Böcker utgivna av Manning Publications

By adopting the micro frontends approach and designing your web apps as systems of features, you can deliver faster feature development, easier upgrades, and pick and choose the technology you use in your stack. Micro Frontends in Action is your guide to simplifying unwieldy frontends by composing them from small, well-defined units. You'll learn to integrate web applications made up of smaller fragments using tools such as web components or server side includes, how to solve the organizational challenges of micro frontends, and how to create a design system that ensures an end user gets a consistent look and feel for your application. Key Features· Applying integration strategies with iframes, AJAX, server-side includes, web components and the app-shell approach· Optimizing for performance and asset delivery strategies· Designing coherent user interfaces· Migrating to a micro frontends architecture For intermediate web developers, team leaders, and software architects.

Discover valuable machine learning techniques you can understand and apply using just high-school math.In Grokking Machine Learning you will learn:     Supervised algorithms for classifying and splitting data     Methods for cleaning and simplifying data     Machine learning packages and tools     Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's inside     Supervised algorithms for classifying and splitting data     Methods for cleaning and simplifying data     Machine learning packages and tools     Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python. No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning

Svelte and Sapper in Action teaches you to design and build fast, elegant web applications. You’ll start immediately by creating an engaging Travel Packing app as you learn to create Svelte components and develop great UX. You’ll master Svelte’s unique state management model, use Sapper for simplified page routing, and take on modern best practices like code splitting, offline support, and server-rendered views.Summary Imagine web apps with fast browser load times that also offer amazing developer productivity and require less code to create. That’s what Svelte and Sapper deliver! Svelte pushes a lot of the work a frontend framework would handle to the compile step, so your app components come out as tight, well-organized JavaScript modules. Sapper is a lightweight web framework that minimizes application size through server-rendering front pages and only loading the JavaScript you need. The end result is more efficient apps with great UX and simplified state management. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Many web frameworks load hundreds of “just-in-case” code lines that clutter and slow your apps. Svelte, an innovative, developer-friendly tool, instead compiles applications to very small bundles for lightning-fast load times that do more with less code. Pairing Svelte with the Sapper framework adds features for flexible and simple page routing, server-side rendering, static site development, and more. About the book Svelte and Sapper in Action teaches you to design and build fast, elegant web applications. You’ll start immediately by creating an engaging Travel Packing app as you learn to create Svelte components and develop great UX. You’ll master Svelte’s unique state management model, use Sapper for simplified page routing, and take on modern best practices like code splitting, offline support, and server-rendered views. What's inside - Creating Svelte components - Using stores for shared data - Configuring page routing - Debugging, testing, and deploying Svelte apps - Using Sapper for dynamic and static sites About the reader For web developers familiar with HTML, CSS, and JavaScript. About the author Mark Volkmann is a partner at Object Computing, where he has provided software consulting and training since 1996. Table of Contents PART 1 - GETTING STARTED 1 Meet the players 2 Your first Svelte app PART 2 - DEEPER INTO SVELTE 3 Creating components 4 Block structures 5 Component communication 6 Stores 7 DOM interactions 8 Lifecycle functions 9 Client-side routing 10 Animation 11 Debugging 12 Testing 13 Deploying 14 Advanced Svelte PART 3 - DEEPER INTO SAPPER 15 Your first Sapper app 16 Sapper applications 17 Sapper server routes 18 Exporting static sties with Sapper 19 Sapper offline support PART 4 - BEYOND SVELTE AND SAPPER 20 Preprocessors 21 Svelte Native

React Hooks in Action teaches you to write fast and reusable React components using Hooks.Summary Build stylish, slick, and speedy-to-load user interfaces in React without writing custom classes. React Hooks are a new category of functions that help you to manage state, lifecycle, and side effects within functional components. React Hooks in Action teaches you to use pre-built hooks like useState, useReducer and useEffect to build your own hooks. Your code will be more reusable, require less boilerplate, and you’ll instantly be a more effective React developer. About the technology Get started with React Hooks and you’ll soon have code that’s better organized and easier to maintain. React Hooks are targeted JavaScript functions that let you reuse and share functionality across components. Use them to split components into smaller functions, manage state and side effects, and access React features without classes—all without having to rearrange your component hierarchy. About the book React Hooks in Action teaches you to write fast and reusable React components using Hooks. You’ll start by learning to create component code with Hooks. Next, you’ll implement a resource booking application that demonstrates managing local state, application state, and side effects like fetching data. Code samples and illustrations make learning Hooks easy. What's inside     Build function components that access React features     Manage local, shared, and application state     Explore built-in, custom, and third-party hooks     Load, update, and cache data with React Query     Improve page and data loading with code-splitting and React Suspense About the reader For beginning to intermediate React developers. About the author John Larsen has been a teacher and web developer for over 20 years, creating apps for education and helping students learn to code. He is the author of Get Programming with JavaScript. Table of Contents PART 1 1 React is evolving 2 Managing component state with useState hook 3 Managing component state with useReducer hook 4 Working with side effects 5 Managing component state with useRef hook 6 Managing application state 7 Managing performance with useMemo 8 Managing state with the Context API 9 Creating your own hooks 10 Using third party hooks PART 2 11 Code splitting with Suspense 12 Integrating data-fetching with Suspense 13 Experimenting with useTransition, useDeferredValue and SuspenseList

API Security in Action teaches you how to create secure APIs for any situation. By following this hands-on guide you’ll build a social network API while mastering techniques for flexible multi-user security, cloud key management, and lightweight cryptography.Summary A web API is an efficient way to communicate with an application or service. However, this convenience opens your systems to new security risks. API Security in Action gives you the skills to build strong, safe APIs you can confidently expose to the world. Inside, you’ll learn to construct secure and scalable REST APIs, deliver machine-to-machine interaction in a microservices architecture, and provide protection in resource-constrained IoT (Internet of Things) environments. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology APIs control data sharing in every service, server, data store, and web client. Modern data-centric designs—including microservices and cloud-native applications—demand a comprehensive, multi-layered approach to security for both private and public-facing APIs. About the book API Security in Action teaches you how to create secure APIs for any situation. By following this hands-on guide you’ll build a social network API while mastering techniques for flexible multi-user security, cloud key management, and lightweight cryptography. When you’re done, you’ll be able to create APIs that stand up to complex threat models and hostile environments. What's inside     Authentication     Authorization     Audit logging     Rate limiting     Encryption About the reader For developers with experience building RESTful APIs. Examples are in Java. About the author Neil Madden has in-depth knowledge of applied cryptography, application security, and current API security technologies. He holds a Ph.D. in Computer Science. Table of Contents PART 1 - FOUNDATIONS 1 What is API security? 2 Secure API development 3 Securing the Natter API PART 2 - TOKEN-BASED AUTHENTICATION 4 Session cookie authentication 5 Modern token-based authentication 6 Self-contained tokens and JWTs PART 3 - AUTHORIZATION 7 OAuth2 and OpenID Connect 8 Identity-based access control 9 Capability-based security and macaroons PART 4 - MICROSERVICE APIs IN KUBERNETES 10 Microservice APIs in Kubernetes 11 Securing service-to-service APIs PART 5 - APIs FOR THE INTERNET OF THINGS 12 Securing IoT communications 13 Securing IoT APIs

Vert.x in Action teaches you how to build production-quality reactive applications in Java. This book covers core Vert.x concepts, as well as the fundamentals of asynchronous and reactive programming. Learn to develop microservices by using Vert.x tools for database communications, persistent messaging, and test app resiliency. The patterns and techniques included here transfer to reactive technologies and frameworks beyond Vert.x.Summary As enterprise applications become larger and more distributed, new architectural approaches like reactive designs, microservices, and event streams are required knowledge. The Vert.x framework provides a mature, rock-solid toolkit for building reactive applications using Java, Kotlin, or Scala. Vert.x in Action teaches you to build responsive, resilient, and scalable JVM applications with Vert.x using well-established reactive design patterns. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Vert.x is a collection of libraries for the Java virtual machine that simplify event-based and asynchronous programming. Vert.x applications handle tedious tasks like asynchronous communication, concurrent work, message and data persistence, plus they’re easy to scale, modify, and maintain. Backed by the Eclipse Foundation and used by Red Hat and others, this toolkit supports code in a variety of languages. About the book Vert.x in Action teaches you how to build production-quality reactive applications in Java. This book covers core Vert.x concepts, as well as the fundamentals of asynchronous and reactive programming. Learn to develop microservices by using Vert.x tools for database communications, persistent messaging, and test app resiliency. The patterns and techniques included here transfer to reactive technologies and frameworks beyond Vert.x. What's inside     Building reactive services     Responding to external service failures     Horizontal scaling     Vert.x toolkit architecture and Vert.x testing     Deploying with Docker and Kubernetes About the reader For intermediate Java web developers. About the author Julien Ponge is a principal software engineer at Red Hat, working on the Eclipse Vert.x project. Table of Contents PART 1 - FUNDAMENTALS OF ASYNCHRONOUS PROGRAMMING WITH VERT.X 1 Vert.x, asynchronous programming, and reactive systems 2 Verticles: The basic processing units of Vert.x 3 Event bus: The backbone of a Vert.x application 4 Asynchronous data and event streams 5 Beyond callbacks 6 Beyond the event bus PART 2 - DEVELOPING REACTIVE SERVICES WITHT VERT.X 7 Designing a reactive application 8 The web stack 9 Messaging and event streaming with Vert.x 10 Persistent state management with databases 11 End-to-end real-time reactive event processing 12 Toward responsiveness with load and chaos testing 13 Final notes: Container-native Vert.x

Graph Databases in Action teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine! Key Features· Graph database fundamentals· An overview of the graph database ecosystem· Relational vs. graph database modelling· Querying graphs using Gremlin· Real-world common graph use cases For readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required. About the technology Graph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority. Dave Bechberger has extensive experience using graph databases as a product architect and a consultant. He's spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He's an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences. Josh Perryman is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.

To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting?and lucrative!?careers in some of today's hottest programming fields. Key Features· 2D and 3D vector math· Matrices and linear transformations· Core concepts from linear algebra· Calculus with one or more variables· Algorithms for regression, classification, and clustering· Interesting real-world examples Written for programmers with solid algebra skills (even if they need some dusting off). No formal coursework in linear algebra or calculus is required. About the technology Most businesses realize they need to apply data science and effective machine learning to gain and maintain a competitive edge. To build these applications, they need developers comfortable writing code and using tools steeped in statistics, linear algebra, and calculus. Math also plays an integral role in other modern applications like game development, computer graphics and animation, image and signal processing, pricing engines, and stock market analysis. Paul Orland is CEO of Tachyus, a Silicon Valley startup building predictive analytics software to optimize energy production in the oil and gas industry. As founding CTO, he led the engineering team to productize hybrid machine learning and physics models, distributed optimization algorithms, and custom web-based data visualizations. He has a B.S. in mathematics from Yale University and a M.S. in physics from the University of Washington.

Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems.

The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage. Azure Data Engineering teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system.

Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you'll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Key Features· Introduction to computer vision· Deep learning and neural network· Transfer learning and advanced CNN architectures· Image classification and captioning For readers with intermediate Python, math and machine learningskills. About the technology By using deep neural networks, AI systems make decisions based on their perceptions of their input data. Deep learning-based computer vision (CV) techniques, which enhance and interpret visual perceptions, makes tasks like image recognition, generation, and classification possible. Mohamed Elgendy is the head of engineering at Synapse Technology, a leading AI company that builds proprietary computer vision applications to detect threats at security checkpoints worldwide. Previously, Mohamed was an engineering manager at Amazon, where he developed and taught the deep learning for computer vision course at Amazon's Machine Learning University. He also built and managed Amazon's computer vision think tank, among many other noteworthy machine learning accomplishments. Mohamed regularly speaks at many AI conferences like Amazon's DevCon, O'Reilly's AI conference and Google's I/O.

We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

Turn the corner from ?Haskell student? to ?Haskell developer.? Haskell in Depth explores the important language features and programming skills you'll need to build production-quality software using Haskell. And along the way, you'll pick up some interesting insights into why Haskell looks and works the way it does. Get ready to go deep! Haskell in Depth is the perfect second book on Haskell. After a quick refresher on Haskell basics, this hands-on guide dives into examples and application scenarios designed to teach how Haskell works and how to apply it correctly. You'll learn about managing projects with Cabal and Stack, tackle error-handling and testing, and package programs and libraries for production deployment. Key Features· Organizing your projects with Cabal and Stack· Testing and profiling· Working with data· Building web services and networking apps· Using the sophisticated libraries like lens, vinyl, and servant Written for developers familiar with Haskell basics. About the technology As software becomes more complex, it's essential to program efficiently using tools and techniques that guarantee your applications will run correctly, grow easily, and last a long time. Haskell is a functional programming language that blends a mathematically-rigorous approach to software design with a tested ecosystem of tools and libraries you can use to build deployable applications.

Unit Testing: Principles, Patterns and Practices shows you how to refine your existing unit tests by implementing modern best practices. YouGÇÖll learn to spot which tests are performing, which need refactoring, and which need to be deleted entirely! Upgrade your testing suite with new testing styles, good patterns, and reliable automated testing. Unit Testing: Principles, Practices and Patterns is a practical guide to modern unit testing best practices. Microsoft MVP Vladimir Khorikov takes you hands-on with examples of the ideal unit test and unit testing practices, building your skills step by step on a solid foundation. Key Features -+ A universal frame of reference by which to assess any unit test -+ Common anti-patterns to identify and avoid -+ Guidelines on how to refactor a test suite along with the -+ production code it covers -+ Using integration tests to verify the behavior of the system as a whole For readers who know the basics of unit testing and want to improve their techniques to get the best return on their efforts. Examples are in C# but apply in any language About the technology Test automation has turned unit testing into a standard practice in software development. The right unit test suite gives great outcomes and is vital to delivering quality software. Author Bio Vladimir Khorikov is an author, blogger, and Microsoft MVP. He has been developing software professionally for over ten years, and has mentored numerous teams on the ins and outs of unit testing.

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you'll learn to take advantage of Spark's core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists, Spark in Action, Second Edition is designed for data engineers and software engineers who want to master data processing using Spark without having to learn a complex new ecosystem of languages and tools. You'll instead learn to apply your existing Java and SQL skills to take on practical, real-world challenges. Key Features· Lots of examples based in the Spark Java APIs using real-life dataset and scenarios · Examples based on Spark v2.3 Ingestion through files, databases, and streaming · Building custom ingestion process · Querying distributed datasets with Spark SQL For beginning to intermediate developers and data engineers comfortable programming in Java. No experience with functional programming, Scala, Spark, Hadoop, or big data is required. About the technology Spark is a powerful general-purpose analytics engine that can handle massive amounts of data distributed across clusters with thousands of servers. Optimized to run in memory, this impressive framework can process data up to 100x faster than most Hadoop-based systems. Author BioAn experienced consultant and entrepreneur passionate about all things data, Jean-Georges Perrin was the first IBM Champion in France, an honor he's now held for ten consecutive years. Jean-Georges has managed many teams of software and data engineers.

Hello World! Third Edition is a fun, easy-to-use guide with copious illustrations and engaging examples. It takes the reader on a playful tour of basic programming concepts and then puts those concepts together to make fun and useful programs. It uses Python, a programming language that is one of the easiest to read, write, and understand.Like the previous two editions, Hello World! Third Edition is not just for kids. While the tone is light and engaging, it doesn't "talk down" to the reader, and beginners of any age will love its readability and sense of humor. Written by Warren Sande and his son, Carter, it is full of examples that will get you thinking and learning. Reviewed by professional educators, this book is kid-tested and parent-approved. You don't need to know anything about programming to use the book, just the basics of using a computer. If you can start a program and save a file, you can learn to program using this book!Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Object Design Style Guide captures dozens of techniques for creating pro-quality OO code that can stand the test of time. Examples are in an instantly-familiar pseudocode, teaching techniques you can apply to any OO language, from C++ to PHP. The design rules for different types of objectsBest practices for naming objectsTesting an object's behavior instead of its implementationExercises for each chapter to test your design skills

With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the tools library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level!Key features• An introduction to functional and parallel programming • Data science workflow • Profiling code for better performance • Fulfilling different quality objectives for a single unifying task • Python multiprocessing • Practical exercises including full-scale distributed applicationsAudienceReaders should have intermediate Python programming skills.About the technologyPython is a data scientist's dream-come-true, thanks to readily available libraries that support tasks like data analysis, machine learning, visualization, and numerical computing.

Practices of the Python Pro teaches you to design and write software that's easy to understand, maintain, and scale. Using practical Python-based examples, experienced developer Dane Hillard introduces design concepts that lay the foundation for designing, building, and maintaining software that's good for the long haul. You'll learn established guidelines for clean coding style and explore techniques to address design considerations like speed, integrity, security, and extensibility. Organizing large Python projectsLeveraging abstraction for clean codeWriting flexible, reusable codeInheritance and compositionConsiderations for testing and performance

Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, (NLP) expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key featuresAn overview of NLP and deep learning• Models for textual similarity• Deep memory-based NLP • Semantic role labeling • Sequential NLPAudienceFor those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required.About the technologyNatural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learningStephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He's the technical coordinator of two large European Union-funded research security-related projects. He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

Type-related failures are common and can be very costly. Famously, in 1999, NASA's Mars Climate Orbiter burned up in the atmosphere because of an error that could have easily been prevented with typing. By taking advantage of the strong type systems available in most modern programming languages, you can eliminate whole classes of errors. Programming with Types teaches you type system techniques for writing software that's safe, correct, easy to test and maintain, and that practically documents itself. Master these techniques, and you may even help prevent an interstellar catastrophe!Key features• Designing functions that don't return values • An introduction to type systems and their benefits • Encoding text• Building data structures with arrays and references • Type composition • Function types • Type safety • Object-oriented programming with typesAudienceReaders need basic high school math skills as well as some programming experience in a mainstream programming language like Java, JavaScript, C#, or C++.About the technologyAs software grows in complexity, it's essential to adopt coding tools and strategies that enforce safety and correctness. A type system governs what types of data are allowable, requiring your code to accurately store, represent, and use its internal data.