"Messy code is a nuisance. 'Tidying' code, to make it more readable, requires breaking it up into manageable sections. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings to improve your code while keeping the overall structure of the system in mind. Instead of trying to master tidying all at once, this book lets you try out a few examples that make sense for your problem. If you have a big function containing many lines of code, you'll learn how to logically divide it into smaller chunks. Along the way, you'll learn the theory behind software design: coupling, cohesion, discounted cash flows, and optionality."--Provided by publisher.
What will you learn from this book? If you're a software developer looking for a quick on-ramp to software architecture, this handy guide is a great place to start. From the authors of Fundamentals of Software Architecture, Head First Software Architecture teaches you how to think architecturally and explores the unique challenges of software architecture. You'll learn the distinction between architecture and design and the relationship between code, components, and architectural styles. You'll also learn how to work with some common architectural styles through vivid, fun examples. Quick, easy, and entertaining, this book is a valuable introduction to the world of software architecture. Why does this book look so different? Based on the latest research in cognitive science and learning theory, Head First Software Architecture uses a visually rich format to engage your mind, rather than a text-heavy approach that puts you to sleep. Why waste your time struggling with new concepts? This multisensory learning experience is designed for the way your brain really works.
Intermediate level, for programmers fairly familiar with Java, but new to the functional style of programming and lambda expressions.Get ready to program in a whole new way. Functional Programming in Java will help you quickly get on top of the new, essential Java 8 language features and the functional style that will change and improve your code. This short, targeted book will help you make the paradigm shift from the old imperative way to a less error-prone, more elegant, and concise coding style that's also a breeze to parallelize. You'll explore the syntax and semantics of lambda expressions, method and constructor references, and functional interfaces. You'll design and write applications better using the new standards in Java 8 and the JDK. Lambda expressions are lightweight, highly concise anonymous methods backed by functional interfaces in Java 8. You can use them to leap forward into a whole new world of programming in Java. With functional programming capabilities, which have been around for decades in other languages, you can now write elegant, concise, less error-prone code using standard Java. This book will guide you though the paradigm change, offer the essential details about the new features, and show you how to transition from your old way of coding to an improved style.In this book you'll see popular design patterns, such as decorator, builder, and strategy, come to life to solve common design problems, but with little ceremony and effort. With these new capabilities in hand, Functional Programming in Java will help you pick up techniques to implement designs that were beyond easy reach in earlier versions of Java. You'll see how you can reap the benefits of tail call optimization, memoization, and effortless parallelization techniques.Java 8 will change the way you write applications. If you're eager to take advantage of the new features in the language, this is the book for you.What you need:Java 8 with support for lambda expressions and the JDK is required to make use of the concepts and the examples in this book.
Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?
> > With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers. > How can decision makers use data and technology to ensure desired outcomes? How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments? How can organizational decision makers assess and improve their decisions over time
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.Learn how to:Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learningUnderstand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargonPerform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significanceManipulate vectors and matrices and perform matrix decompositionIntegrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networksNavigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
Get up to speed on Git for tracking, branching, merging, and managing code revisions. Through a series of step-by-step tutorials, this practical guide takes you quickly from Git fundamentals to advanced techniques, and provides friendly yet rigorous advice for navigating the many functions of this open source version control system.This thoroughly revised edition also includes tips for manipulating trees, extended coverage of the reflog and stash, and a complete introduction to the GitHub repository. Git lets you manage code development in a virtually endless variety of ways, once you understand how to harness the systems flexibility. This book shows you how.Learn how to use Git for several real-world development scenariosGain insight into Gits common-use cases, initial tasks, and basic functionsUse the system for both centralized and distributed version controlLearn how to manage merges, conflicts, patches, and diffsApply advanced techniques such as rebasing, hooks, and ways to handle submodulesInteract with Subversion (SVN) repositoriesincluding SVN to Git conversionsNavigate, use, and contribute to open source projects though GitHub
Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes.In this practical book, author Patrick Viafore shows you how to use Python's type system to the max. You'll look at user-defined types, such as classes and enums, and Python's type hinting system. You'll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you'll write clearer and more maintainable code.Learn why types are essential in modern development ecosystemsUnderstand how type choices such as classes, dictionaries, and enums reflect specific intentsMake Python extensible for the future without adding bloatUse popular Python tools to increase the safety and robustness of your codebaseEvaluate current code to detect common maintainability gotchasBuild a safety net around your codebase with linters and tests
This in-depth book shows you ways to master strategic thinking, manage difficult projects, and set the standard for technical work. You'll learn how to be a leader without direct authority, how to plan ahead so that you're making the right technical decisions, and how to make everyone around you better.
This hands-on third edition, expanded and thoroughly updated for version 1.0 and beyond, shows you the fastest way to get up and running with Terraform.
Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it easier to learn the next. You'll understand why the tidymodels framework has been built to be used by a broad range of people.With this book, you will:Learn the steps necessary to build a model from beginning to endUnderstand how to use different modeling and feature engineering approaches fluentlyExamine the options for avoiding common pitfalls of modeling, such as overfittingLearn practical methods to prepare your data for modelingTune models for optimal performanceUse good statistical practices to compare, evaluate, and choose among models
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.
Provides a overview of the agile development process that serves as a introduction for non-technical readers.
With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow.You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference.Learn the key steps for preparing your data for analysisPerform time series analysis using SQL's date and time manipulationsUse cohort analysis to investigate how groups change over timeUse SQL's powerful functions and operators for text analysisDetect outliers in your data and replace them with alternate valuesEstablish causality using experiment analysis, also known as A/B testing
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Grner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.You'll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.
The Lightning Network (LN) is a rapidly growing second-layer payment protocol that works on top of Bitcoin to provide near-instantaneous transactions between two parties. With this practical guide, authors Andreas M. Antonopoulos, Olaoluwa Osuntokun, and Rene Pickhardt explain how this advancement will enable the next level of scale for Bitcoin, increasing speed and privacy while reducing fees.Ideal for developers, systems architects, investors, and entrepreneurs looking to gain a better understanding of LN, this book demonstrates why experts consider LN a critical solution to Bitcoin's scalability problem. You'll learn how LN has the potential to support far more transactions than today's financial networks.This book examines:How the Lightning Network addresses the challenge of blockchain scalingThe Basis of Lightning Technology (BOLT) standards documentsThe five layers of the Lightning Network Protocol SuiteLN basics, including wallets, nodes, and how to operate oneLightning payment channels, onion routing, and gossip protocolFinding paths across payment channels to transport Bitcoin off-chain from sender to recipient
To get started, you'll need only what you can usually find in your home: items like paper, ziploc bags, index cards, coloring utensils, pencils, rubberbands, scissors, etc. As we progress in the world of making magic, we'll explore how to use 3D design tools like Tinkercad (you can register for a free account at tinkercad.com).
Get up to speed with Helm, the preeminent package manager for the Kubernetes container orchestration system. This practical guide shows you how to efficiently create, install, and manage the applications running inside your containers. Helm maintainers Matt Butcher, Matt Farina, and Josh Dolitsky explain how this package manager fits into the Kubernetes ecosystem and provide an inside look at Helm's design and best practices.More than 70% of the organizations that work with Kubernetes use Helm today. While the Helm community provides thousands of packages, or charts, to help you get started, this book walks developers and DevOps engineers through the process of creating custom charts to package applications. If you have a working understanding of Kubernetes, you're ready to go.Explore primary features including frequently used Helm commandsLearn how to build and deploy Helm charts from scratchUse Helm to manage complexity and achieve repeatable deploymentsPackage an application and its dependencies for easy installationManage the entire lifecycle of applications on KubernetesExplore ways to extend Helm to add features and functionalityLearn features for testing, handling dependencies, and providing security
This practical guide presents Lean and Agile principles and patterns to help you move fast at scale - and demonstrates why and how to apply these methodologies throughout your organization, rather than with just one department or team.
This updated book shows your company how to use alignment diagrams to turn valuable customer observations into actionable insight. You'll learn how this unique tool helps you visually map your existing customer experience and envision future solutions.
The future will be increasingly distributed. As the publicity surrounding Bitcoin and blockchain has shown, distributed technology and business models are gaining popularity. Yet the disruptive potential of this technology is often obscured by hype and misconception. This detailed guide distills the complex, fast moving ideas behind blockchain into an easily digestible reference manual, showing what's really going on under the hood.Finance and technology pros will learn how a blockchain works as they explore the evolution and current state of the technology, including the functions of cryptocurrencies and smart contracts. This book is for anyone evaluating whether to invest time in the cryptocurrency and blockchain industry. Go beyond buzzwords and see what the technology really has to offer.Learn why Bitcoin was fundamentally important in blockchain's birthExplore altcoin and alternative blockchain projects to understand what's possibleUnderstand the challenges of scaling and forking a blockchainLearn what Ethereum and other blockchains offerExamine emerging business uses for blockchain beyond cryptocurrencyDiscover where the future lies in this exciting new technology
One key responsibility of product designers and UX practitioners is to conduct formal and informal research to clarify design decisions and business needs. But theres often mystery around product research, with the feeling that you need to be a research Zen master to gather anything useful. Fact is, anyone can conduct product research. With this quick reference guide, youll learn a common language and set of tools to help you carry out research in an informed and productive manner.This book contains four sections, including a brief introduction to UX research, planning and preparation, facilitating research, and analysis and reporting. Each chapter includes a short exercise so you can quickly apply what youve learned.Learn what it takes to ask good research questionsKnow when to use quantitative and qualitative research methodsExplore the logistics and details of coordinating a research sessionUse softer skills to make research seem natural to participantsLearn tools and approaches to uncover meaning in your raw dataCommunicate your findings with a framework and structure
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.Youll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "e;signals"e; in your datasetCommunicatelearn R Markdown for integrating prose, code, and results
What will you learn from this book?This brain-friendly guide teaches you everything from JavaScript language fundamentals to advanced topics, including objects, functions, and the browsers document object model. You wont just be readingyoull be playing games, solving puzzles, pondering mysteries, and interacting with JavaScript in ways you never imagined. And youll write real code, lots of it, so you can start building your own web applications. Prepare to open your mind as you learn (and nail) key topics including:The inner details of JavaScriptHow JavaScript works with the browserThe secrets of JavaScript typesUsing arraysThe power of functionsHow to work with objectsMaking use of prototypesUnderstanding closuresWriting and testing applicationsWhats so special about this book?We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First JavaScript Programming uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep. This book replaces Head First JavaScript, which is now out of print.
A new wave of products is helping people change their behavior and daily routines, whether it's exercising more, taking control of their finances, or organizing their email. This practical guide shows you how to design these types of products.
By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Python's implementation. You'll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs
As data floods into your company, you need to put it to work right away-and SQL is the best tool for the job. With the latest edition of this introductory guide, author Alan Beaulieu helps developers get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports.
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