Databaser

Data is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of tools and analytical systems, including relational databases, NoSQL datastores, plus data warehouses and data lakes. What are the right choices for your application? How do you make sense of all these buzzwords?In this second edition, authors Martin Kleppmann and Chris Riccomini build on the foundation laid in the acclaimed first edition, integrating new technologies and emerging trends. You'll be guided through the maze of decisions and trade-offs involved in building a modern data system, from choosing the right tools like Spark and Flink to understanding the intricacies of data laws like the GDPR.Peer under the hood of the systems you already use, and learn to use them more effectivelyMake informed decisions by identifying the strengths and weaknesses of different toolsNavigate the trade-offs around consistency, scalability, fault tolerance, and complexityUnderstand the distributed systems research upon which modern databases are builtPeek behind the scenes of major online services, and learn from their architectures

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.The Semantic Web treats data as a distributed resource on the scale of the World Wide Web, and incorporates features to address the challenges of massive data distribution as part of its basic design. The aim of the first two editions was to motivate the Semantic Web technology stack from end-to-end; to describe not only what the Semantic Web standards are and how they work, but also what their goals are and why they were designed as they are. It tells a coherent story from beginning to end of how the standards work to manage a world-wide distributed web of knowledge in a meaningful way.The third edition builds on this foundation to bring Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, bringing with him years of experience in global linked data, to open up the story to a modern view of global linked data. While the overall story is the same, the examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data. Also included with the third edition, all of the data sets and queries are available online for study and experimentation at data.world/swwo.

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you’ll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence!The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligenceBegins with fundamental design recommendations and progresses through increasingly complex scenariosPresents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and moreDraws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and moreDesign dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.

Your complete guide to Excel 365, written for newbies all the way to seasoned professionals Neatly organised and written using accessible language, Microsoft Excel 365 Bible contains everything that readers need to know to get up and running quickly with Excel. Covering the numerous updates to Excel 365 since the previous edition was published in 2022, this Second Edition contains many useful examples and tips and tricks that cover all essential aspects of Excel—from the basics, such as navigating the user interface, to more advanced topics, such as creating visualisations and crafting custom functions. Written by an accomplished team of authors with decades of Excel and business intelligence experience, this book explores topics including: Entering and editing worksheet data, with information on data types, formulas, dates, times and other cell contentsBuilding formulas with Excel table objects, correcting common formula errors and using formula variablesWorking with data series, changing basic chart elements and making use of chart customizationLoading data from other sources, performing common transformations and applying conditional logicMicrosoft Excel 365 Bible earns a well-deserved spot on the bookshelves of all professionals and individuals seeking to get the most out of Excel 365, from beginners with limited knowledge of the software all the way to advanced users with years of experience under their belts.

Winner of the 2019 International Cartographic Conference – Educational Products awardA lavishly illustrated reference guide, Cartography. by Kenneth Field is an inspiring and creative companion along the nonlinear journey toward making a great map. This sage compendium for contemporary mapmakers distills the essence of cartography into useful topics, organized for convenience in finding the specific idea or method you need. Unlike books targeted to deep scholarly discourse of cartographic theory, this book provides sound, visually compelling information that translates into practical and useful tools for modern mapmaking. At the intersection of science and art, this book serves as a guidepost for designing an accurate and effective map.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Assess data engineering problems using an end-to-end data framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle

This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals.  In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis.  The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics.To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python notebooks complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject.  The Students’ Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors’ Solutions Manual from the link supplied on the text’s Springer website.The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on.

This print textbook is available for students to rent for their classes. The Pearson print rental program provides students with affordable access to learning materials, so they come to class ready to succeed.

A top-down, layered approach to computer networking.

Unique among computer networking texts, the 8th Edition of the popular Computer Networking: A Top Down Approach builds on the authors’ long tradition of teaching this complex subject through a layered approach in a “top-down manner.” The text works its way from the application layer down toward the physical layer, motivating students by exposing them to important concepts early in their study of networking. Focusing on the Internet and the fundamentally important issues of networking, this text provides an excellent foundation for students in computer science and electrical engineering, without requiring extensive knowledge of programming or mathematics. The 8th Edition has been updated to reflect the most important and exciting recent advances in networking, including the importance of software-defined networking (SDN) and the rapid adoption of 4G/5G networks and the mobile applications they enable.

Learn how to deploy and monitor databases in the cloud, manipulate documents, visualize data, and build applications running on MongoDB using Node.jsKey FeaturesLearn the fundamentals of NoSQL databases with MongoDBCreate, manage, and optimize a MongoDB database in the cloud using AtlasUse a real-world dataset to gain practical experience of handling big dataBook DescriptionMongoDB is one of the most popular database technologies for handling large collections of data. This book will help MongoDB beginners develop the knowledge and skills to create databases and process data efficiently.Unlike other MongoDB books, MongoDB Fundamentals dives into cloud computing from the very start – showing you how to get started with Atlas in the first chapter. You will discover how to modify existing data, add new data into a database, and handle complex queries by creating aggregation pipelines. As you progress, you'll learn about the MongoDB replication architecture and configure a simple cluster. You will also get to grips with user authentication, as well as techniques for backing up and restoring data. Finally, you'll perform data visualization using MongoDB Charts.You will work on realistic projects that are presented as bitesize exercises and activities, allowing you to challenge yourself in an enjoyable and attainable way. Many of these mini-projects are based around a movie database case study, while the last chapter acts as a final project where you will use MongoDB to solve a real-world problem based on a bike-sharing app.By the end of this book, you'll have the skills and confidence to process large volumes of data and tackle your own projects using MongoDB.What you will learnSet up and use MongoDB Atlas on the cloudInsert, update, delete, and retrieve data from MongoDBBuild aggregation pipelines to perform complex queriesOptimize queries using indexesMonitor databases and manage user authorizationImprove scalability and performance with sharding clustersReplicate clusters, back up your database, and restore dataCreate data-driven charts and reports from real-time dataWho this book is forThis book is designed for people who are new to MongoDB. It is suitable for developers, database administrators, system administrators, and cloud architects who are looking to use MongoDB for smooth data processing in the cloud. Although not necessary, basic knowledge of a general programming language and experience with other databases will help you grasp the topics covered more easily.

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architecture

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the Jupyter notebook and IPython shell for exploratory computingLearn basic and advanced features in NumPyGet started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples

Join the technological revolution that's taking the financial world by storm. Mastering Bitcoin is your guide through the seemingly complex world of Bitcoin, providing the knowledge you need to participate in the internet of money. Whether you're building the next killer app, investing in a startup, or simply curious about the technology, this revised and expanded third edition provides essential detail to get you started.Bitcoin, the first successful decentralized digital currency, has already spawned a multibillion-dollar global economy open to anyone with the knowledge and passion to participate. Mastering Bitcoin provides the knowledge. You supply the passion.The third edition includes:A broad introduction to Bitcoin and its underlying blockchain-ideal for nontechnical users, investors, and business executivesAn explanation of Bitcoin's technical foundation and cryptographic currency for developers, engineers, and software and systems architectsDetails of the Bitcoin decentralized network, peer-to-peer architecture, transaction lifecycle, and security principlesNew developments such as Taproot, Tapscript, Schnorr signatures, and the Lightning NetworkA deep dive into Bitcoin applications, including how to combine the building blocks offered by this platform into powerful new toolsUser stories, analogies, examples, and code snippets illustrating key technical concepts

Databasteknik används nästan överallt i dag där data behöver lagras och bearbetas. Allt från webbplatser till mobilappar lagrar sina data i databaser. Databaser har därför blivit ett av de områden inom datatekniken som man har störst nytta av att kunna, vare sig man är användare, datatekniker eller programmerare.Boken Databasteknik börjar med grunderna om databaser, som scheman, datamodellering och användningsområden för databaser. Vi går vidare bland annat med frågespråk, transaktionshantering, säkerhet, prestanda och hur databashanteraren arbetar internt.Den vanligaste och populäraste typen av databaser är relations­databaser, och tyngdpunkten ligger därför på dem, bland annat med frågespråket SQL, och vi beskriver system som MySQL, PostgreSQL och Microsoft SQL Server. Men vi vänder oss till alla som arbetar med databaser, även till exempel objektorienterade databaser och NoSQL-databaser.

International bestseller with over half a million copies sold!Celebrate a decade of data storytelling with this special 10th anniversary edition of Storytelling with Data. Featuring refreshed visuals, expanded content, and new insights, this collector's edition builds on the classic's foundational lessons, empowering you to craft persuasive data stories.Don't simply show your data—tell a story with it!Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story.Specifically, you'll learn how to: Understand the importance of context and audienceDetermine the appropriate type of graph for your situationRecognize and eliminate the clutter clouding your informationDirect your audience's attention to the most important parts of your dataThink like a designer and utilize concepts of design in data visualizationLeverage the power of storytelling to help your message resonate with your audienceTogether, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it.

There is growing recognition of the need to address the fragility of digital information, on which our society heavily depends for smooth operation in all aspects of daily life. This has been discussed in many books and articles on digital preservation, so why is there a need for yet one more? Because, for the most part, those other publications focus on documents, images and webpages – objects that are normally rendered to be simply displayed by software to a human viewer. Yet there are clearly many more types of digital objects that may need to be preserved, such as databases, scientific data and software itself.David Giaretta, Director of the Alliance for Permanent Access,  and his contributors explain why the tools and techniques used for preserving rendered objects are inadequate for all these other types of digital objects, and they provide the concepts, techniques and tools that are needed. The book is structured in three parts. The first part is on theory, i.e., the concepts and techniques that are essential for preserving digitally encoded information. The second part then shows practice, i.e., the use and validation of these tools and techniques. Finally, the third part concludes by addressing how to judge whether money is being well spent, in terms of effectiveness and cost sharing.Various examples of digital objects from many sources are used to explain the tools and techniques presented. The presentation style mainly aims at practitioners in libraries, archives and industry who are either directly responsible for preservation or who need to prepare for audits of their archives. Researchers in digital preservation and developers of preservation tools and techniques will also find valuable practical information here. Researchers creating digitally encoded information of all kinds will also need to be aware of these topics so that they can help to ensure that their data is usable and can be valued by others now and in thefuture.To further assist the reader, the book is supported by many hours of videos and presentations from the CASPAR project and by a set of open source software.

Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

Operations from logic and set theory can be applied to existing sets of truths to derive new sets of truths. Applied Mathematics for Database Professionals introduces you to this way of thinking, to the logic and set theory that underlies relational database technology.

This highly regarded textbook provides the definitive account of Customer Relationship Management (CRM) concepts, applications, and technologies, focusing on how companies can create and maintain mutually beneficial relationships with customers.Readers will gain a thorough understanding of the conceptual foundations of CRM, see CRM in practice through illustrative case examples and exercises, and understand how to organise customer data gathering, analysis, and presentation for decision making. The book achieves these outcomes by first considering strategic CRM before moving into operational CRM and, finally, onto analytical aspects of CRM. The fifth edition has been fully updated to include:A series of new case examples to illustrate CRM within various regional and industrial contexts, including those relevant to large, medium, and small enterprisesA series of new exercises and discussion questions to help readers understand CRM concepts and to support pedagogical processes, particularly in higher education environmentsA greater emphasis on managerial applications of CRM through new content to help guide managersAn updated account of new and emerging technologies relevant to CRMExpanded coverage of customer experience (CX), customer engagement (CE), and customer journey management (CJM)Customer Relationship Management is essential reading for advanced undergraduate and postgraduate students studying CRM, Sales Management, Customer Experience Management, and Relationship Marketing, as well as executives who oversee CRM functions. Online resources include an Instructor’s Manual, chapter-by-chapter PowerPoint slides, and a bank of exam questions.

The latest and most authoritative version of the popular step-by-step tutorial for Revit Architecture The newly revised third edition of Revit 2024 for Architecture: No Experience Required is the latest update to the market-leading, real-world guide for learning and building with Revit—the powerful and sophisticated Building Information Modeling (BIM) software used by professionals around the world. This popular, user-friendly book teaches you the Revit interface and helps you understand the foundational concepts and features of the software. You’ll learn to design, document, and present a 3D BIM project with a continuous, step-by-step tutorial that guides you through every phase of the project: from placing walls, doors, windows, structural elements, dimensions, and text, to generating documentation, advanced detailing, site grading, construction scheduling, material takeoffs, and more. In addition, this book helps you prepare for the Autodesk Revit Architecture Certification Exam. Throughout the book, you will find helpful insights directly related to the exam. The last two chapters are dedicated entirely to the exam with a practice test at the end of the book. You’ll also: Learn each phase of designing, documenting, and presenting a four-story office building using a simple yet engaging continuous tutorialFollow the tutorial sequentially or jump to any chapter by downloading the project files from the Sybex websiteUse the start-to-finish tutorial project as a reference for your own real-world projects and to develop a powerful Revit skillsetGain thorough knowledge of Revit’s essential concepts and features to make the move from 2D drafting to 3D building information modelingGet up to speed with advanced features, including new coverage of advanced walls, families, sites, topography, and moreThe Autodesk Revit 2024 for Architecture: No Experience Required, 3rd Edition, is the go-to guide for professionals and students seeking to learn Revit’s essential functions quickly and effectively.

Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivityKey FeaturesPerform advanced data analysis and visualization techniques with R and Python on Excel dataUse exploratory data analysis and pivot table analysis for deeper insights into your dataIntegrate R and Python code directly into Excel using VBA or API endpointsPurchase of the print or Kindle book includes a free PDF eBookBook Description– Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics. – This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. – Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. – Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level. – By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed. What you will learnRead and write Excel files with R and Python librariesAutomate Excel tasks with R and Python scriptsUse R and Python to execute Excel VBA macrosFormat Excel sheets using R and Python packagesCreate graphs with ggplot2 and Matplotlib in ExcelAnalyze Excel data with statistical methods and time series analysisExplore various methods to call R and Python functions from ExcelWho this book is for– If you’re a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. – The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners. – A basic understanding of Excel, Python, and R is all you need to get started.

Praise for the first edition:“In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science.”- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6“This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely—very useful for readers who wish to understand the rationale and flow of the background knowledge.”- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning:Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC.Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection.Regression: New automatic bandwidth selection for local linear regression.Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions.Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code.Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization–Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.

An eye-opening discussion of how to apply new blockchain technologies to realize our digital potentialIn Zero Knowledge, Infinite Trust, pioneering cryptographer, blockchain innovator, and the co-inventor of the STARK cryptographic system, Eli Ben-Sasson, delivers a deeply insightful discussion of the fundamental values and mathematical foundations on which society’s technological backbone will be built. Skipping the speculative frenzy and political manifestos that characterize much of the popular discussion in blockchain, Ben-Sasson reveals how blockchain’s greatest promise lies in humanity’s timeless pursuit of integrity and trust in digital interactions.The book explains how Zero Knowledge Proofs and STARK technology are solving critical challenges that have prevented truly decentralized networks from achieving mass adoption. It demonstrates how to use these tools to protect privacy, secure personal data and digital identities, ensure election integrity, and address other contemporary threats to safety, security, and wellbeing.Inside the book: A discussion of current technological development in the context of a broader historical context, drawing original insights into how digital trust is evolving todayAn exploration of how mathematical certainty can replace institutional authorityAn up-to-date guide to using powerful blockchain technologies to reshape fundamental structuresPerfect for tech professionals and enthusiasts concerned about digital privacy, corporate power, and the future of technology, Zero Knowledge, Infinite Trust offers a compelling new perspective on how to achieve the digital future we were all promised and have yet to realize.

A fast, up-to-date, and convenient desk reference for Excel users of any skill level Drawn from the most important tasks in the latest bestselling Excel Bible , Excel Quick and Easy is your ticket to data mastery. Excel veterans Michael Alexander and Dick Kusleika distil the most essential and useful tasks you need to understand about the world's most popular spreadsheet program, from functions to charts, graphs, formulas and more. Prepare for a whirlwind tour of Excel, packed with simple and step-by-step guides to common and lesser-known Excel features. This book shows you how to: Create new spreadsheets and workbooks by entering and editing worksheet dataSimplify working with large amounts of data by naming and moving rangesMake calculations and draw conclusions from your data by using formulasVisualize and present your data by creating functional chartsThe secret weapon in your productivity arsenal Being great at Excel is quickly becoming a standard expectation for a ton of employers and organizations, in all sorts of industries. Sharpening your skills can boost your workplace performance and make it easier to land promotions or find new roles. Excel Quick and Easy makes it a breeze to develop the proficiencies that help you stand out from your peers. Unique features of this book Step-by-step guides to the most commonly used and productive Excel tasks, from basic worksheet operations to formatting spreadsheets for hardcopy printingIntuitive explanations for making your data tell a compelling story with visualizations, including charts, graphs, and tablesAdvanced number-crunching techniques, including formulas and functions, that help you unlock fresh insights and new conclusions from your dataExcel Quick and Easy is the perfect reference for brand-new Excel users trying to get up-to-speed quickly and confidently. It's also a must-read for anyone migrating from another spreadsheet program, like Google Sheets, or more experienced Excel users who need to solidify and improve their skills. If you're tired of stumbling through your spreadsheets and just “surviving” in Excel, grab a copy of Excel Quick and Easy and supercharge your productivity. You'll refine your understanding of the basics, learn brand-new skills and features, and become the Excel expert that every office desperately needs.

Thinking of migrating to PostgreSQL? This clear, fast-paced introduction helps you understand and use this open source database system. Not only will you learn about the enterprise class features in versions 9.5 to 10, you’ll also discover that PostgeSQL is more than a database system—it’s an impressive application platform as well.With examples throughout, this book shows you how to achieve tasks that are difficult or impossible in other databases. This third edition covers new features, such as ANSI-SQL constructs found only in proprietary databases until now: foreign data wrapper (FDW) enhancements; new full text functions and operator syntax introduced in version 9.6; XML constructs new in version 10; query parallelization features introduced in 9.6 and enhanced in 10; built-in logical replication introduced in Version 10.e.If you’re a current PostgreSQL user, you’ll pick up gems you may have missed before.Learn basic administration tasks such as role management, database creation, backup, and restoreApply the psql command-line utility and the pgAdmin graphical administration toolExplore PostgreSQL tables, constraints, and indexesLearn powerful SQL constructs not generally found in other databasesUse several different languages to write database functionsTune your queries to run as fast as your hardware will allowQuery external and variegated data sources with foreign data wrappersLearn how to use built-in replication to replicate data