Kibana 8.x - A Quick Start Guide to Data Analysis is an essential resource for anyone wanting to harness the robust capabilities of Kibana to analyze, visualize, and make sense of their data. Through clear explanations and practical exercises, this guide breaks down topics like creating dashboards, exploring datasets, and configuring Kibana's powerful features. What this Book will help me do Understand Kibana's interface and functionalities to manage Elasticsearch data. Learn how to create intuitive visualizations and customize dashboards. Explore features such as data discovery and real-time updates for analytics. Optimize and query datasets using ESQL and detailed analytics techniques. Master the process of embedding dashboards and exporting insights. Author(s) None Shah is an experienced data analytics professional with a deep understanding of the Elastic Stack, including Kibana and Elasticsearch. Having spent years working on big data projects, Shah is dedicated to helping technologists turn data into actionable insights. Her writing aims to simplify complex concepts into achievable learning milestones. Who is it for? This book is ideal for data analysts, data engineers, and anyone working extensively with Elasticsearch datasets. If you aim to gain hands-on experience with building interactive dashboards and visualizing data trends, this book is tailored for you. A foundational understanding of Elasticsearch would be beneficial but is not strictly required. Perfect for advancing decision-making with data insights.

Learn Grafana 10.x is your essential guide to mastering the art of data visualization and monitoring through interactive dashboards. Whether you're starting from scratch or updating your knowledge to Grafana 10.x, this book walks you through installation, implementation, data transformation, and effective visualization techniques. What this Book will help me do Install and configure Grafana 10.x for real-time data visualization and analytics. Create and manage insightful dashboards with Grafana's enhanced features. Integrate Grafana with diverse data sources such as Prometheus, InfluxDB, and Elasticsearch. Set up dynamic templated dashboards and alerting systems for proactive monitoring. Implement Grafana's user authentication mechanisms for enhanced security. Author(s) None Salituro is a seasoned expert in data analytics and observability platforms with extensive experience working with time-series data using Grafana. Their practical teaching approach and passion for sharing insights make this book an invaluable resource for both newcomers and experienced users. Who is it for? This book is perfect for business analysts, data visualization enthusiasts, and developers interested in analyzing and monitoring time-series data. Whether you're a newcomer or have some background knowledge, this book offers accessible guidance and advanced tips suitable for all levels. If you're aiming to efficiently build and utilize Grafana dashboards, this is the book for you.

This book deals with the autoregressive method for digital processing of random oscillations. The method is based on a one-to-one transformation of the numeric factors of the Yule series model to linear elastic system characteristics. This parametric approach allowed to develop a formal processing procedure from the experimental data to obtain estimates of logarithmic decrement and natural frequency of random oscillations. A straightforward mathematical description of the procedure makes it possible to optimize a discretization of oscillation realizations providing efficient estimates. The derived analytical expressions for confidence intervals of estimates enable a priori evaluation of their accuracy. Experimental validation of the method is also provided. Statistical applications for the analysis of mechanical systems arise from the fact that the loads experienced by machineries and various structures often cannot be described by deterministic vibration theory. Therefore, a sufficient description of real oscillatory processes (vibrations) calls for the use of random functions. In engineering practice, the linear vibration theory (modeling phenomena by common linear differential equations) is generally used. This theory’s fundamental concepts such as natural frequency, oscillation decrement, resonance, etc. are credited for its wide use in different technical tasks. In technical applications two types of research tasks exist: direct and inverse. The former allows to determine stochastic characteristics of the system output X(t) resulting from a random process E(t) when the object model is considered known. The direct task enables to evaluate the effect of an operational environment on the designed object and to predict its operation under various loads. The inverse task is aimed at evaluating the object model on known processes E(t) and X(t), i.e. finding model (equations) factors. This task is usually met at the tests of prototypes to identify (or verify) its model experimentally. To characterize random processes a notion of "shaping dynamic system" is commonly used. This concept allows to consider the observing process as the output of a hypothetical system with the input being stationary Gauss-distributed ("white") noise. Therefore, the process may be exhaustively described in terms of parameters of that system. In the case of random oscillations, the "shaping system" is an elastic system described by the common differential equation of the second order: X ̈(t)+2hX ̇(t)+ ω_0^2 X(t)=E(t), where ω0 = 2π/Т0 is the natural frequency, T0 is the oscillation period, and h is a damping factor. As a result, the process X(t) can be characterized in terms of the system parameters – natural frequency and logarithmic oscillations decrement δ = hT0 as well as the process variance. Evaluation of these parameters is subjected to experimental data processing based on frequency or time-domain representations of oscillations. It must be noted that a concept of these parameters evaluation did not change much during the last century. For instance, in case of the spectral density utilization, evaluation of the decrement values is linked with bandwidth measurements at the points of half-power of the observed oscillations. For a time-domain presentation, evaluation of the decrement requires measuring covariance values delayed by a time interval divisible by T0. Both estimation procedures are derived from a continuous description of research phenomena, so the accuracy of estimates is linked directly to the adequacy of discrete representation of random oscillations. This approach is similar a concept of transforming differential equations to difference ones with derivative approximation by corresponding finite differences. The resulting discrete model, being an approximation, features a methodical error which can be decreased but never eliminated. To render such a presentation more accurate it is imperative to decrease the discretization interval and to increase realization size growing requirements for computing power. The spectral density and covariance function estimates comprise a non-parametric (non-formal) approach. In principle, any non-formal approach is a kind of art i.e. the results depend on the performer’s skills. Due to interference of subjective factors in spectral or covariance estimates of random signals, accuracy of results cannot be properly determined or justified. To avoid the abovementioned difficulties, the application of linear time-series models with well-developed procedures for parameter estimates is more advantageous. A method for the analysis of random oscillations using a parametric model corresponding discretely (no approximation error) with a linear elastic system is developed and presented in this book. As a result, a one-to-one transformation of the model’s numerical factors to logarithmic decrement and natural frequency of random oscillations is established. It allowed to develop a formal processing procedure from experimental data to obtain the estimates of δ and ω0. The proposed approach allows researchers to replace traditional subjective techniques by a formal processing procedure providing efficient estimates with analytically defined statistical uncertainties.

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.

Mastering Kibana 6.x is your guide to leveraging Kibana for creating impactful data visualizations and insightful dashboards. From setting up basic visualizations to exploring advanced analytics and machine learning integrations, this book equips you with the necessary skills to dive deep into your data and gain actionable insights at scale. You'll also learn to effectively manage and monitor data with powerful tools such as X-Pack and Beats. What this Book will help me do Build sophisticated dashboards to visualize elastic stack data effectively. Understand and utilize Timelion expressions for analyzing time series data. Incorporate X-Pack capabilities to enhance security and monitoring in Kibana. Extract, analyze, and visualize data from Elasticsearch for advanced analytics. Set up monitoring and alerting using Beats components for reliable data operations. Author(s) With extensive experience in big data technologies, the author brings a practical approach to teaching advanced Kibana topics. Having worked on real-world data analytics projects, their aim is to make complex concepts accessible while showing how to tackle analytics challenges using Kibana. Who is it for? This book is ideal for data engineers, DevOps professionals, and data scientists who want to optimize large-scale data visualizations. If you're looking to manage Elasticsearch data through insightful dashboards and visual analytics, or enhance your data operations with features like machine learning, then this book is perfect for you. A basic understanding of the Elastic Stack is helpful, though not required.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP . Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. This book is part of the SAS Press program.

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. is the solution. R for Everyone, Second Edition, Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you'll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R's facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

Learning Kibana 5.0 is your gateway to mastering the art of data visualization using the powerful features of the Kibana platform. This book guides you through the process of creating stunning interactive dashboards and making data-driven insights accessible with real-time visualizations. Whether you're new to the Elastic stack or seeking to refine your expertise, this book equips you to harness Kibana's full potential. What this Book will help me do Build robust, real-time dashboards in Kibana to visualize complex datasets efficiently. Leverage Timelion to perform time-series data analysis and create metrics-based dashboards. Explore advanced analytics using the Graph plugin to uncover relationships and correlations in data. Learn how to create and deploy custom plugins to tailor Kibana to specific project needs. Understand how to use the Elastic stack to monitor, analyze, and optimize various types of data flows. Author(s) Bahaaldine Azarmi is a seasoned expert in the Elastic stack, known for his dedication to making complex technical topics approachable and practical. With years of experience in data analytics and software development, Bahaaldine shares not only his technical expertise but also his passion for helping professionals achieve their goals through clear, actionable guidance. His writing emphasizes hands-on learning and practical application. Who is it for? This book is perfect for developers, data visualization engineers, and data scientists who aim to hone their skills in data visualization and interactive dashboard development. It assumes a basic understanding of Elasticsearch and Logstash to maximize its practicality. If you aim to advance your career by learning how to optimize data architecture and solve real-world problems using the Elastic stack, this book is ideal for you.

Dive into "Kibana Essentials" and discover how to efficiently analyze data and create visually engaging visualizations and dashboards with Kibana. Whether you are new to Kibana or looking to enhance your skills, this book provides practical guidance to help you apply Kibana features to real-world scenarios. By the end, you'll have the skills to create and apply dashboards that run on Elasticsearch. What this Book will help me do Understand the core features and setup process of Kibana on both Windows and Ubuntu platforms. Master the Discover, Visualize, Dashboard, and Settings functionalities in Kibana. Utilize Elasticsearch's search capabilities to analyze data in Kibana. Create, customize, and share stunning visualizations and dashboards for various use cases. Gain advanced knowledge to tweak Kibana settings for optimized workflows. Author(s) None Gupta is an experienced author and data professional who has worked extensively with Kibana and Elasticsearch technologies. With a passion for simplifying complex concepts, None specializes in breaking down technical topics into digestible, actionable steps. Their practical approach ensures that learners can confidently apply knowledge immediately after reading. Who is it for? This book is for professionals or enthusiasts aiming to delve into data visualization using Kibana. Whether starting from scratch or familiar with similar tools, readers will find the foundational to advanced techniques invaluable. It's especially suited for those who want a practical, hands-on approach to mastering Kibana.

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ 1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES • Exploring R, RStudio, and R packages • Using R for math: variable types, vectors, calling functions, and more • Exploiting data structures, including data.frames, matrices, and lists • Creating attractive, intuitive statistical graphics • Writing user-defined functions • Controlling program flow with if, ifelse, and complex checks • Improving program efficiency with group manipulations • Combining and reshaping multiple datasets • Manipulating strings using R’s facilities and regular expressions • Creating normal, binomial, and Poisson probability distributions • Programming basic statistics: mean, standard deviation, and t-tests • Building linear, generalized linear, and nonlinear models • Assessing the quality of models and variable selection • Preventing overfitting, using the Elastic Net and Bayesian methods • Analyzing univariate and multivariate time series data • Grouping data via K-means and hierarchical clustering • Preparing reports, slideshows, and web pages with knitr • Building reusable R packages with devtools and Rcpp • Getting involved with the R global community

The ability of a structural assembly to carry loads and forces determines how stable it will be over time. Viewing structural assemblages as comprising columns, beams, arches, rings, and plates, this book will introduce the student to both a classical and advanced understanding of the mechanical behavior of such structural systems under load and how modeling the resulting strains can predict the overall future performance—the stability—of that structure. While covering traditional beam theory, the book is more focused on elastica theory in keeping with modern approaches. This text will be an expanded and updated version a similar, previously published book, but with pedagogical improvements and updated analytical methods. This engineering textbook will provide a focused treatment on the study of how structures behave and perform when under stress loading, including plastic deformation and buckling. All advanced engineering students studying engineering mechanics, structural analysis and design, fatigue and failure, and other related subjects need to have this knowledge, and this book will provide it in a thorough and coherent fashion. Written by two of the world’s leading engineering professors in this subject area, the pedagogy has been classroom-tested over many years and should find a receptive readership among both students and instructors. * An understandable introduction to the theory of structural stability, useful for a wide variety of engineering disciplines, including mechanical, civil and aerospace engineering * Covers both static and dynamic loads, for both conservative and nonconservative systems * Emphasizes elastic behavior under loads, including vertical buckling, torsional buckling and nonlinear affects of structural system buckling and stability * Case examples to illustrate real-world applications of Stability Theory