"Learning Tableau 2025" provides a comprehensive guide to mastering Tableau's latest features, including advanced AI capabilities like Tableau Pulse and Agent. This book, authored by Tableau expert Joshua N. Milligan, will equip you with the tools to transform complex data into actionable insights and interactive dashboards. What this Book will help me do Learn to use Tableau's advanced AI features, including Tableau Agent and Pulse, to streamline data analysis and automate insights. Develop skills to create and customize dynamic dashboards tailored to interactive data storytelling. Understand and utilize new geospatial functions within Tableau for advanced mapping and analytics. Master Tableau Prep's enhanced data preparation capabilities for efficient data modeling and structuring. Learn to effectively integrate and analyze data from multiple sources, enhancing your ability to extract meaningful insights. Author(s) Joshua N. Milligan, a Tableau Zen Master and Visionary, has years of experience in the field of data visualization and analytics. With a hands-on approach, Joshua combines his expertise and passion for Tableau to make complex topics accessible and engaging. His teaching method ensures that readers gain practical, actionable knowledge. Who is it for? This book is ideal for aspiring business intelligence developers, data analysts, data scientists, and professionals seeking to enhance their data visualization skills. It's suitable for both beginners looking to get started with Tableau and experienced users eager to explore its new features. A Tableau license or access to a 14-day trial is recommended.

Use this practical guide to learn how to use people analytics, HR data and generative AI to add value to the business.

As organizations increasingly leverage Microsoft Fabric to unify their data engineering, analytics, and governance strategies, the role of the Fabric Analytics Engineer has become more crucial than ever. This book equips readers with the knowledge and hands-on skills required to excel in this domain and pass the DP-600 certification exam confidently. This book covers the entire certification syllabus with clarity and depth, beginning with an overview of Microsoft Fabric. You will gain an understanding of the platform’s architecture and how it integrates with data and AI workloads to provide a unified analytics solution. You will then delve into implementing a data warehouse in Microsoft Fabric, exploring techniques to ingest, transform, and store data efficiently. Next, you will learn how to work with semantic models in Microsoft Fabric, enabling them to create intuitive, meaningful data representations for visualization and reporting. Then, you will focus on administration and governance in Microsoft Fabric, emphasizing best practices for security, compliance, and efficient management of analytics solutions. Lastly, you will find detailed practice tests and exam strategies along with supplementary materials to reinforce key concepts. After reading the book, you will have the background and capability to learn the skills and concepts necessary both to pass the DP-600 exam and become a confident Fabric Analytics Engineer. What You Will Learn A complete understanding of all DP-600 certification exam objectives and requirements Key concepts and terminology related to Microsoft Fabric Analytics Step-by-step preparation for successfully passing the DP-600 certification exam Insights into exam structure, question patterns, and strategies for tackling challenging sections Confidence in demonstrating skills validated by the Microsoft Certified: Fabric Analytics Engineer Associate credential Who This Book Is For ​​​​​​​Data engineers, analysts, and professionals with some experience in data engineering or analytics, seeking to expand their knowledge of Microsoft Fabric

Put statistics into practice with Python! Data-driven decisions rely on statistics. Statistics Every Programmer Needs introduces the statistical and quantitative methods that will help you go beyond “gut feeling” for tasks like predicting stock prices or assessing quality control, with examples using the rich tools of the Python ecosystem. Statistics Every Programmer Needs will teach you how to: Apply foundational and advanced statistical techniques Build predictive models and simulations Optimize decisions under constraints Interpret and validate results with statistical rigor Implement quantitative methods using Python In this hands-on guide, stats expert Gary Sutton blends the theory behind these statistical techniques with practical Python-based applications, offering structured, reproducible, and defensible methods for tackling complex decisions. Well-annotated and reusable Python code listings illustrate each method, with examples you can follow to practice your new skills. About the Technology Whether you’re analyzing application performance metrics, creating relevant dashboards and reports, or immersing yourself in a numbers-heavy coding project, every programmer needs to know how to turn raw data into actionable insight. Statistics and quantitative analysis are the essential tools every programmer needs to clarify uncertainty, optimize outcomes, and make informed choices. About the Book Statistics Every Programmer Needs teaches you how to apply statistics to the everyday problems you’ll face as a software developer. Each chapter is a new tutorial. You’ll predict ultramarathon times using linear regression, forecast stock prices with time series models, analyze system reliability using Markov chains, and much more. The book emphasizes a balance between theory and hands-on Python implementation, with annotated code and real-world examples to ensure practical understanding and adaptability across industries. What's Inside Probability basics and distributions Random variables Regression Decision trees and random forests Time series analysis Linear programming Monte Carlo and Markov methods and much more About the Reader Examples are in Python. About the Author Gary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Dunk: Statistical analysis with R on real NBA data. Quotes A well-organized tour of the statistical, machine learning and optimization tools every data science programmer needs. - Peter Bruce, Author of Statistics for Data Science and Analytics Turns statistics from a stumbling block into a superpower. Clear, relevant, and written with a coder’s mindset! - Mahima Bansod, LogicMonitor Essential! Stats and modeling with an emphasis on real-world system design. - Anupam Samanta, Google A great blend of theory and practice. - Ariel Andres, Scotia Global Asset Management

A radical wake up call for world overloaded with data and how data visualisation could be the answer In From Chaos to Clarity: How Data Visualisation Can Save the World, celebrated data visualisation creator James Eagle reveals how our data-saturated age harbours hidden dangers that places humanity in peril. He looks at how masterful visual storytelling might be our salvation. Through vivid examples and profound insights, James Eagle exposes the data pollution clouding modern life, whilst demonstrating how thoughtful, human-centred data visuals can cut through the noise, sharpen our collective understanding and light the path toward a more discerning future. Inside the book: How to unlock the human side of data visualisation by using empathy and storytelling Understanding our brain's deep connection to pictures and stories, and why this matters in this digital age Ways data visualisation can restore our human understanding of this world and tackle misinformation This is a must-read urgent message on how data visualisation is needed to confront data overload and misuse. From Chaos to Clarity is perfect for professionals in finance, engineering, science, mathematics and health, as well as journalists, writers, data scientists, and anyone interested in visual storytelling, reclaiming truth and sharpening our collective thinking to tackling some of the biggest challenges we face in this world.

Textbook on nonlinear and parametric vibrations discussing relevant terminology and analytical and computational tools for analysis, design, and troubleshooting Introduction to Engineering Nonlinear and Parametric Vibrations with MATLAB and MAPLE is a comprehensive textbook that provides theoretical breadth and depth and analytical and computational tools needed to analyze, design, and troubleshoot related engineering problems. The text begins by introducing and providing the required math and computer skills for understanding and simulating nonlinear vibration problems. This section also includes a thorough treatment of parametric vibrations. Many illustrative examples, including software examples, are included throughout the text. A companion website includes the MATLAB and MAPLE codes for examples in the textbook, and a theoretical development for a homoclinic path to chaos. Introduction to Engineering Nonlinear and Parametric Vibrations with MATLAB and MAPLE provides information on: Natural frequencies and limit cycles of nonlinear autonomous systems, covering the multiple time scale, Krylov-Bogellubov, harmonic balance, and Lindstedt-Poincare methods Co-existing fixed point equilibrium states of nonlinear systems, covering location, type, and stability, domains of attraction, and phase plane plotting Parametric and autoparametric vibration including Floquet, Mathieu and Hill theory Numerical methods including shooting, time domain collocation, arc length continuation, and Poincare plotting Chaotic motion of nonlinear systems, covering iterated maps, period doubling and homoclinic paths to chaos, and discrete and continuous time Lyapunov exponents Extensive MATLAB and MAPLE coding for the examples presented Introduction to Engineering Nonlinear and Parametric Vibrations with MATLAB and MAPLE is an essential up-to-date textbook on the subject for upper level undergraduate and graduate engineering students as well as practicing vibration engineers. Knowledge of differential equations and basic programming skills are requisites for reading.

Simplify stats and learn how to graph, analyze, and interpret data the easy way Statistical Analysis with R For Dummies makes stats approachable by combining clear explanations with practical applications. You'll learn how to download and use R and RStudio—two free, open-source tools—to learn statistics concepts, create graphs, test hypotheses, and draw meaningful conclusions. Get started by learning the basics of statistics and R, calculate descriptive statistics, and use inferential statistics to test hypotheses. Then, visualize it all with graphs and charts. This Dummies guide is your well-marked path to sailing through statistics. Get clear explanations of the basics of statistics and data analysis Learn how to analyze and visualize data with R, step by step Create charts, graphs, and summaries to interpret results Explore hypothesis testing, and prediction techniques This is the perfect introduction to R for students, professionals, and the stat-curious.

Unlock the world of data science—no coding required. Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics—what data analysis involves, which skills are useful, and how terms like “data analytics” and “machine learning” connect—without getting too technical too fast. Data science isn’t just about crunching numbers, pulling data from a database, or running fancy algorithms. It’s about asking the right questions, understanding the process from start to finish, and knowing what’s possible (and what’s not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security—because working with data means thinking about people, too. Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today’s most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts— just as AI and data become increasingly central to everyday life. What You Will Learn Grasp foundational statistics and how it matters in data analysis and data science Understand the data science project life cycle and how to manage a data science project Examine the ethics of working with data and its use in data analysis and data science Understand the foundations of data security and privacy Collect, store, prepare, visualize, and present data Identify the many types of machine learning and know how to gauge performance Prepare for and find a career in data science Who This Book is for A wide range of readers who are curious about data science and eager to build a strong foundation. Perfect for undergraduates in the early semesters of their data science degrees, as it assumes no prior programming or industry experience. Professionals will find particular value in the real-world insights shared through practitioner interviews. Business leaders can use it to better understand what data science can do for them and how their teams are applying it. And for career changers, this book offers a welcoming entry point into the field—helping them explore the landscape before committing to more intensive learning paths like degrees or boot camps.

In the rapidly evolving world of data and analytics, professionals face the challenge of navigating complex platforms in order to build more efficient solutions. Microsoft Fabric, hailed as Microsoft’s “biggest data product in history after SQL Server,” offers powerful capabilities but comes with a steep learning curve. The myriad of choices within Fabric can be overwhelming, with multiple ways to tackle tasks, not all of which are equally efficient. This book serves as a definitive roadmap to understanding Microsoft Fabric—and leveraging it to suit your needs. Authors Nikola Ilic and Ben Weissman demystify the core concepts and components necessary to build, manage, and administer robust data solutions within this game-changing product. Discover the core Microsoft Fabric components and understand key concepts and techniques for building a robust data platform Learn to apply Microsoft Fabric effectively in your day-to-day job Understand the concept of a lake-centric architecture Gain the skills to implement a scalable and efficient end-to-end analytics solution Manage and administer a Fabric tenant

This comprehensive guide is designed to address the most frequent and challenging issues faced by users of Power Query, a powerful data transformation tool integrated into Excel, Power BI, and Microsoft Azure. By tackling 96 real-world problems with practical, step-by-step solutions, this book is an essential resource for data analysts, Excel enthusiasts, and Power BI professionals. It aims to enhance your data transformation skills and improve efficiency in handling complex data sets. Structured into 12 chapters, the book covers specific areas of Power Query such as data extraction, referencing, column splitting and merging, sorting and filtering, and pivoting and unpivoting tables. You will learn to combine data from Excel files with varying column names, handle multi-row headers, perform advanced filtering, and manage missing values using techniques such as linear interpolation and K-nearest neighbors (K-NN) imputation. The book also dives into advanced Power Query functions such as Table.Group, List.Accumulate, and List.Generate, explored through practical examples such as calculating running totals and implementing complex grouping and iterative processes. Additionally, it covers crucial topics such as error-handling strategies, custom function creation, and the integration of Python and R with Power Query. In addition to providing explanations on the use of functions and the M language for solving real-world challenges, this book discusses optimization techniques for data cleaning processes and improving computational speed. It also compares the execution time of functions across different patterns and proposes the optimal approach based on these comparisons. In today’s data-driven world, mastering Power Query is crucial for accurate and efficient data processing. But as data complexity grows, so do the challenges and pitfalls that users face. This book serves as your guide through the noise and your key to unlocking the full potential of Power Query. You’ll quickly learn to navigate and resolve common issues, enabling you to transform raw data into actionable insights with confidence and precision. What You Will Learn Master data extraction and transformation techniques for various Excel file structures Apply advanced filtering, sorting, and grouping methods to organize and analyze data Leverage powerful functions such as Table.Group, List.Accumulate, and List.Generate for complex transformations Optimize queries to execute faster Create and utilize custom functions to handle iterative processes and advanced list transformation Implement effective error-handling strategies, including removing erroneous rows and extracting error reasons Customize Power Query solutions to meet specific business needs and share custom functions across files Who This Book Is For Aspiring and developing data professionals using Power Query in Excel or Power BI who seek practical solutions to enhance their skills and streamline complex data transformation workflows

This comprehensive guide book equips you with the knowledge and confidence needed to prep for the exam and thrive as a Power Platform Solution Architect. The book starts with a foundation for successful solution architecture, emphasizing essential skills such as requirements gathering, governance, and security. You will learn to navigate customer discovery, translate business needs into technical requirements, and design solutions that address both functional and non-functional needs. The second part of the book delves into the Microsoft Power Platform ecosystem, offering an in-depth look at its core components—Power Apps, Power Automate, Power BI, Microsoft Copilot, and Robotic Process Automation (RPA). Detailed insights into data modeling, security strategies, and AI integration will guide you in building scalable, secure solutions. Coverage of application life cycle management, which empowers solution architects to design, implement, and deploy Power Platform solutions effectively, is discussed next. You will then go through real-world scenarios, giving you a practical understanding of the challenges and considerations in managing Power Platform projects within a business context. The book concludes with strategies for continuous learning and resources for professional development, including practice questions to assess knowledge and readiness for the PL-600 exam. After reading the book, you will be ready to take the exam and become a successful Power Platform Solution Architect. What You Will Learn Understand the Solution Architect's role, responsibilities, and strategic approaches to successfully navigate projects Master the basics of Power Platform Solution Architecture Understand governance, security, and integration concepts in real-world scenarios Design and deploy effective business solutions using Power Platform components Gain the skills necessary to prep for the PL-600 certification exam Who This Book Is For Professionals pursuing Microsoft PL-600 Solution Architect certification and IT consultants and developers transitioning to solution architect roles

Data is your business. Have you unlocked its full potential? If you read nothing else on data strategy, read this book. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you maximize your analytics capabilities; harness the power of data, algorithms, and AI; and gain competitive advantage in our hyperconnected world. This book will inspire you to: Reap the rewards of digital transformation Make better data-driven decisions Design breakout products that generate profitable insights Address vulnerabilities to cyberattacks and data breaches Reskill your workforce and build a culture of continuous learning Win with personalized customer experiences at scale This collection of articles includes "What's Your Data Strategy?," by Leandro DalleMule and Thomas H. Davenport; "Democratizing Transformation," by Marco Iansiti and Satya Nadella; "Why Companies Should Consolidate Tech Roles in the C-Suite," by Thomas H. Davenport, John Spens, and Saurabh Gupta; "Developing a Digital Mindset," by Tsedal Neeley and Paul Leonardi; "What Does It Actually Take to Build a Data-Driven Culture?," by Mai B. AlOwaish and Thomas C. Redman; "When Data Creates Competitive Advantage," by Andrei Hagiu and Julian Wright; "Building an Insights Engine," by Frank van den Driest, Stan Sthanunathan, and Keith Weed; "Personalization Done Right," by Mark Abraham and David C. Edelman; "Ensure High-Quality Data Powers Your AI," by Thomas C. Redman; "The Ethics of Managing People's Data," by Michael Segalla and Dominique Rouzies; "Where Data-Driven Decision-Making Can Go Wrong," by Michael Luca and Amy C. Edmondson; "Sizing Up Your Cyberrisks," by Thomas J. Parenty and Jack J. Domet; "A Better Way to Put Your Data to Work," Veeral Desai, Tim Fountaine, and Kayvaun Rowshankish; and "Heavy Machinery Meets AI," by Vijay Govindarajan and Venkat Venkatraman. HBR's 10 Must Reads are definitive collections of classic ideas, practical advice, and essential thinking from the pages of Harvard Business Review. Exploring topics like disruptive innovation, emotional intelligence, and new technology in our ever-evolving world, these books empower any leader to make bold decisions and inspire others.

Despite recent advances in technology, software developers, enterprise users, and business technologists still spend much of their time performing repetitive manual tasks. This cookbook shows you how to level up your automation skills with Power Automate to drive efficiency and productivity within your organization. Author Ahmad Najjar provides recipes to help you complete common tasks and solve a wide range of issues you'll encounter when working with Power Automate. This cookbook guides you through fundamental concepts as well as basic-to-intermediate Power Automate activities—everything from understanding flow components to automating approvals, building on-demand flows, and integrating Power Automate with other applications and services. You'll also learn how Microsoft 365 services correlate and integrate with Power Automate. Use Power Automate to create a standard workflow Integrate Power Automate with other applications and services Leverage other Power Platform tools with Power Automate Use Power Automate to work with files and build basic business process flows Send notifications and reminders using Power Automate Trigger workflows on demand

Qualitative and quantitative techniques to apply decision analysis to real-world decision problems, supported by sound mathematics, best practices, soft skills, and more With substantive illustrations based on the authors’ personal experiences throughout, Handbook of Decision Analysis describes the philosophy, knowledge, science, and art of decision analysis. Key insights from decision analysis applications and behavioral decision analysis research are presented, and numerous decision analysis textbooks, technical books, and research papers are referenced for comprehensive coverage. This book does not introduce new decision analysis mathematical theory, but rather ensures the reader can understand and use the most common mathematics and best practices, allowing them to apply rigorous decision analysis with confidence. The material is supported by examples and solution steps using Microsoft Excel and includes many challenging real-world problems. Given the increase in the availability of data due to the development of products that deliver huge amounts of data, and the development of data science techniques and academic programs, a new theme of this Second Edition is the use of decision analysis techniques with big data and data analytics. Written by a team of highly qualified professionals and academics, Handbook of Decision Analysis includes information on: Behavioral decision-making insights, decision framing opportunities, collaboration with stakeholders, information assessment, and decision analysis modeling techniques Principles of value creation through designing alternatives, clear value/risk tradeoffs, and decision implementation Qualitative and quantitative techniques for each key decision analysis task, as opposed to presenting one technique for all decisions. Stakeholder analysis, decision hierarchies, and influence diagrams to frame descriptive, predictive, and prescriptive analytics decision problems to ensure implementation success Handbook of Decision Analysis is a highly valuable textbook, reference, and/or refresher for students and decision professionals in business, management science, engineering, engineering management, operations management, mathematics, and statistics who want to increase the breadth and depth of their technical and soft skills for success when faced with a professional or personal decision.

A practical guide to reproducible and high impact mass spectrometry data analysis R Programming for Mass Spectrometry teaches a rigorous and detailed approach to analyzing mass spectrometry data using the R programming language. It emphasizes reproducible research practices and transparent data workflows and is designed for analytical chemists, biostatisticians, and data scientists working with mass spectrometry. Readers will find specific algorithms and reproducible examples that address common challenges in mass spectrometry alongside example code and outputs. Each chapter provides practical guidance on statistical summaries, spectral search, chromatographic data processing, and machine learning for mass spectrometry. Key topics include: Comprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data Processing chromatographic peaks, peak detection, and quality control in mass spectrometry data Applying machine learning techniques, using Tidymodels for supervised and unsupervised learning, as well as for feature engineering and selection, providing modern approaches to data-driven insights Methods for producing reproducible, publication-ready reports and web pages using RMarkdown R Programming for Mass Spectrometry is an indispensable guide for researchers, instructors, and students. It provides modern tools and methodologies for comprehensive data analysis. With a companion website that includes code and example datasets, it serves as both a practical guide and a valuable resource for promoting reproducible research in mass spectrometry.

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Data Without Labels you’ll learn: Fundamental building blocks and concepts of machine learning and unsupervised learning Data cleaning for structured and unstructured data like text and images Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE Association rule algorithms like aPriori, ECLAT, SPADE Unsupervised time series clustering, Gaussian Mixture models, and statistical methods Building neural networks such as GANs and autoencoders Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling Association rule algorithms like aPriori, ECLAT, and SPADE Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask How to interpret the results of unsupervised learning Choosing the right algorithm for your problem Deploying unsupervised learning to production Maintenance and refresh of an ML solution Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You’ll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don’t get bogged down in theory—the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. About the Technology Generative AI, predictive algorithms, fraud detection, and many other analysis tasks rely on cheap and plentiful unlabeled data. Machine learning on data without labels—or unsupervised learning—turns raw text, images, and numbers into insights about your customers, accurate computer vision, and high-quality datasets for training AI models. This book will show you how. About the Book Data Without Labels is a comprehensive guide to unsupervised learning, offering a deep dive into its mathematical foundations, algorithms, and practical applications. It presents practical examples from retail, aviation, and banking using fully annotated Python code. You’ll explore core techniques like clustering and dimensionality reduction along with advanced topics like autoencoders and GANs. As you go, you’ll learn where to apply unsupervised learning in business applications and discover how to develop your own machine learning models end-to-end. What's Inside Master unsupervised learning algorithms Real-world business applications Curate AI training datasets Explore autoencoders and GANs applications About the Reader Intended for data science professionals. Assumes knowledge of Python and basic machine learning. About the Author Vaibhav Verdhan is a seasoned data science professional with extensive experience working on data science projects in a large pharmaceutical company. Quotes An invaluable resource for anyone navigating the complexities of unsupervised learning. A must-have. - Ganna Pogrebna, The Alan Turing Institute Empowers the reader to unlock the hidden potential within their data. - Sonny Shergill, Astra Zeneca A must-have for teams working with unstructured data. Cuts through the fog of theory ili Explains the theory and delivers practical solutions. - Leonardo Gomes da Silva, onGRID Sports Technology The Bible for unsupervised learning! Full of real-world applications, clear explanations, and excellent Python implementations. - Gary Bake, Falconhurst Technologies

In today's data-driven world, earning the Tableau Certified Data Analyst credential signals your ability to connect, analyze, and communicate insights using one of the industry's leading visualization platforms. This study guide offers practical and comprehensive preparation for the certification exam, with walk-throughs, best practices, vocabulary, and example questions to help you build both confidence and competence in Tableau. Written by Christopher Gardner, business intelligence analyst and lead Tableau developer at the University of Michigan, this guide supports first-time test-takers and seasoned users alike. You'll begin with foundational skills in Tableau Prep Builder and Tableau Desktop—connecting, combining, and preparing data—before progressing to building effective visualizations, performing calculations, and applying advanced tools like level-of-detail expressions, parameters, forecasts, and predictive analytics. Read, manipulate, and prepare data for analysis Navigate Tableau's tools to build impactful visualizations Write calculations and functions to enhance your dashboards Share your work responsibly with secure publishing options

The better the tools you have in your experimentation toolkit, the better off teams will be shipping and evaluating new features on a product. Learn how to create robust A/B testing strategies that evolve with your product and engineering needs. See how to run experiments quickly, efficiently, and at less cost with the overarching goal of improving your product experience and your company's bottom line. The long-term success of any product hinges on a company’s ability to experiment quickly and effectively. The more a company evolves and grows, the more demand there is on the experimentation platform. To continue to meet testing demands and empower teams to leverage A/B testing in their product development life cycle, it’s vital to incorporate techniques to improve testing velocity, cost, and quality. Learn how to create an A/B testing environment for the long term that lets you quickly construct, run, and analyze tests and enables the business to explore and exploit new features in a cost-effective and controlled way. Know when to use techniques — stratified random sampling, interleaving, and metric sensitivity analysis — that let you work faster, more accurately, and more cost-effectively. With practical strategies and hands-on engineering tasks oriented around improving the rate and quality of testing on a product, you can apply what you’ve learned to optimize your experimentation practices. A/B testing is vital to product development. It's time to create the tools and environment that let you run these tests easily, affordably, and reliably.

Ballistics examines the analytical and computational tools for predicting a weapon’s behavior in terms of pressure, stress, and velocity, demonstrating their applications in ammunition and weapons design. It includes updated and revised equations, end-of-chapter problems, case studies, and practical examples.

Discover the skills and insights you need to accurately and efficiently interpret data, improving your influence and decision-making at work