals aiming to expand their technical toolkit for data and web projects.

A good visualization can communicate the nature and potential impact of ideas more powerfully than any other form of communication. For a long time, "dataviz" was left to specialists--data scientists and professional designers. No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could. The Harvard Business Review Good Charts Collection brings together two popular books to help you become more sophisticated in understanding and using dataviz to communicate your ideas and advance your career. In Good Charts, dataviz maven and Harvard Business Review editor Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. He lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping. In Good Charts Workbook, Berinato extends the usefulness of Good Charts by putting theory into practice. He leads readers step-by-step through several example datasets and basic charts, providing space to practice the Good Charts talk-sketch-prototype process for improving those charts. Examples include a "Discussion Key" showing how to approach the challenge and why. Each challenge focuses on a different, common visualization problem such as simplification, storytelling, creating conceptual charts, and many others. The Harvard Business Review Good Charts Collection is your go-to resource for turning plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas.

A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

Communicating your idea in a clear, compelling, and persuasive manner is critical when trying to launch a new venture. This Harvard Business Review collection brings together two popular books to help you craft your story, design better visualizations, impress your audience, and turn your idea into reality. Understanding and using data viz to persuade is a must-have skill for anyone in business today--especially if you're launching a new venture. In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping. How do you launch the venture of your dreams? In Get Backed, entrepreneurs Evan Baehr and Evan Loomis argue that it's not just about securing startup funding. It's about building the right relationships, crafting a compelling story, and creating the perfect pitch deck. Filled with proven tips, exercises, and templates, this book shows the process for how to successfully communicate your vision. Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas. Get Backed will show you exactly what it takes to get funded and will give you the tools to launch a new venture. Together, these books will help you bring your idea to life.

Talk. Sketch. Prototype. Repeat. You know right away when you see an effective chart or graphic. It hits you with an immediate sense of its meaning and impact. But what actually makes it clearer, sharper, and more effective? If you're ready to create your own "good charts"--data visualizations that powerfully communicate your ideas and research and that advance your career—the Good Charts Workbook is the hands-on guide you've been looking for. The original Good Charts changed the landscape by helping readers understand how to think visually and by laying out a process for creating powerful data visualizations. Now, the Good Charts Workbook provides tools, exercises, and practical insights to help people in all kinds of enterprises gain the skills they need to get started. Harvard Business Review Senior Editor and dataviz expert Scott Berinato leads you, step-by-step, through the key challenges in creating good charts—controlling color, crafting for clarity, choosing chart types, practicing persuasion, capturing concepts—with warm-up exercises and mini-challenges for each. The Workbook includes helpful prompts and reminders throughout, as well as white space for users to practice the Good Charts talk-sketch-prototype process. Good Charts Workbook is the must-have manual for better understanding the dataviz around you and for creating better charts to make your case more effectively.

Explore and understand data with the powerful data visualization techniques of Tableau, and then communicate insights in powerful ways Key Features Apply best practices in data visualization and chart types exploration Explore the latest version of Tableau Desktop with hands-on examples Understand the fundamentals of Tableau storytelling Book Description Graphical presentation of data enables us to easily understand complex data sets. Tableau 10 Complete Reference provides easy-to-follow recipes with several use cases and real-world business scenarios to get you up and running with Tableau 10. This Learning Path begins with the history of data visualization and its importance in today's businesses. You'll also be introduced to Tableau - how to connect, clean, and analyze data in this visual analytics software. Then, you'll learn how to apply what you've learned by creating some simple calculations in Tableau and using Table Calculations to help drive greater analysis from your data. Next, you'll explore different advanced chart types in Tableau. These chart types require you to have some understanding of the Tableau interface and understand basic calculations. You'll study in detail all dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. Last but not least, you'll learn about the power of storytelling through the creation of interactive dashboards in Tableau. Through this Learning Path, you will gain confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau. This Learning Path includes content from the following Packt products: Learning Tableau 10 - Second Edition by Joshua N. Milligan Getting Started with Tableau 2018.x by Tristan Guillevin What you will learn Build effective visualizations, dashboards, and story points Build basic to more advanced charts with step-by-step recipes Become familiar row-level, aggregate, and table calculations Dig deep into data with clustering and distribution models Prepare and transform data for analysis Leverage Tableau's mapping capabilities to visualize data Use data storytelling techniques to aid decision making strategy Who this book is for Tableau 10 Complete Reference is designed for anyone who wants to understand their data better and represent it in an effective manner. It is also used for BI professionals and data analysts who want to do better at their jobs. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.

Mastering Matplotlib 2.x guides you through the art and science of creating sophisticated data visualizations with Python's powerful Matplotlib library. You will start by learning the basics of plotting and customizing your charts, progressing to more advanced topics such as 3D visualization, geospatial data display, and creating interactive plots using Jupyter Notebook. What this Book will help me do Create complex and highly customizable data plots using Matplotlib. Effectively visualize data in three dimensions, including geospatial data. Use advanced matplotlib features to represent non-Cartesian and vector data. Build interactive visualizations using Jupyter Notebook and Python. Develop special-purpose and movie-style plots to enhance data representation. Author(s) None Keller is a seasoned software engineer and data visualization enthusiast with years of experience using Python for data analysis. Their practical and hands-on approach ensures that readers can directly apply the concepts taught in their projects. None aims to make advanced visualization techniques accessible to all. Who is it for? This book is perfect for developers, scientists, and analysts who need sophisticated visualization tools for their projects. Prior experience with Python and basic familiarity with Matplotlib will help you get the most out of the book. If you're looking to deepen your understanding of data visualization or to create interactive and advanced visualizations, this book is for you.