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Practical Data Science
by Andreas François VermeulenLearn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.What You'll LearnBecome fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteriaMaster the methods for retrieving actionable business knowledgeCoordinate the handling of polyglot data types in a data lake for repeatable resultsWho This Book Is ForData scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
Practical Data Science Cookbook
by Sean Patrick Murphy Tony OjedaIf you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of data science projects, the steps in the data science pipeline, and the programming examples presented in this book. Since the book is formatted to walk you through the projects with examples and explanations along the way, no prior programming experience is required.
Practical Data Science Cookbook - Second Edition
by Sean Patrick Murphy Tony Ojeda Benjamin Bengfort Abhijit Dasgupta Prabhanjan TattarOver 85 recipes to help you complete real-world data science projects in R and Python About This Book • Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data • Get beyond the theory and implement real-world projects in data science using R and Python • Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn • Learn and understand the installation procedure and environment required for R and Python on various platforms • Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python • Build a predictive model and an exploratory model • Analyze the results of your model and create reports on the acquired data • Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
Practical Data Science with Python 3: Synthesizing Actionable Insights from Data
by Ervin VargaGain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data science practicesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3.
Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data
by Nathan GeorgeLearn to effectively manage data and execute data science projects from start to finish using PythonKey FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor's, Master's, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Practical Data Science with R
by Nina Zumel John Mount"Practical Data Science with R" lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Practical Data Science with R, Second Edition
by Nina Zumel John MountSummary Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You&’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you&’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you&’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the reader You&’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the author Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science.
Practical Data Science with SAP: Machine Learning Techniques for Enterprise Data
by Greg Foss Paul ModdermanLearn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data.Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths.You'll explore:Examples of how data analysis can help you solve several SAP challengesNatural language processing for unlocking the secrets in textData science techniques for data clustering and segmentationMethods for detecting anomalies in your SAP dataData visualization techniques for making your data come to life
Practical Data Security (Routledge Revivals)
by John GordonFirst published in 1993, this volume emerged in response to the genesis of the Internet and provides early considerations on issues including computer viruses, cyber security and network encryption management, with a particular focus on applying risk analysis to the data security of financial institutions. With the stage set by the UK Data Protection Act of 1984 and the Computer Misuse Act of 1990, this volume provides a series of useful contributions for large companies and home PCs and provides a clear introduction setting out the context and the relevant terminology.
Practical DataOps: Delivering Agile Data Science at Scale
by Harvinder AtwalGain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making.Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles.This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goalsRecognize and eliminate barriers to delivering data to users at scaleWork on the right things for the right stakeholders through agile collaborationCreate trust in data via rigorous testing and effective data managementBuild a culture of learning and continuous improvement through monitoring deployments and measuring outcomesCreate cross-functional self-organizing teams focused on goals not reporting linesBuild robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data productsWho This Book Is ForData science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.
Practical Database Auditing for Microsoft SQL Server and Azure SQL: Troubleshooting, Regulatory Compliance, and Governance
by Josephine BushKnow how to track changes and key events in your SQL Server databases in support of application troubleshooting, regulatory compliance, and governance. This book shows how to use key features in SQL Server ,such as SQL Server Audit and Extended Events, to track schema changes, permission changes, and changes to your data. You’ll even learn how to track queries run against specific tables in a database. Not all changes and events can be captured and tracked using SQL Server Audit and Extended Events, and the book goes beyond those features to also show what can be captured using common criteria compliance, change data capture, temporal tables, or querying the SQL Server log. You will learn how to audit just what you need to audit, and how to audit pretty much anything that happens on a SQL Server instance. This book will also help you set up cloud auditing with an emphasis on Azure SQL Database, Azure SQL Managed Instance, and AWS RDS SQL Server. You don’t need expensive, third-party auditing tools to make auditing work for you, and to demonstrate and provide value back to your business. This book will help you set up an auditing solution that works for you and your needs. It shows how to collect the audit data that you need, centralize that data for easy reporting, and generate audit reports using built-in SQL Server functionality for use by your own team, developers, and organization’s auditors. What You Will LearnUnderstand why auditing is important for troubleshooting, compliance, and governanceTrack changes and key events using SQL Server Audit and Extended EventsTrack SQL Server configuration changes for governance and troubleshootingUtilize change data capture and temporal tables to track data changes in SQL Server tablesCentralize auditing data from all your databases for easy querying and reportingConfigure auditing on Azure SQL, Azure SQL Managed Instance, and AWS RDS SQL Server Who This Book Is ForDatabase administrators who need to know what’s changing on their database servers, and those who are making the changes; database-savvy DevOps engineers and developers who are charged with troubleshooting processes and applications; developers and administrators who are responsible for generating reports in support of regulatory compliance reporting and auditing
Practical Database Programming with Java
by Ying BaiCovers fundamental and advanced Java database programming techniques for beginning and experienced readers This book covers the practical considerations and applications in database programming using Java NetBeans IDE, JavaServer Pages, JavaServer Faces, and Java Beans, and comes complete with authentic examples and detailed explanations. Two data-action methods are developed and presented in this important resource. With Java Persistence API and plug-in Tools, readers are directed step by step through the entire database programming development process and will be able to design and build professional data-action projects with a few lines of code in mere minutes. The second method, runtime object, allows readers to design and build more sophisticated and practical Java database applications. Advanced and updated Java database programming techniques such as Java Enterprise Edition development kits, Enterprise Java Beans, JavaServer Pages, JavaServer Faces, Java RowSet Object, and Java Updatable ResultSet are also discussed and implemented with numerous example projects. Ideal for classroom and professional training use, this text also features: A detailed introduction to NetBeans Integrated Development Environment Java web-based database programming techniques (web applications and web services) More than thirty detailed, real-life sample projects analyzed via line-by-line illustrations Problems and solutions for each chapter A wealth of supplemental material available for download from the book's ftp site, including PowerPoint slides, solution manual, JSP pages, sample image files, and sample databases Coverage of two popular database systems: SQL Server 2008 and Oracle This book provides undergraduate and graduate students as well as database programmers and software engineers with the necessary tools to handle the database programming issues in the Java NetBeans environment. To obtain instructor materials please send an email to: pressbooks@ieee.org
Practical Database Programming with Visual Basic.NET
by Ying BaiThe most up-to-date Visual Basic.NET programming textbook—covering both fundamentals and advanced-level programming techniques—complete with examples and solutions Visual Basic.NET (VB.NET) is an object-oriented computer programming language that can be viewed as an evolution of the classic Visual Basic (VB), which is implemented on the .NET Framework. Microsoft currently supplies two major implementations of Visual Basic: Microsoft Visual Studio (which is commercial software) and Microsoft Visual Studio Express (which is free of charge). Forgoing the large amounts of programming codes found in most database programming books, Practical Database Programming with Visual Basic.NET shows students and professionals both how to develop professional and practical database programs in a Visual Basic.NET environment by using Visual Studio.NET Data Tools and Wizards related to ADO.NET 4.0, and how to apply codes that are auto-generated by solely using Wizards. The fully updated Second Edition: Covers both fundamentals and advanced database programming techniques Introduces three popular database systems with practical examples including MS Access, SQL Server 2008, and Oracle Features more than fifty sample projects with detailed illustrations and explanations to help students understand key techniques and programming technologies Includes downloadable programming codes and exercise questions This book provides undergraduate and graduate students as well as database programmers and software engineers with the necessary tools to handle the database programming issues in the Visual Studio.NET environment.
Practical Debugging at Scale: Cloud Native Debugging in Kubernetes and Production
by Shai AlmogOverhaul your debugging techniques and master the theory and tools needed to debug and troubleshoot cloud applications in production environments. This book teaches debugging skills that universities often avoid, but that typically consume as much as 60% of our time as developers. The book covers the use of debugger features such as tracepoints, object marking, watch renderers, and more. Author Shai Almog presents a scientific approach to debugging that is grounded in theory while being practical enough to help you to chase stubborn bugs through the maze of a Kubernetes deployment.Practical Debugging at Scale assumes a polyglot environment as is common for most enterprises, but focuses on JVM environments. Most of the tooling and techniques described are applicable to Python, Node, and other platforms, as well as to Java and other JVM languages. The book specifically covers debugging in production, an often-neglected discipline but an all too painful reality. You’ll learn modern techniques around observability, monitoring, logging, and full stack debugging that you can put to immediate use in troubleshooting common ailments in production environments. You Will Learn:The scientific method underlying the process of debuggingDebugger capabilities such as tracepoints and marker objectsThe correct use of less understood features such as exception breakpointsTechniques for tracing issues in production Kubernetes environmentsObservability and monitoring to resolve production problemsIndustry best practices for common tooling such as logging Profiling to understand performance and memory problems Who This Book Is ForDevelopers in Java and JVM-related languages who want to improve their debugging skills and production reliability; and developers of cloud applications who are facing the pain of production bugs that are hard to replicate and fix
Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production
by Yong Liu Dr. Matei ZahariaTrain, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowKey FeaturesFocus on deep learning models and MLflow to develop practical business AI solutions at scaleShip deep learning pipelines from experimentation to production with provenance trackingLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibilityBook DescriptionThe book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.What you will learnUnderstand MLOps and deep learning life cycle developmentTrack deep learning models, code, data, parameters, and metricsBuild, deploy, and run deep learning model pipelines anywhereRun hyperparameter optimization at scale to tune deep learning modelsBuild production-grade multi-step deep learning inference pipelinesImplement scalable deep learning explainability as a serviceDeploy deep learning batch and streaming inference servicesShip practical NLP solutions from experimentation to productionWho this book is forThis book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.
Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
by Anirudh Koul Siddha Ganju Meher KasamWhether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow LiteDevelop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google CoralExplore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studiesSimulate an autonomous car in a video game environment and build a miniature version with reinforcement learningUse transfer learning to train models in minutesDiscover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Practical Deep Learning, 2nd Edition: A Python-Based Introduction
by Ronald T. KneuselDeep learning made simple.Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel.After a brief review of basic math and coding principles, you&’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you&’re a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you:How neural networks work and how they&’re trainedHow to use classical machine learning modelsHow to develop a deep learning model from scratchHow to evaluate models with industry-standard metricsHow to create your own generative AI modelsEach chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you&’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you&’ll gain the skills and confidence you need to build real AI systems that solve real problems.New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).
Practical Deep Learning: A Python-Based Introduction
by Ronald T. KneuselPractical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you&’ve been curious about artificial intelligence and machine learning but didn&’t know where to start, this is the book you&’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further.All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you&’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models&’ performance.You&’ll also learn:How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector MachinesHow neural networks work and how they&’re trainedHow to use convolutional neural networksHow to develop a successful deep learning model from scratch You&’ll conduct experiments along the way, building to a final case study that incorporates everything you&’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Practical Design Patterns for Java Developers: Hone your software design skills by implementing popular design patterns in Java
by Miroslav Wengner Bruno SouzaUnravel the power of Java design patterns by learning where to apply them effectively to solve specific software design and development problemsKey FeaturesDecouple logic across objects with dependency injection by creating various vehicles with featuresFinalize vehicle construction by chaining handlers using the Chain of Responsibility PatternPlan and execute an advanced vehicle sensor initiation with the Scheduler PatternBook DescriptionDesign patterns are proven solutions to standard problems in software design and development, allowing you to create reusable, flexible, and maintainable code. This book enables you to upskill by understanding popular patterns to evolve into a proficient software developer.You'll start by exploring the Java platform to understand and implement design patterns. Then, using various examples, you'll create different types of vehicles or their parts to enable clarity in design pattern thinking, along with developing new vehicle instances using dedicated design patterns to make the process consistent. As you progress, you'll find out how to extend vehicle functionalities and keep the code base structure and behavior clean and shiny. Concurrency plays an important role in application design, and you'll learn how to employ a such design patterns with the visualization of thread interaction. The concluding chapters will help you identify and understand anti-pattern utilization in the early stages of development to address refactoring smoothly. The book covers the use of Java 17+ features such as pattern matching, switch cases, and instances of enhancements to enable productivity.By the end of this book, you'll have gained practical knowledge of design patterns in Java and be able to apply them to address common design problems.What you will learnUnderstand the most common problems that can be solved using Java design patternsUncover Java building elements, their usages, and concurrency possibilitiesOptimize a vehicle memory footprint with the Flyweight PatternExplore one-to-many relations between instances with the observer patternDiscover how to route vehicle messages by using the visitor patternUtilize and control vehicle resources with the thread-pool patternUnderstand the penalties caused by anti-patterns in software designWho this book is forIf you are an intermediate-level Java developer or software architect looking to learn the practical implementation of software design patterns in Java, then this book is for you. No prior knowledge of design patterns is required, but an understanding of Java programming is necessary.
Practical DevOps
by Joakim VeronaHarness the power of DevOps to boost your skill set and make your IT organization perform better About This Book * Get to know the background of DevOps so you understand the collaboration between different aspects of an IT organization and a software developer * Improve your organization's performance to ensure smooth production of software and services * Deploy top-quality software and ensure software maintenance and release management with this practical guide Who This Book Is For This book is aimed at developers and system administrators who wish to take on larger responsibilities and understand how the infrastructure that builds today's enterprises works. This book is also great for operations personnel who would like to better support developers. You do not need to have any previous knowledge of DevOps. What You Will Learn * Appreciate the merits of DevOps and continuous delivery and see how DevOps supports the agile process * Understand how all the systems fit together to form a larger whole * Set up and familiarize yourself with all the tools you need to be efficient with DevOps * Design an application that is suitable for continuous deployment systems with Devops in mind * Store and manage your code effectively using different options such as Git, Gerrit, and Gitlab * Configure a job to build a sample CRUD application * Test the code using automated regression testing with Jenkins Selenium * Deploy your code using tools such as Puppet, Ansible, Palletops, Chef, and Vagrant * Monitor the health of your code with Nagios, Munin, and Graphite * Explore the workings of Trac--a tool used for issue tracking In Detail DevOps is a practical field that focuses on delivering business value as efficiently as possible. DevOps encompasses all the flows from code through testing environments to production environments. It stresses the cooperation between different roles, and how they can work together more closely, as the roots of the word imply--Development and Operations. After a quick refresher to DevOps and continuous delivery, we quickly move on to looking at how DevOps affects architecture. You'll create a sample enterprise Java application that you'll continue to work with through the remaining chapters. Following this, we explore various code storage and build server options. You will then learn how to perform code testing with a few tools and deploy your test successfully. Next, you will learn how to monitor code for any anomalies and make sure it's running properly. Finally, you will discover how to handle logs and keep track of the issues that affect processes Style and approach This book is primarily a technical guide to DevOps with practical examples suitable for people who like to learn by implementing concrete working code. It starts out with background information and gradually delves deeper into technical subjects.
Practical DevOps, Second Edition: Implement DevOps in your organization by effectively building, deploying, testing, and monitoring code, 2nd Edition
by Joakim VeronaUnderstand the benefits of DevOps and continuous delivery and see how they support the agile software development processKey FeaturesLearn how DevOps can accelerate your entire software development life cycleImprove your organization's performance to ensure the smooth production of software and servicesGet hands-on experience in using efficient DevOps tools to better effectBook DescriptionDevOps is a practical field that focuses on delivering business value as efficiently as possible. DevOps encompasses all code workflows from testing environments to production environments. It stresses cooperation between different roles, and how they can work together more closely, as the roots of the word imply—Development and Operations.Practical DevOps begins with a quick refresher on DevOps and continuous delivery and quickly moves on to show you how DevOps affects software architectures. You'll create a sample enterprise Java application that you’'ll continue to work with through the remaining chapters. Following this, you will explore various code storage and build server options. You will then learn how to test your code with a few tools and deploy your test successfully. In addition to this, you will also see how to monitor code for any anomalies and make sure that it runs as expected. Finally, you will discover how to handle logs and keep track of the issues that affect different processes.By the end of the book, you will be familiar with all the tools needed to deploy, integrate, and deliver efficiently with DevOps.What you will learnUnderstand how all deployment systems fit together to form a larger systemSet up and familiarize yourself with all the tools you need to be efficient with DevOpsDesign an application suitable for continuous deployment systems with DevOps in mindStore and manage your code effectively using Git, Gerrit, Gitlab, and moreConfigure a job to build a sample CRUD applicationTest your code using automated regression testing with Jenkins SeleniumDeploy your code using tools such as Puppet, Ansible, Palletops, Chef, and VagrantWho this book is forIf you're a developer or system administrator looking to take on larger responsibilities and understand how the infrastructure that builds today's enterprises works, this is the book for you. This book will also help you greatly if you're an operations worker who would like to better support developers. You do not need any previous knowledge of DevOps to understand the concepts in this book.
Practical Development Environments
by Matthew B. DoarThis book doesn't tell you how to write faster code, or how to write code with fewer memory leaks, or even how to debug code at all. What it does tell you is how to build your product in better ways, how to keep track of the code that you write, and how to track the bugs in your code. Plus some more things you'll wish you had known before starting a project.Practical Development Environments is a guide, a collection of advice about real development environments for small to medium-sized projects and groups. Each of the chapters considers a different kind of tool - tools for tracking versions of files, build tools, testing tools, bug-tracking tools, tools for creating documentation, and tools for creating packaged releases. Each chapter discusses what you should look for in that kind of tool and what to avoid, and also describes some good ideas, bad ideas, and annoying experiences for each area. Specific instances of each type of tool are described in enough detail so that you can decide which ones you want to investigate further.Developers want to write code, not maintain makefiles. Writers want to write content instead of manage templates. IT provides machines, but doesn't have time to maintain all the different tools. Managers want the product to move smoothly from development to release, and are interested in tools to help this happen more often. Whether as a full-time position or just because they are helpful, all projects have toolsmiths: making choices about tools, installing them, and then maintaining the tools that everyone else depends upon. This book is especially for everyone who ends up being a toolsmith for his or her group.
Practical Digital Forensics
by Richard BoddingtonThis book is for anyone who wants to get into the field of digital forensics. Prior knowledge of programming languages (any) will be of great help, but not a compulsory prerequisite.
Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python
by Ryan T. White Archana Tikayat RayA practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data scienceKey FeaturesApply the math of countable objects to practical problems in computer scienceExplore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematicsLearn complex statistical and mathematical concepts with the help of hands-on examples and expert guidanceBook DescriptionDiscrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.What you will learnUnderstand the terminology and methods in discrete math and their usage in algorithms and data problemsUse Boolean algebra in formal logic and elementary control structuresImplement combinatorics to measure computational complexity and manage memory allocationUse random variables, calculate descriptive statistics, and find average-case computational complexitySolve graph problems involved in routing, pathfinding, and graph searches, such as depth-first searchPerform ML tasks such as data visualization, regression, and dimensionality reductionWho this book is forThis book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.
Practical Django 2 and Channels 2: Building Projects And Applications With Real-time Capabilities
by Federico MaraniLearn how to rapidly build your own ecommerce site by applying Django’s battle-tested components. This book demonstrates Django’s features and conventions to help you develop modern web applications quickly. You'll adopt a "learn by doing" approach and gain a deeper understanding Django by working through a project in which the real-time component will be critical. The book starts with the basics and explains the difference between a Django project and a Django app, the most important settings, how to change them, and the fundamentals of packaging. You'll then be introduced to all the standard tools of Django, along with a sample project. The book then moves on to Channels, a recent addition to the Django ecosystem. It extends the framework with support for real-time operations such as Websockets and other asynchronous features.Practical Django 2 and Channels 2 provides the practical concepts needed to create complex, database-driven websites as easily as possible.What You'll LearnBuild and deploy a simple company site with DjangoDevelop more complex, data-heavy sites using the Django ORMIntegrate Django with ChannelsUnit-test your solutionsWho This Book Is ForPython developers and web developers wanting to learn Django 2 and Channels 2