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SQL for Data Science: Data Cleaning, Wrangling and Analytics with Relational Databases

1C Agda DataBase (SQL)
SQL for Data Science: Data Cleaning, Wrangling and Analytics with Relational Databases
Автор: Badia Antonio
Дата выхода: 2020
Издательство: Springer Nature
Количество страниц: 290
Размер файла: 1,7 МБ
Тип файла: PDF
Добавил: LibCoder
Оглавление
 CoverTable of ContentsChange HistoryBeta 9—June 9,....2021Beta 8—April 27,....2021Beta 7—March 11,....2021Beta 6—August 11,....2020Beta 5—June 5,....2020Beta 4—March 23,....2020Beta 3—February 9,....2020Beta 2—December 4,....2019Beta 1—October 30,....2019AcknowledgmentsSo You Want to Write Some Client-Side CodeBasic AssumptionsThe Tools We’ll UseHow This Book Is OrganizedLet’s Build an AppThe Sample CodePart I—Getting Started1. Getting Started with Client-Side RailsManaging State and Front-End DevelopmentConfiguring WebpackerUsing WebpackerWhat’s Next2. Hotwire and TurboThe Hotwire WayInstalling TurboWhat Is Turbo Drive?Adding Interactivity with Turbo FramesNavigating Outside a Turbo FrameExtending Our Page with Turbo StreamsTurbo Frames vs. Turbo StreamsLazy Loading a Turbo FrameWhat’s Next3. StimulusWhat Is Stimulus?Installing StimulusAdding Our First ControllerCreating an ActionAdding a TargetUsing ValuesAutomating Value ChangesStimulus Has ClassGoing GenericStimulus Quick ReferenceWhat’s Next4. ReactWhat Is React?Installing ReactAdding Our First ComponentComposing ComponentsConnecting to the PageInteractivity, State, and HooksSharing StateWhat’s Next5. Cascading Style SheetsBuilding CSS in webpackAdding CSS and Assets to webpackAnimating CSSAdding CSS TransitionsAnimating Turbo Streams with Animate.cssUsing CSS and React ComponentsWhat’s NextPart II—Going Deeper6. TypeScriptUsing TypeScriptUnderstanding Basic TypeScript TypesStatic vs. Dynamic TypingAdding Type Annotations to VariablesAdding Type Annotations to FunctionsAdding Type Annotations to ClassesDefining InterfacesType Checking Classes and InterfacesGetting Type Knowledge to TypeScriptWhat’s Next7. webpackUnderstanding Why webpack ExistsManaging Dependencies with YarnUnderstanding webpack ConfigurationWhat’s Next8. WebpackerWebpacker BasicsWriting Code Using WebpackerIntegrating Webpacker with FrameworksRunning webpackDeploying Webpacker in ProductionCustomizing WebpackerWhat’s NextPart III—Managing Servers and State9. Talking to the ServerUsing Stimulus to Manage FormsStimulus and AjaxUsing Data in StimulusAcquiring Data in React with useStateWhat’s Next10. Immediate Communication with ActionCableInstalling ActionCableTurbo Streams and ActionCableStimulus and ActionCableReact and ActionCableWhat’s Next11. Managing State in Stimulus CodeUsing Data Values for LogicObserving and Responding to DOM ChangesRendering CSS with Data AttributesWhat’s Next12. Managing State in ReactUsing ReducersUsing Context to Share StateAdding Asynchronous Events to ContextsWhat’s Next13. Using Redux to Manage StateInstalling and Using ReduxAdding Asynchronous Actions to ReduxWhat’s NextPart IV—Validating Your Code14. Validating Code with Advanced TypeScriptCreating Union TypesSpecifying Types with Literal TypesUsing Enums and Literal TypesBuilding Mapped Types and Utility TypesTypeScript Configuration OptionsDealing with StrictnessWhat’s Next15. Testing with CypressWhy Cypress?Installing CypressConfiguring Cypress and RailsWriting Our First TestUnderstanding How Cypress WorksWhat’s Next16. More Testing and TroubleshootingWriting More Cypress TestsTesting the Schedule FilterCypress and ReactCypress Utilities and APITroubleshootingWhat’s NextA1. Framework SwapThe All-Hotwire AppThe All-React AppComparisonIndex

Описание

Ниже — практический обзор по теме «data».

This textbook explains SQL within the contextof data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing.

The book is organized as follows. 

the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage.Chapter 2 gets into databases proper, explaining how relational databases organize data. Chapter 1 describes the data life cycle, i.e. Non-traditional data, like XML and text, are also covered.Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation.Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication.Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually usingit.This book is for anyone interested in data science and/or databases. All concepts are introduced intuitively and with a minimum of specialized jargon. It just demands a bit of computer fluency, butno specific background on databases or data analysis. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.

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автор — Badia Antonio, издательство Springer Nature, год выпуска 2020, 290 страниц.

О чём книга «SQL for Data Science: Data Cleaning, Wrangling and Analytics with Relational Databases»?

This textbook explains SQL within the contextof data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis.

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