Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you'll learn: How to write code for machine learning, adaptive control and estimation using MATLAB How these three areas complement each other How these three areas are needed for robust machine learning applications How to use MATLAB graphics and visualization tools for machine learning How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

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.

Beyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts using R and RStudio. About the Technology Spreadsheets are powerful tools for many tasks, but if you need to interpret, interrogate, and present data, they can feel like the wrong tools for the task. That’s when R programming is the way to go. The R programming language provides a comfortable environment to properly handle all types of data. And within the open source RStudio development suite, you have at your fingertips easy-to-use ways to simplify complex manipulations and create reproducible processes for analysis and reporting. About the Book With Beyond Spreadsheets with R you’ll learn how to go from raw data to meaningful insights using R and RStudio. Each carefully crafted chapter covers a unique way to wrangle data, from understanding individual values to interacting with complex collections of data, including data you scrape from the web. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts. What's Inside How to start programming with R and RStudio Understanding and implementing important R structures and operators Installing and working with R packages Tidying, refining, and plotting your data About the Reader If you’re comfortable writing formulas in Excel, you’re ready for this book. About the Author Dr Jonathan Carroll is a data science consultant providing R programming services. He holds a PhD in theoretical physics. We interviewed Jonathan as a part of our Six Questions series. Check it out here. Quotes A useful guide to facilitate graduating from spreadsheets to more serious data wrangling with R. - John D. Lewis, DDN An excellent book to help you understand how stored data can be used. - Hilde Van Gysel, Trebol Engineering A great introduction to a data science programming language. Makes you want to learn more! - Jenice Tom, CVS Health Handy to have when your data spreads beyond a spreadsheet. - Danil Mironov, Luxoft Poland

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.

Dive into the intricacies of data science with 'Principles of Data Science'. This book takes you on a journey to explore, analyze, and transform data into actionable insights using mathematical models, Python programming, and machine learning concepts. With a clear and engaging style, you will progress from understanding theoretical foundations to implementing advanced techniques in real-world scenarios. What this Book will help me do Master the five critical steps in a practical data science workflow. Clean and prepare raw datasets for accurate machine learning models. Understand and apply statistical models and mathematical principles for data analysis. Build and evaluate predictive models using Python and effective metrics. Create impactful visualizations that clearly convey data insights. Author(s) Sinan Ozdemir is an expert in data science, with a background in developing and teaching advanced courses in machine learning and predictive analytics. With co-authors None Kakade and None Tibaldeschi, they bring years of hands-on experience in data science to this comprehensive guide. Their approach simplifies complex concepts, making them accessible without sacrificing depth, to empower readers to make data-driven decisions confidently. Who is it for? This book is ideal for aspiring data scientists seeking a practical introduction to the field. It's perfect for those with basic math skills looking to apply them to data science or experienced programmers who want to explore the mathematical foundation of data science. A basic understanding of Python programming will be invaluable, but the book builds up core concepts step-by-step, making it accessible to both beginners and experienced professionals.

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.

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.

Design, develop, and master efficient Power BI solutions for impactful business insights Key Features Get to grips with the fundamentals of Microsoft Power BI Combine data from multiple sources, create visuals, and publish reports across platforms Understand Power BI concepts with real-world use cases Book Description Microsoft Power BI Complete Reference Guide gets you started with business intelligence by showing you how to install the Power BI toolset, design effective data models, and build basic dashboards and visualizations that make your data come to life. In this Learning Path, you will learn to create powerful interactive reports by visualizing your data and learn visualization styles, tips and tricks to bring your data to life. You will be able to administer your organization's Power BI environment to create and share dashboards. You will also be able to streamline deployment by implementing security and regular data refreshes. Next, you will delve deeper into the nuances of Power BI and handling projects. You will get acquainted with planning a Power BI project, development, and distribution of content, and deployment. You will learn to connect and extract data from various sources to create robust datasets, reports, and dashboards. Additionally, you will learn how to format reports and apply custom visuals, animation and analytics to further refine your data. By the end of this Learning Path, you will learn to implement the various Power BI tools such as on-premises gateway together along with staging and securely distributing content via apps. This Learning Path includes content from the following Packt products: Microsoft Power BI Quick Start Guide by Devin Knight et al. Mastering Microsoft Power BI by Brett Powell What you will learn Connect to data sources using both import and DirectQuery options Leverage built-in and custom visuals to design effective reports Administer a Power BI cloud tenant for your organization Deploy your Power BI Desktop files into the Power BI Report Server Build efficient data retrieval and transformation processes Who this book is for Microsoft Power BI Complete Reference Guide is for those who want to learn and use the Power BI features to extract maximum information and make intelligent decisions that boost their business. If you have a basic understanding of BI concepts and want to learn how to apply them using Microsoft Power BI, then Learning Path is for you. It consists of real-world examples on Power BI and goes deep into the technical issues, covers additional protocols, and much more.

This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

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.

Must-have study guide for the SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling exam! Written for both new and experienced SAS programmers, the SAS® Certification Prep Guide: Statistical Business Analysis Using SAS®9 is an in-depth prep guide for the SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling exam. The authors step through identifying the business question, generating results with SAS, and interpreting the output in a business context. The case study approach uses both real and simulated data to master the content of the certification exam. Each chapter also includes a quiz aimed at testing the reader’s comprehension of the material presented. Major topics include: ANOVA Linear Regression Logistic Regression Inputs for Predictive Modeling Model Performance For those new to statistical topics or those needing a review of statistical foundations, this book also serves as an excellent reference guide for understanding descriptive and inferential statistics. This book is part of the SAS Press program.

Get a clear picture of the salaries and bonuses data science professionals around the world receive, as well as the tools and cloud providers they use, the tasks they perform, and how interpersonal ("soft") skills might affect their pay. The fifth edition of O’Reilly’s online Data Science Salary Survey provides complete results from nearly 800 participants from 69 different countries, 42 different US states, and Washington, DC. With five years of data, the survey’s results are consistent enough to reliably identify changes and trends. The survey asked specific questions about industry, team, and company size, but also posed questions such as, "How easy is it to move to another position?" or "What is your next career step?" You can plug in your own data points to the survey model and see how you compare to other data science professionals in your industry. With this report, you’ll learn: Where data scientists make the highest salaries—by country and by US state Tools that respondents most commonly use on the job, and tools that contribute most to salary Activities that contribute to higher earnings How gender and bargaining skills affect salaries when all other factors are equal Salary differences between those using open source tools vs those using proprietary tools How the increase in respondents outside of the US signal a rise in international companies starting and growing data organizations Participate in the 2018 Survey: Spend just 5 to 10 minutes and take the anonymous salary survey here: https://www.oreilly.com/ideas/take-the-​data-science-salary-survey.

This is a practical, user-friendly guide to the identification and assessment of indoor air contaminants that contribute to buildingrelated illness in commercial buildings, institutions, and residences. The third edition covers basic concepts and details various approaches and up-to-date analytical methods.

This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book is part of the SAS Press program. Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models

"Bioinformatics with Python Cookbook" offers a detailed exploration into the modern approaches to computational biology using the Python programming language. Through hands-on recipes, you will master the practical applications of bioinformatics, enabling you to analyze vast biological data effectively using Python libraries and tools. What this Book will help me do Master processing and analyzing genomic datasets in Python to enable accurate bioinformatics discoveries. Understand and apply next-generation sequencing techniques for advanced biological research. Learn to utilize machine learning approaches such as PCA and decision trees for insightful data analysis in biology. Gain proficiency in using high-performance computing frameworks like Dask and Spark for scalable bioinformatics workflows. Develop capabilities to visually represent biological data interactions and insights for presentation and analysis. Author(s) Tiago Antao is a computational scientist specializing in bioinformatics with extensive experience in Python programming applied to biological sciences. He has worked on numerous bioinformatics projects and has a special interest in using Python to bridge biology and data science. Tiago's approachable writing style ensures that both newcomers and experts benefit from his insights. Who is it for? This book is designed for bioinformatics professionals, researchers, and data scientists who are eager to harness the power of Python programming for their biological data analysis needs. If you are familiar with Python and are looking to tackle intermediate to advanced bioinformatics challenges using practical recipes, this book is ideal for you. It is suitable for those seeking to expand their knowledge in computational biology and data visualization techniques. Whether you are working on next-generation sequencing or population genetics, this resource will guide you effectively.

Dive into "Hands-On Data Science with R" and embark on a journey to master the R language for practical data science applications. This comprehensive guide walks through data manipulation, visualization, and advanced analytics, preparing you to tackle real-world data challenges with confidence. What this Book will help me do Understand how to utilize popular R packages effectively for data science tasks. Learn techniques for cleaning, preprocessing, and exploring datasets. Gain insights into implementing machine learning models in R for predictive analytics. Master the use of advanced visualization tools to extract and communicate insights. Develop expertise in integrating R with big data platforms like Hadoop and Spark. Author(s) This book was written by experts in data science and R including Doug Ortiz and his co-authors. They bring years of industry experience and a desire to teach, presenting complex topics in an approachable manner. Who is it for? Designed for data analysts, statisticians, or programmers with basic R knowledge looking to dive into machine learning and predictive analytics. If you're aiming to enhance your skill set or gain confidence in tackling real-world data problems, this book is an excellent choice.

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.

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

This textbook reviews the methodologies of reliability prediction as currently used in industries such as electronics, automotive, aircraft, aerospace, off-highway, farm machinery, and others. It then discusses why these are not successful; and, presents methods developed by the authors for obtaining accurate information for successful prediction. The approach is founded on approaches that accurately duplicate the real world use of the product. Their approach is based on two fundamental components needed for successful reliability prediction; first, the methodology necessary; and, second, use of accelerated reliability and durability testing as a source of the necessary data. Applicable to all areas of engineering, this textbook details the newest techniques and tools to achieve successful reliabilityprediction and testing. It demonstrates practical examples of the implementation of the approaches described. This book is a tool for engineers, managers, researchers, in industry, teachers, and students. The reader will learn the importance of the interactions of the influencing factors and the interconnections of safety and human factors in product prediction and testing.

The Foundational Hands-On Skills You Need to Dive into Data Science “Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.” –From the foreword by Jared Lander, series editor Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Programming Skills for Data Science Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you've uncovered. Step by step, you'll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything's focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to Install your complete data science environment, including R and RStudio Manage projects efficiently, from version tracking to documentation Host, manage, and collaborate on data science projects with GitHub Master R language fundamentals: syntax, programming concepts, and data structures Load, format, explore, and restructure data for successful analysis Interact with databases and web APIs Master key principles for visualizing data accurately and intuitively Produce engaging, interactive visualizations with ggplot and other R packages Transform analyses into sharable documents and sites with R Markdown Create interactive web data science applications with Shiny Collaborate smoothly as part of a data science team Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Architect and deploy a Power BI solution. This book will help you understand the many available options and choose the best combination for hosting, developing, sharing, and deploying a Power BI solution within your organization. Pro Power BI Architecture provides detailed examples and explains the different methods available for sharing and securing Power BI content so that only intended recipients can see it. Commonly encountered problems you will learn to handle include content unexpectedly changing while users are in the process of creating reports and building analysis, methods of sharing analyses that don’t cover all the requirements of your business or organization, and inconsistent security models. The knowledge provided in this book will allow you to choose an architecture and deployment model that suits the needs of your organization, ensuring that you do not spend your time maintaining your solution but onusing it for its intended purpose and gaining business value from mining and analyzing your organization’s data. What You'll Learn Architect and administer enterprise-level Power BI solutions Choose the right sharing method for your Power BI solution Create and manage environments for development, testing, and production Implement row level security in multiple ways to secure your data Save money by choosing the right licensing plan Select a suitable connection type—Live Connection, DirectQuery, or Scheduled Refresh—for your use case Set up a Power BI gateway to bridge between on-premises data sources and the Power BI cloud service Who This Book Is For Data analysts, developers, architects, and managers who want to leverage Power BI for their reporting solution