Databaser

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

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

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

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

**From BBC presenter and journalist Ros Atkins, creator of the viral 'Ros Atkins on...' explainer videos and host of the forthcoming BBC Radio 4 podcast 'Communicating with Ros Atkins'**'A great read for polishing your communication skills' FORBES 'For all those who want their audiences to listen and understand' JEREMY BOWEN 'Precision, deftness and a calming expertise' THE TIMES Do you worry about holding people's attention during presentations?Are you unsure where to start when faced with writing an essay or report?Are you preparing for an interview and wondering how to get all your points across?Explanation - identifying and communicating what we want to say - is an art. And the BBC presenter and journalist Ros Atkins, creator of the viral 'Ros Atkins on...' explainer videos, is something of a master of the form. In this book, Ros shares the secrets he has learned from years of working in high-pressure newsrooms, identifying the ten elements of a good explanation and the seven steps you need to take to express yourself with clarity and impact.Whether at work, school, university or home, we all benefit from being able to articulate ourselves clearly. Filled with practical examples, The Art of Explanation is a must-read for anyone who wants to sharpen their communication skills.

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

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

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

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

Learn to research, plan, design, and test the UX of AI-powered products Unlock the future of design with UX for AI—your indispensable guide to not only surviving but thriving in a world powered by artificial intelligence. Whether you're a seasoned UX designer or a budding design student, this book offers a lifeline for navigating the new normal, ensuring you stay relevant, valuable, and indispensable to your organization. In UX for AI: A Framework for Designing AI-Driven Products, Greg Nudelman—a seasoned UX designer and AI strategist—delivers a battle-tested framework that helps you keep your edge, thrive in your design job, and seize the opportunities AI brings to the table. Drawing on insights from 35 real-world AI projects and acknowledging the hard truth that 85% of AI initiatives fail, this book equips you with the practical skills you need to reverse those odds. You'll gain powerful tools to research, plan, design, and test user experiences that seamlessly integrate human-AI interactions. From practical design techniques to proven user research methods, this is the essential guide for anyone determined to create AI products that not only succeed but set new standards of value and impact. Inside the book: Hands-on exercises: Build your confidence and skills with practice UX design tasks like Digital Twin and Value Matrix, which you can immediately apply to your own AI projects.Common AI patterns and best practices: Explore design strategies for LLMs (Large Language Models), search engines, copilots, and more.Proven user research strategies: Learn how to uncover user needs and behaviors in this brave new world of AI-powered design.Real-world case studies: See how simple, practical UX approaches have prevented multimillion-dollar failures and unlocked unprecedented value.Perfect for any UX designer working with AI-enabled and AI-driven products, UX for AI is also a must-read resource for designers-in-training and design students with an interest in artificial intelligence and contemporary design.

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

Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse-a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way.Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools.You'll learn how to:Visualize-create plots for data exploration and communication of resultsTransform-discover types of variables and the tools you can use to work with themImport-get data into R and in a form convenient for analysisProgram-learn R tools for solving data problems with greater clarity and easeCommunicate-integrate prose, code, and results with Quarto

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

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

Denna bok ger en introduktion till relationsdatabaser och en pedagogiskt upplagd genomgång av SQL-språket. Syftet är att förmedla kunskaper som behövs för att någorlunda kunna förstå en databas uppbyggnad och för att kunna ställa frågor mot databasen. Boken kan användas som kurslitteratur på databaskurser eller som en självstudiebok. Förftattarna är lärare vid högskolan i Luleå och har mångårig erfarenhet av undervisning i relationsdatabaser och SQL-språket.

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

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

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization - and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you're to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Seasoned Experts Alberto Ferrari and Marco Russo Help You Master DAX for Superior Business Intelligence Solutions

The Definitive Guide to DAX is an authoritative resource for mastering the DAX language, which is pivotal for creating measures for semantic models in Microsoft Power BI, Fabric, Analysis Services, and Excel. With years of experience since the inception of Power Pivot, the authors offer a comprehensive reference that navigates through the unique and complex concepts of DAX, making it an essential tool for BI professionals. This third edition updates you with the latest features and provides a framework for writing efficient DAX code, enhancing your ability to customize calculations in data models. You will gain the skills necessary to understand and apply advanced DAX concepts, structure code in user-defined functions, and leverage new calendars for time intelligence calculations.

By reading this book, you will:

Understand the foundational concepts of DAX and its unique language structure Master the use of evaluation contexts to enhance data analysis Utilize CALCULATE and CALCULATETABLE functions effectively Implement variables to simplify complex DAX expressions Leverage classic and new calendar based time intelligence functions Work with iterators to perform advanced calculations Apply visual calculations to improve report interactivity Create and manage calculation groups for dynamic reporting Handle complex hierarchies and relationships in data models Author DAX queries for testing and troubleshooting

About This Book

For BI professionals and Excel power users eager to deepen their understanding of DAX and enhance their data modeling capabilities with advanced techniques For data analysts and IT specialists seeking to optimize their use of Microsoft Power BI, Fabric, Analysis Services, and Excel for more efficient and insightful data analysis

The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himselfIn addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage mediaDiscusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projectsCovers advanced topics, including data monitoring and testingAlthough the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55

Beginning Oracle SQL is your introduction to the interactive query tools and specific dialect of SQL used with Oracle Database. These tools include SQL*Plus and SQL Developer. SQL*Plus is the one tool any Oracle developer or database administrator can always count on, and it is widely used in creating scripts to automate routine tasks. SQL Developer is a powerful, graphical environment for developing and debugging queries. Oracle's is possibly the most valuable dialect of SQL from a career standpoint. Oracle's database engine is widely used in corporate environments worldwide. It is also found in many government applications. Oracle SQL implements many features not found in competing products. No developer or DBA working with Oracle can afford to be without knowledge of these features and how they work, because of the performance and expressiveness they bring to the table. Written in an easygoing and example-based style, Beginning Oracle SQL is the book that will get you started down the path to successfully writing SQL statements and getting results from Oracle Database. Takes an example-based approach, with clear and authoritative explanations Introduces both SQL and the query tools used to execute SQL statements Shows how to create tables, populate them with data, and then query that data to generate business results

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack.You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions.You'll learn:What a data pipeline is and how it worksHow data is moved and processed on modern data infrastructure, including cloud platformsCommon tools and products used by data engineers to build pipelinesHow pipelines support analytics and reporting needsConsiderations for pipeline maintenance, testing, and alerting

When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for eachStorage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead LogDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patternsDatabase clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency

Data is getting bigger, arriving faster, and coming in varied formats-and it all needs to be processed at scale for analytics or machine learning. How can you process such varied data workloads efficiently? Enter Apache Spark. Updated to emphasize new features in Spark 2.4., this second edition shows data engineers and scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine-learning algorithms. Through discourse, code snippets, and notebooks, you'll be able to: Learn Python, SQL, Scala, or Java high-level APIs: DataFrames and Datasets Peek under the hood of the Spark SQL engine to understand Spark transformations and performance Inspect, tune, and debug your Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow Use open source Pandas framework Koalas and Spark for data transformation and feature engineering

With its lively, conversational tone and practical focus, this edition mixes applied and theoretical aspects for a solid introduction to cryptography and security, including the latest significant advancements in the field.

The professional programmer's Deitel® guide to Python® with introductory artificial intelligence case studies

Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today's most compelling, leading-edge computing technologies and programming in Python--one of the world's most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details.

In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you'll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you'll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more. You'll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more.

Features

500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more

Register your product to gain access to updated chapters and material, as well as downloads, future updates, and/or corrections as they become available. See inside book for more information.

Everything you need to know to apply data contracts and build a truly data-driven organization that harnesses quality data to deliver tangible business valuePurchase of the print or Kindle book includes a free PDF eBookKey FeaturesUnderstand data contracts and their power to resolving the problems in contemporary data platformsLearn how to design and implement a cutting-edge data platform powered by data contractsAccess practical guidance from the pioneer of data contracts to get expert insights on effective utilizationBook DescriptionDespite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value.With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products.By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.What you will learnGain insights into the intricacies and shortcomings of today's data architecturesUnderstand exactly how data contracts can solve prevalent data challengesDrive a fundamental transformation of your data culture by implementing data contractsDiscover what goes into a data contract and why it's importantDesign a modern data architecture that leverages the power of data contractsExplore sample implementations to get practical knowledge of using data contractsEmbrace best practices for the successful deployment of data contractsWho this book is forIf you’re a data engineer, data leader, architect, or practitioner thinking about your data architecture and looking to design one that enables your organization to get the most value from your data, this book is for you. Additionally, staff engineers, product managers, and software engineering leaders and executives will also find valuable insights.