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.

Whether you’re a marketer with development skills or a full-on web developer/analyst, Practical Google Analytics and Google Tag Manager for Developers shows you how to implement Google Analytics using Google Tag Manager to jumpstart your web analytics measurement. There’s a reason that so many organizations use Google Analytics. Effective collection of data with Google Analytics can reduce customer acquisition costs, provide priceless feedback on new product initiatives, and offer insights that will grow a customer or client base. So where does Google Tag Manager fit in? Google Tag Manager allows for unprecedented collaboration between marketing and technical teams, lightning fast updates to your site, and standardization of the most common tags for on-site tracking and marketing efforts. To achieve the rich data you're really after to better serve your users’ needs, you'll need the tools Google Tag Manager provides for a best-in-class implementation of Google Analytics measurement on your site. Written by data evangelist and Google Analytics expert Jonathan Weber and the team at LunaMetrics, this book offers foundational knowledge, a collection of practical Google Tag Manager recipes, well-tested best practices, and troubleshooting tips to get your implementation in tip-top condition. It covers topics including: • Google Analytics implementation via Google Tag Manager • How to customize Google Analytics for your unique situation • Using Google Tag Manager to track and analyze interactions across multiple devices and touch points • How to extract data from Google Analytics and use Google BigQuery to analyze Big Data questions

More than ever, data drives decisions in organizations—and we have more data, and more ways to analyze it, than ever. Yet strategic initiatives continue to fail as often as they did when computers ran on punch cards. Economist and research scientist Alec Levenson says we need a new approach. The problem, Levenson says, is that the business people who devise the strategies and the human resources people who get employees to implement them use completely different analytics. Business analytics can determine if operational priorities aren't being achieved but can't explain why. HR analytics reveal potentially helpful policy and process improvements but can't identify which would have the greatest strategic impact. This book shows how to use an integrated approach to bring these two pieces together. Levenson presents a thorough and realistic treatment of the reasons for and challenges of taking an integrated approach. He provides details on the different parts of both enterprise and human capital analytics that have to be conducted for integration to be successful and includes specific questions to ask, along with examples of applying integrated analytics to address particular organizational challenges. Effective analytics is a team sport. Levenson's approach allows you to get the deepest insights by bringing people together from both the business and HR perspectives to assess what's going on and determine the right solution.

Learn or refresh core statistical methods for business with SAS® and approach real business analytics issues and techniques using a practical approach that avoids complex mathematics and instead employs easy-to-follow explanations.

Business Statistics Made Easy in SAS® is designed as a user-friendly, practice-oriented, introductory text to teach businesspeople, students, and others core statistical concepts and applications. It begins with absolute core principles and takes you through an overview of statistics, data and data collection, an introduction to SAS®, and basic statistics (descriptive statistics and basic associational statistics). The book also provides an overview of statistical modeling, effect size, statistical significance and power testing, basics of linear regression, introduction to comparison of means, basics of chi-square tests for categories, extrapolating statistics to business outcomes, and some topical issues in statistics, such as big data, simulation, machine learning, and data warehousing.

The book steers away from complex mathematical-based explanations, and it also avoids basing explanations on the traditional build-up of distributions, probability theory and the like, which tend to lose the practice-oriented reader. Instead, it teaches the core ideas of statistics through methods such as careful, intuitive written explanations, easy-to-follow diagrams, step-by-step technique implementation, and interesting metaphors.

With no previous SAS experience necessary, Business Statistics Made Easy in SAS® is an ideal introduction for beginners. It is suitable for introductory undergraduate classes, postgraduate courses such as MBA refresher classes, and for the business practitioner. It is compatible with SAS® University Edition.

The 9th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2015) was held on September 18-20, 2015, Shanghai, China. This proceedings volume assembles papers from various professionals engaged in the fields of Biomedical Engineering, Bioinformatics and Computational Biology.

It’s much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet. This introductory guide shows you how to use the R language to create a variety of useful graphs for visualizing and analyzing complex data for science, business, media, and many other fields. You’ll learn methods for highlighting important relationships and trends, reducing data to simpler forms, and emphasizing key numbers at a glance. Anyone who wants to analyze data will find something useful here—even if you don’t have a background in mathematics, statistics, or computer programming. If you want to examine data related to your work, this book is the ideal way to start.

Dive into the world of Bayesian Machine Learning with "Learning Bayesian Models with R." This comprehensive guide introduces the foundations of probability theory and Bayesian inference, teaches you how to implement these concepts with the R programming language, and progresses to practical techniques for supervised and unsupervised problems in data science. What this Book will help me do Understand and set up an R environment for Bayesian modeling Build Bayesian models including linear regression and classification for predictive analysis Learn to apply Bayesian inference to real-world machine learning problems Work with big data and high-performance computation frameworks like Hadoop and Spark Master advanced Bayesian techniques and apply them to deep learning and AI challenges Author(s) Hari Manassery Koduvely is a proficient data scientist with extensive experience in leveraging Bayesian frameworks for real-world applications. His passion for Bayesian Machine Learning is evident in his approachable and detailed teaching methodology, aimed at making these complex topics accessible for practitioners. Who is it for? This book is best suited for data scientists, analysts, and statisticians familiar with R and basic probability theory who aim to enhance their expertise in Bayesian approaches. It's ideal for professionals tackling machine learning challenges in applied data contexts. If you're looking to incorporate advanced probabilistic methods into your projects, this guide will show you how.

Practical Graph Analytics with Apache Giraph helps you build data mining and machine learning applications using the Apache Foundation’s Giraph framework for graph processing. This is the same framework as used by Facebook, Google, and other social media analytics operations to derive business value from vast amounts of interconnected data points. Graphs arise in a wealth of data scenarios and describe the connections that are naturally formed in both digital and real worlds. Examples of such connections abound in online social networks such as Facebook and Twitter, among users who rate movies from services like Netflix and Amazon Prime, and are useful even in the context of biological networks for scientific research. Whether in the context of business or science, viewing data as connected adds value by increasing the amount of information available to be drawn from that data and put to use in generating new revenue or scientific opportunities. Apache Giraph offers a simple yet flexible programming model targeted to graph algorithms and designed to scale easily to accommodate massive amounts of data. Originally developed at Yahoo!, Giraph is now a top top-level project at the Apache Foundation, and it enlists contributors from companies such as Facebook, LinkedIn, and Twitter. Practical Graph Analytics with Apache Giraph brings the power of Apache Giraph to you, showing how to harness the power of graph processing for your own data by building sophisticated graph analytics applications using the very same framework that is relied upon by some of the largest players in the industry today.

Explore the possibilities of web scraping using Python with this practical guide. The book provides a comprehensive introduction to extracting information from web pages, managing complex scraping scenarios, and utilizing specialized tools such as Scrapy. Whether you're dealing with static pages or interactive web content, this book equips you with the skills to gather and process web data efficiently. What this Book will help me do Gain proficiency in writing Python scripts to extract data from web pages. Learn to build and manage multithreaded crawlers to handle large-scale scraping tasks. Master techniques for interacting with dynamic web content and JavaScript-rendered pages. Understand how to work with web forms, sessions, and tackle challenges like CAPTCHA. Implement practical examples of web scraping using Scrapy for real-world data projects. Author(s) Richard Penman is an experienced software engineer and an expert in Python programming and web development. With years of practical expertise in web crawling and data extraction, Richard shares his extensive knowledge in this field to make complex tasks accessible to developers of all levels. His thoughtful approach aims to empower readers to confidently tackle data challenges on the web. Who is it for? This book is ideal for developers and technical professionals who want to learn effective techniques for web scraping with Python. A basic understanding of programming concepts and experience with Python will help readers get the most out of the practical examples. It's also suitable for advanced learners looking to apply Python skills for automating web data extraction tasks. If you're enthusiastic about turning web data into actionable insights, this guide is for you.

Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you’ll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you’ll find out how far data science reaches. With this anthology, you’ll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries.

Explore the world of QlikView dashboards with this comprehensive guide that walks you through the entire process of creating effective and visually engaging dashboards for your business needs. From identifying KPIs to rolling out your application, this book provides actionable steps and best practices for delivering data-driven results. What this Book will help me do Define key performance indicators (KPIs) based on business objectives and goals. Design and structure dashboards using best practices in data visualization. Master creating various chart types, including bar, line, pie charts, and advanced visualizations, like heat maps. Integrate data from multiple sources, such as ERP systems and spreadsheets, into a cohesive dashboard. Learn the steps to develop mobile-optimized dashboards for accessibility on the go. Author(s) None Villafuerte, a seasoned expert in data visualization and QlikView development, brings a wealth of experience to this book. With years of hands-on work creating impactful dashboards for various business needs, the author's pragmatic and result-oriented approach provides readers with practical and insightful knowledge. Who is it for? The book is tailored for QlikView developers who already possess a basic understanding of scripting and dashboard layout design. It's ideal for professionals aiming to enhance their design and visualization skills. Additionally, business analysts or managers with a technical inclination could also benefit from its comprehensive approach to creating interactive dashboards. If building effective and appealing dashboards that drive business impact is your goal, this book is for you.

This book is issued from a 30 years’ experience on the presentation of variational methods to successive generations of students and researchers in Engineering. It gives a comprehensive, pedagogical and engineer-oriented presentation of the foundations of variational methods and of their use in numerical problems of Engineering. Particular applications to linear and nonlinear systems of equations, differential equations, optimization and control are presented. MATLAB programs illustrate the implementation and make the book suitable as a textbook and for self-study. The evolution of knowledge, of the engineering studies and of the society in general has led to a change of focus from students and researchers. New generations of students and researchers do not have the same relations to mathematics as the previous ones. In the particular case of variational methods, the presentations used in the past are not adapted to the previous knowledge, the language and the centers of interest of the new generations. Since these methods remain a core knowledge – thus essential - in many fields (Physics, Engineering, Applied Mathematics, Economics, Image analysis …), a new presentation is necessary in order to address variational methods to the actual context.

Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3. R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research.

Have you ever wanted to transform your data analysis in R into interactive, web-based dashboards and applications? "Learning Shiny" is your guide to mastering R's Shiny framework to create dynamic, visual, and engaging web applications. With its step-by-step approach, this book enables you to harness Shiny's features effectively. What this Book will help me do Understand the core principles of R and data processing using tools like apply and lapply, empowering you to handle data programmatically. Learn the Shiny framework fundamentals, including structuring an interactive application using UI and server scripts. Create stunning visualizations and dashboards using libraries like ggplot2 and integrate Shiny seamlessly. Deploy and host Shiny web applications on Linux servers for effective sharing and collaboration. Enhance your applications with JavaScript integrations, using tools like D3.js, for advanced customization. Author(s) Hernan Resnizky is a renowned data scientist and educator with extensive experience in R programming and Shiny application development. Known for his clear teaching style, he has guided numerous professionals in using R for real-world applications. His practical approach ensures readers not only learn techniques but understand how to apply them effectively. Who is it for? "Learning Shiny" is ideal for data scientists looking to showcase their work through interactive web apps and visualizations, and for web developers curious about leveraging the Shiny framework in R. Beginners as well as those with some R experience will find tailored guidance to suit their level. If you aim to expand your toolkit with web-focused R capabilities, this book is for you.

Preparing and cleaning data is notoriously expensive, prone to error, and time consuming: the process accounts for roughly 80% of the total time spent on analysis. As this O’Reilly report points out, enterprises have already invested billions of dollars in big data analytics, so there’s great incentive to modernize methods for cleaning, combining, and transforming data. Author Federico Castanedo, Chief Data Scientist at WiseAthena.com, details best practices for reducing the time it takes to convert raw data into actionable insights. With these tools and techniques in mind, your organization will be well positioned to translate big data into big decisions. Explore the problems organizations face today with traditional prep and integration Define the business questions you want to address before selecting, prepping, and analyzing data Learn new methods for preparing raw data, including date-time and string data Understand how some cleaning actions (like replacing missing values) affect your analysis Examine data curation products: modern approaches that scale Consider your business audience when choosing ways to deliver your analysis

The rise of big data in recent years coincides with the economic and political rise of Asia, especially among the five countries that make up the bulk of the East Asian Internet-using population: China, Japan, Korea, India, and Indonesia. If you’re thinking of entering the Asian market, this O’Reilly report provides an overview of the current state of big data and open data in these countries, and helps you examine whether the benefits of doing business with them outweigh the costs. While Japan and South Korea are highly developed countries with lofty Internet penetration rates, China, India, and Indonesia have enormous populations, relatively low Internet penetration, and enormous growth potential. But access to open data from fields such as healthcare, education, agriculture, transportation, energy, and finance—data vital for building businesses and services—varies from country to country. Each of them has a distinctive character reflecting its national priorities. To help you assess risk vs opportunity in the Asian market, author Franklin Lu reviews these five countries individually to reveal the nature of data privacy laws, open data initiatives, and existing businesses.

This guide presents sophisticated SAS programming techniques, procedures, and tools, such as Proc SQL, hash tables, and SAS Macro programming, for any industry. It empowers both advanced programmers who need a quick refresher and programmers interested in learning new techniques. It shows how to take advantage of the latest SAS options and new SAS procedures. The book illustrates syntax with simple, common task-oriented examples and prepares readers for the advanced SAS certification exam. Mindmaps and process flowcharts are available on the author's website.

The book takes a hands-on approach to developing dashboards, from instructing users on advanced Excel techniques to addressing dashboard pitfalls common in the real world. Dashboards for Excel is your key to creating informative, actionable, and interactive dashboards and decision support systems. Throughout the book, the reader is challenged to think about Excel and data analytics differently—that is, to think outside the cell. This book shows you how to create dashboards in Excel quickly and effectively. In this book, you learn how to: Apply data visualization principles for more effective dashboards Employ dynamic charts and tables to create dashboards that are constantly up-to-date and providing fresh information Use understated yet powerful formulas for Excel development Apply advanced Excel techniques mixing formulas and Visual Basic for Applications (VBA) to create interactive dashboards Create dynamic systems for decision support in your organization Avoid common problems in Excel development and dashboard creation Get started with the Excel data model, PowerPivot, and Power Query

Unlock the full potential of the R programming language with 'Mastering Data Analysis with R'. This book takes you from basic data manipulation to advanced visualization and modeling techniques, providing hands-on guidance to solve real-world data science challenges. What this Book will help me do Efficiently manipulate and clean large datasets using R techniques. Build and evaluate statistical models and machine learning algorithms. Visualize data insights through compelling graphics and visualizations. Analyze social networks and graph data within R's environment. Perform geospatial data analysis with specialized R packages. Author(s) None Daróczi is a seasoned data scientist and R developer with extensive industry and academic experience. He specializes in employing R for sophisticated data analysis tasks and visualization. His approachable writing style, combined with in-depth technical expertise, ensures learners of varying levels can connect with and benefit from his materials. Who is it for? This book is ideal for data scientists, statisticians, and analysts who are familiar with basics of R and want to deepen their expertise. If you are looking to learn practical applications of advanced R capabilities for data wrangling, modeling, and visualization, this is for you. It suits professionals aiming to implement data-driven solutions and empowers them to make informed decisions with R's tools. Find practical techniques to elevate your data analysis proficiency here.

Dive into building recommendation systems with R in this comprehensive guide. You will learn about data mining, machine learning, and how R's powerful libraries and tools can be utilized to create efficient and optimized recommendation engines. By the end of this book, you will have the expertise to develop custom solutions tailored to specific data and user cases. What this Book will help me do Master the foundations of recommendation systems and their applications. Understand and implement essential data preprocessing techniques. Learn to optimize recommendation algorithms for better efficiency. Explore the use of the recommenderlab package in R for building models. Gain hands-on experience through a complete case study building a recommendation engine. Author(s) None Usuelli is a seasoned data scientist and R programming enthusiast passionate about machine learning and data analysis. They have extensive experience in developing recommendation systems for various industries, leveraging the power of R for robust solutions. None's clear teaching approach makes complex concepts accessible to learners of all levels. Who is it for? This book is ideal for developers who already possess a fundamental understanding of R and basic machine learning principles. If you aim to deepen your knowledge in creating advanced recommendation systems and practically apply these concepts, this book is the perfect resource for you. It is an excellent guide for professionals looking to specialize in predictive analytics and systems design.

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis. Introduces data analysis techniques and cloud computing concepts Describes cloud-based models and systems for Big Data analytics Provides examples of the state-of-the-art in cloud data analysis Explains how to develop large-scale data mining applications on clouds Outlines the main research trends in the area of scalable Big Data analysis

For the third consecutive year, O’Reilly Media conducted an anonymous survey to expose the tools that successful data scientists and engineers use, and how those tool choices might relate to their salary. For the 2015 version of the Data Science Salary Survey, we heard from over 600 respondents who work in and around the data space for a variety of industries across 47 countries and 38 U.S. states. The research was based on data collected through an online 32-question survey, including demographic information, time spent on various data-related tasks, and the use or non-use of 116 software tools. Findings include: Download this free in-depth report to gain insight from these potentially career-changing findings, and plug your own variables into one of the linear models to predict your own salary. Average number of tools and median income for all respondents Distribution of responses by a variety of factors, including age, gender, location, industry, role, and cloud computing Detailed analysis of tool use, including tool clusters Correlation of tool usage and salary The survey is now open for the 2016 report, and it takes just 5 to 10 minutes to complete: http://www.oreilly.com/go/ds-salary-​survey-2016.

This book introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The book covers the foundational motivations for this new approach, the basic theory behind its calibration properties, many important applications, and new directions for research. It explores a new way of thinking compared to existing schools of thought on statistical inference and encourages readers to think carefully about the correct approach to scientific inference.

In Beginning Big Data with Power BI and Excel 2013, you will learn to solve business problems by tapping the power of Microsoft’s Excel and Power BI to import data from NoSQL and SQL databases and other sources, create relational data models, and analyze business problems through sophisticated dashboards and data-driven maps. While Beginning Big Data with Power BI and Excel 2013 covers prominent tools such as Hadoop and the NoSQL databases, it recognizes that most small and medium-sized businesses don’t have the Big Data processing needs of a Netflix, Target, or Facebook. Instead, it shows how to import data and use the self-service analytics available in Excel with Power BI. As you’ll see through the book’s numerous case examples, these tools—which you already know how to use—can perform many of the same functions as the higher-end Apache tools many people believe are required to carry out in Big Data projects. Through instruction, insight, advice, and case studies, Beginning Big Data with Power BI and Excel 2013 will show you how to: Import and mash up data from web pages, SQL and NoSQL databases, the Azure Marketplace and other sources. Tap into the analytical power of PivotTables and PivotCharts and develop relational data models to track trends and make predictions based on a wide range of data. Understand basic statistics and use Excel with PowerBI to do sophisticated statistical analysis—including identifying trends and correlations. Use SQL within Excel to do sophisticated queries across multiple tables, including NoSQL databases. Create complex formulas to solve real-world business problems using Data Analysis Expressions (DAX).

An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.