diff --git a/.github/workflows/azure-static-web-apps-red-water-0103e7a0f.yml b/.github/workflows/azure-static-web-apps-red-water-0103e7a0f.yml index e7bcd591..4d4cb1f3 100644 --- a/.github/workflows/azure-static-web-apps-red-water-0103e7a0f.yml +++ b/.github/workflows/azure-static-web-apps-red-water-0103e7a0f.yml @@ -22,7 +22,7 @@ jobs: id: builddeploy uses: Azure/static-web-apps-deploy@v1 with: - azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_RED_BAY_0A991EC0F }} + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_RED_WATER_0103E7A0F }} repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments) action: "upload" ###### Repository/Build Configurations - These values can be configured to match your app requirements. ###### @@ -41,5 +41,5 @@ jobs: id: closepullrequest uses: Azure/static-web-apps-deploy@v1 with: - azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_RED_BAY_0A991EC0F }} + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_RED_WATER_0103E7A0F }} action: "close" diff --git a/.github/workflows/azure-static-web-apps-witty-beach-04b13e603.yml b/.github/workflows/azure-static-web-apps-witty-beach-04b13e603.yml new file mode 100644 index 00000000..816645d3 --- /dev/null +++ b/.github/workflows/azure-static-web-apps-witty-beach-04b13e603.yml @@ -0,0 +1,45 @@ +name: Azure Static Web Apps CI/CD + +on: + push: + branches: + - main + pull_request: + types: [opened, synchronize, reopened, closed] + branches: + - main + +jobs: + build_and_deploy_job: + if: github.event_name == 'push' || (github.event_name == 'pull_request' && github.event.action != 'closed') + runs-on: ubuntu-latest + name: Build and Deploy Job + steps: + - uses: actions/checkout@v2 + with: + submodules: true + - name: Build And Deploy + id: builddeploy + uses: Azure/static-web-apps-deploy@v1 + with: + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_WITTY_BEACH_04B13E603 }} + repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments) + action: "upload" + ###### Repository/Build Configurations - These values can be configured to match your app requirements. ###### + # For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig + app_location: "/quiz-app" # App source code path + api_location: "" # Api source code path - optional + output_location: "dist" # Built app content directory - optional + ###### End of Repository/Build Configurations ###### + + close_pull_request_job: + if: github.event_name == 'pull_request' && github.event.action == 'closed' + runs-on: ubuntu-latest + name: Close Pull Request Job + steps: + - name: Close Pull Request + id: closepullrequest + uses: Azure/static-web-apps-deploy@v1 + with: + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_WITTY_BEACH_04B13E603 }} + action: "close" diff --git a/1-Introduction/01-defining-data-science/README.md b/1-Introduction/01-defining-data-science/README.md index ddabfdb4..4c433437 100644 --- a/1-Introduction/01-defining-data-science/README.md +++ b/1-Introduction/01-defining-data-science/README.md @@ -8,7 +8,7 @@ [![Defining Data Science Video](images/video-def-ds.png)](https://youtu.be/beZ7Mb_oz9I) -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/0) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/0) ## What is Data? In our everyday life, we are constantly surrounded by data. The text you are reading now is data. The list of phone numbers of your friends in your smartphone is data, as well as the current time displayed on your watch. As human beings, we naturally operate with data by counting the money we have or by writing letters to our friends. @@ -153,7 +153,7 @@ Visit [`notebook.ipynb`](/1-Introduction/01-defining-data-science/notebook.ipynb -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1) ## Assignments diff --git a/1-Introduction/01-defining-data-science/translations/README.es.md b/1-Introduction/01-defining-data-science/translations/README.es.md index b69d0cf6..4eb88160 100644 --- a/1-Introduction/01-defining-data-science/translations/README.es.md +++ b/1-Introduction/01-defining-data-science/translations/README.es.md @@ -8,7 +8,7 @@ [![Video definiendo la ciencia de datos](../images/video-def-ds.png)](https://youtu.be/beZ7Mb_oz9I) -## [Cuestionario antes de la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/0) +## [Cuestionario antes de la lección](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/0) ## ¿Qué son los datos? En nuestra vida cotidiana estamos rodeados de datos. El texto que estás leyendo ahora mismo son datos. La lista de tus contactos en tu teléfono móvil son datos, como lo es la hora que muestra tu reloj. Como seres humanos, operamos naturalmente condatos como por ejemplo contando el dinero que tenemos o escribiendo cartas a nuestros amigos. @@ -159,7 +159,7 @@ Visite [`notebook.ipynb`](notebook.ipynb) para leer el código. También puedes -## [Cuestionario después de la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1) +## [Cuestionario después de la lección](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1) ## Tareas diff --git a/1-Introduction/01-defining-data-science/translations/README.hi.md b/1-Introduction/01-defining-data-science/translations/README.hi.md index a0608c5e..2e6589ee 100644 --- a/1-Introduction/01-defining-data-science/translations/README.hi.md +++ b/1-Introduction/01-defining-data-science/translations/README.hi.md @@ -8,7 +8,7 @@ [![डेटा विज्ञान वीडियो को परिभाषित करना](/1-Introduction/01-defining-data-science/images/video-def-ds.png)](https://youtu.be/beZ7Mb_oz9I) -## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/0) +## [प्री-लेक्चर क्विज](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/0) ## डेटा क्या है? अपने दैनिक जीवन में हम लगातार डेटा से घिरे रहते हैं। अभी आप जो पाठ पढ़ रहे हैं वह डेटा है। आपके स्मार्टफ़ोन में आपके मित्रों के फ़ोन नंबरों की सूची डेटा है, साथ ही आपकी घड़ी पर प्रदर्शित वर्तमान समय भी है। मनुष्य के रूप में, हम स्वाभाविक रूप से हमारे पास मौजूद धन की गणना करके या अपने मित्रों को पत्र लिखकर डेटा के साथ काम करते हैं। @@ -152,7 +152,7 @@ Visit [`notebook.ipynb`](/1-Introduction/01-defining-data-science/notebook.ipynb -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1) ## कार्य (Assignments) diff --git a/1-Introduction/01-defining-data-science/translations/README.ko.md b/1-Introduction/01-defining-data-science/translations/README.ko.md index 0f70e55d..be7ce42e 100644 --- a/1-Introduction/01-defining-data-science/translations/README.ko.md +++ b/1-Introduction/01-defining-data-science/translations/README.ko.md @@ -8,7 +8,7 @@ [![데이터 과학(Data Science) 정의 영상](../images/video-def-ds.png)](https://youtu.be/pqqsm5reGvs) -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/0) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/0) ## 데이터란 무엇인가? 일상 생활에서 우리는 항상 데이터에 둘러싸여 있습니다. 지금 당신이 읽고 있는 이 글, 당신의 스마트폰 안에 있는 친구들의 전화번호 목록도 데이터이며, 시계에 표시되는 현재 시간 역시 마찬가지입니다. 인간으로서 우리는 가지고 있는 돈을 세거나 친구들에게 편지를 쓰면서 자연스럽게 데이터를 조작합니다. @@ -153,7 +153,7 @@ -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1) ## 과제 diff --git a/1-Introduction/01-defining-data-science/translations/README.nl.md b/1-Introduction/01-defining-data-science/translations/README.nl.md index 64aed857..ca46a3ab 100644 --- a/1-Introduction/01-defining-data-science/translations/README.nl.md +++ b/1-Introduction/01-defining-data-science/translations/README.nl.md @@ -8,7 +8,7 @@ [![Defining Data Science Video](../images/video-def-ds.png)](https://youtu.be/beZ7Mb_oz9I) -## [Starttoets data science](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/0) +## [Starttoets data science](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/0) ## Wat is Data? In ons dagelijks leven zijn we voortdurend omringd door data. De tekst die je nu leest is data. De lijst met telefoonnummers van je vrienden op je smartphone is data, evenals de huidige tijd die op je horloge wordt weergegeven. Als mens werken we van nature met data, denk aan het geld dat we moeten tellen of door berichten te schrijven aan onze vrienden. @@ -152,7 +152,7 @@ Ga naar ['notebook.ipynb'](notebook.ipynb) om de code door te lezen. Je kunt de > Als je niet weet hoe je code in een Jupyter Notebook moet uitvoeren, kijk dan eens naar [dit artikel](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1) ## Opdrachten diff --git a/1-Introduction/01-defining-data-science/translations/README.pt-br.md b/1-Introduction/01-defining-data-science/translations/README.pt-br.md index 04b47d4a..62cbf7c3 100644 --- a/1-Introduction/01-defining-data-science/translations/README.pt-br.md +++ b/1-Introduction/01-defining-data-science/translations/README.pt-br.md @@ -8,7 +8,7 @@ [![Definindo Ciências de Dados](../images/video-def-ds.png)](https://youtu.be/pqqsm5reGvs) -## [Quiz pré-aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/0) +## [Quiz pré-aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/0) ## O que são Dados? Na nossa vida cotidiana, nós estamos constantemente cercados por dados. O texto que você está lendo agora é um dado, a lista de telefones dos seus amigos no seu celular é um dado, assim como o horário atual mostrado no seu relógio. Como seres humanos, nós operamos naturalmente com dados. contando o dinheiro que temos ou escrevendo cartas para os nossos amigos. @@ -153,7 +153,7 @@ Visite [`notebook.ipynb`](../notebook.ipynb) para ler o código. Você também p -## [Quiz pós-aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1) +## [Quiz pós-aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1) ## Tarefas diff --git a/1-Introduction/01-defining-data-science/translations/README.ru.md b/1-Introduction/01-defining-data-science/translations/README.ru.md index dd7c4b96..b51d1040 100644 --- a/1-Introduction/01-defining-data-science/translations/README.ru.md +++ b/1-Introduction/01-defining-data-science/translations/README.ru.md @@ -8,7 +8,7 @@ [![Defining Data Science Video](../images/video-def-ds.png)](https://youtu.be/beZ7Mb_oz9I) -## [Вступительный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/0) +## [Вступительный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/0) ## Что такое данные? В повседневной жизни мы окружены данными. Текст, который Вы в данный момент читаете, является данными, список номеров телефонов друзей в Вашем смартфоне является данными, также как и время на Ваших часах. Люди умеют оперировать даными естественным образом, считая деньги, которые у нас есть, или составляя письма нашим друзьям. @@ -165,7 +165,7 @@ > Если Вы не знаете, как запустить код в Jupyter Notebook, прочтите [данную статью](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). -## [Проверочный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1) +## [Проверочный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1) ## Домашнее задание diff --git a/1-Introduction/02-ethics/README.md b/1-Introduction/02-ethics/README.md index 235b1f90..b047e228 100644 --- a/1-Introduction/02-ethics/README.md +++ b/1-Introduction/02-ethics/README.md @@ -21,7 +21,7 @@ In this lesson, we'll explore the fascinating area of data ethics - from core co -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/2) 🎯 +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/2) 🎯 ## Basic Definitions @@ -247,7 +247,7 @@ Note that there remains an intangible gap between _compliance_ (doing enough to The latter requires [collaborative approaches to defining ethics cultures](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that build emotional connections and consistent shared values _across organizations_ in the industry. This calls for more [formalized data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organizations - allowing _anyone_ to [pull the Andon cord](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (to raise ethics concerns early in the process) and making _ethical assessments_ (e.g., in hiring) a core criteria team formation in AI projects. --- -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/3) 🎯 +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/3) 🎯 ## Review & Self Study Courses and books help with understanding core ethics concepts and challenges, while case studies and tools help with applied ethics practices in real-world contexts. Here are a few resources to start with. diff --git a/1-Introduction/02-ethics/translations/README.hi.md b/1-Introduction/02-ethics/translations/README.hi.md index d89d2c66..265dbc41 100644 --- a/1-Introduction/02-ethics/translations/README.hi.md +++ b/1-Introduction/02-ethics/translations/README.hi.md @@ -19,7 +19,7 @@ इस पाठ में, हम डेटा नैतिकता के आकर्षक क्षेत्र के बारे में सीखेंगे - मूल अवधारणाओं और चुनौतियों से लेकर केस-स्टडी और शासन जैसी एप्लाइड AI अवधारणाओं तक - जो डेटा और AI के साथ काम करने वाली समूह और संगठनों में नैतिकता संस्कृति स्थापित करने में मदद करते हैं । -## [पाठ से पहले की प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/2) 🎯 +## [पाठ से पहले की प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/2) 🎯 ## मूल परिभाषाएं @@ -243,7 +243,7 @@ बाद वाले को [नैतिक संस्कृतियों को परिभाषित करने के लिए सहयोगात्मक दृष्टिकोण](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-drive-approach-26f451afa29f) की आवश्यकता होती है, जो पूरे संगठनों में भावनात्मक संबंध और सुसंगत साझा मूल्यों का निर्माण करते हैं । यह संगठनों में अधिक [औपचारिक डेटा नैतिकता संस्कृतियों](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) की मांग करता है - _किसी_ को [एंडोन कॉर्ड को खींचने](https://en.wikipedia.org/wiki/Andon_(manufacturing)) की अनुमति देता है (इस प्रक्रिया में नैतिकता संबंधी चिंताओं को जल्दी उठाने के लिए) और एआई परियोजनाओं में _नैतिक मूल्यांकन_ (उदाहरण के लिए, भर्ती में) एक मुख्य मानदंड टीम गठन करना । --- -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/3) 🎯 +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/3) 🎯 ## समीक्षा और स्व अध्ययन पाठ्यक्रम और पुस्तकें मूल नैतिकता अवधारणाओं और चुनौतियों को समझने में मदद करती हैं, जबकि केस स्टडी और उपकरण वास्तविक दुनिया के संदर्भों में लागू नैतिकता प्रथाओं के साथ मदद करते हैं। शुरू करने के लिए यहां कुछ संसाधन दिए गए हैं। diff --git a/1-Introduction/02-ethics/translations/README.ko.md b/1-Introduction/02-ethics/translations/README.ko.md index bfc3d7d5..8dd32714 100644 --- a/1-Introduction/02-ethics/translations/README.ko.md +++ b/1-Introduction/02-ethics/translations/README.ko.md @@ -21,7 +21,7 @@ -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/2) 🎯 +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/2) 🎯 ## 기본 정의 @@ -247,7 +247,7 @@ _준수_ ("법규"를 충족하기 위해 충분히 노력함)와 (골화, 정 후자는 산업에서 _조직 전체적으로_ 정서적 연결과 일관된 공유 가치를 구축하는 [윤리 문화를 정의하기 위한 협력적 접근 방식](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f)이 필요합니다. 이것은 조직에서 더 많은 [공식화된 데이터 윤리 문화](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/)를 요구합니다. 이런 문화는 _누구나_ (프로세스 초기에 윤리 문제 제기를 위해) [Andon 강령을 사용하고](https://en.wikipedia.org/wiki/Andon_(manufacturing)) _윤리적 평가_ (예: 고용 시)를 AI 프로젝트의 핵심 기준 팀 구성으로 만듭니다. --- -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/3) 🎯 +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/3) 🎯 ## 복습 & 독학 과정과 책은 핵심 윤리 개념과 과제를 이해하는 데 도움이 되며, Case Study와 도구는 실제 상황에서 윤리 사항들을 적용하는 데 도움이 됩니다. 다음은 시작을 할 때 도움이 되는 몇가지 자료들입니다. diff --git a/1-Introduction/02-ethics/translations/README.nl.md b/1-Introduction/02-ethics/translations/README.nl.md index 039f4c53..f04df66f 100644 --- a/1-Introduction/02-ethics/translations/README.nl.md +++ b/1-Introduction/02-ethics/translations/README.nl.md @@ -21,7 +21,7 @@ In deze les verkennen we het fascinerende gebied van data-ethiek - van kernconce -## [Pre-college quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/2) 🎯 +## [Pre-college quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/2) 🎯 ## Basisdefinities @@ -243,7 +243,7 @@ Onthoud dat er een ongrijpbare kloof blijft tussen _compliance_ (genoeg doen om Dit laatste vereist [samenwerkingsbenaderingen voor het definiëren van ethische culturen](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) die emotionele verbindingen en consistente gedeelde waarden _over organisaties_ in de industrie. Dit vraagt ​​om meer [geformaliseerde data-ethiekculturen](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organisaties - waardoor _iedereen_ [aan het Andon-koord kan trekken](https:/ /en.wikipedia.org/wiki/Andon_(manufacturing)) (om ethische problemen vroeg in het proces aan de orde te stellen) en het maken van _ethische beoordelingen_ (bijvoorbeeld bij het aannemen) een kerncriterium voor teamvorming in AI-projecten. --- -## [Quiz voor na het college](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/3) 🎯 +## [Quiz voor na het college](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/3) 🎯 ## Review & Zelfstudie Cursussen en boeken helpen bij het begrijpen van kernconcepten en uitdagingen op het gebied van ethiek, terwijl casestudy's en hulpmiddelen helpen bij toegepaste ethische praktijken in echte contexten. Hier zijn een paar bronnen om mee te beginnen. diff --git a/1-Introduction/02-ethics/translations/README.pt-br.md b/1-Introduction/02-ethics/translations/README.pt-br.md index 61fc47c0..43f62992 100644 --- a/1-Introduction/02-ethics/translations/README.pt-br.md +++ b/1-Introduction/02-ethics/translations/README.pt-br.md @@ -21,7 +21,7 @@ Nessa aula, nós vamos explorar a área fascinante de ética dos dados - desde c -## [Quiz pré aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/2) 🎯 +## [Quiz pré aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/2) 🎯 ## Definição Básica @@ -246,7 +246,7 @@ Note que existe uma lacuna intangível entre _compliance_ (fazer o suficiente pa Este último requere [abordagens colaborativas para definir culturas éticas](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) que constrói conexões emocionais e valores compartilhados consistentes _em todas as organizações_ na indústria. Isso requere mais [culturas de ética de dados formalizadas](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) nas organizações - permitindo _qualquer um_ a [puxar o cordão Andom](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (para aumentar as preocupações éticas mais cedo no processo) e fazendo _avaliações éticas_ (ex. na contratação) um critério fundamental na formação de times em projetos de IA. --- -## [Quiz pós aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/3) 🎯 +## [Quiz pós aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/3) 🎯 ## Revisão e Autoestudo Cursos e livros ajudam a entender os conceitos essencias da ética, enquanto estudos de caso e ferramentas ajudam com práticas da ética aplicado em contextos do mundo real. Aqui estão alguns recursos para começar. diff --git a/1-Introduction/02-ethics/translations/README.ru.md b/1-Introduction/02-ethics/translations/README.ru.md index d15240c5..5cfcff23 100644 --- a/1-Introduction/02-ethics/translations/README.ru.md +++ b/1-Introduction/02-ethics/translations/README.ru.md @@ -21,7 +21,7 @@ -## [Вступительный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/2) 🎯 +## [Вступительный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/2) 🎯 ## Основные определения @@ -257,7 +257,7 @@ Решение этой проблемы кроется в [совместных подходах к определению этичных культур](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f), которые выстраивают эмоциональные связи и постоянные общие ценности _во всех организациях_ отрасли. Это требует более глубокой [формализации культуры в области этики данных](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) в организациях, позволяющей _любому_ [потянуть за ниточки](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (чтобы поднять вопрос этики на ранней стадии) и провести _оценку этичности_ (например, при найме на работу) основных критериев формирования команд в проектах с ИИ. --- -## [Проверочный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/3) 🎯 +## [Проверочный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/3) 🎯 ## Дополнительные источники Курсы и книги помогут Вам понять основные этические принципы и вызовы, а примеры из реальной практики помогут с прикладными вопросами этики в контексте реального мира. Вот некоторые ресурсы, с которых можно начать: diff --git a/1-Introduction/03-defining-data/README.md b/1-Introduction/03-defining-data/README.md index 85848c3f..72a90e37 100644 --- a/1-Introduction/03-defining-data/README.md +++ b/1-Introduction/03-defining-data/README.md @@ -8,7 +8,7 @@ Data is facts, information, observations and measurements that are used to make This lesson focuses on identifying and classifying data by its characteristics and its sources. -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/4) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/4) ## How Data is Described **Raw data** is data that has come from its source in its initial state and has not been analyzed or organized. In order to make sense of what is happening with a dataset, it needs to be organized into a format that can be understood by humans as well as the technology they may use to analyze it further. The structure of a dataset describes how it's organized and can be classified at structured, unstructured and semi-structured. These types of structure will vary, depending on the source but will ultimately fit in these three categories. ### Quantitative Data @@ -56,7 +56,7 @@ Kaggle is an excellent source of open datasets. Use the [dataset search tool](ht - Is the data quantitative or qualitative? - Is the data structured, unstructured, or semi-structured? -## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5) +## [Post-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/5) diff --git a/1-Introduction/03-defining-data/translations/README.es.md b/1-Introduction/03-defining-data/translations/README.es.md index 3b47621b..b0ebd2fd 100644 --- a/1-Introduction/03-defining-data/translations/README.es.md +++ b/1-Introduction/03-defining-data/translations/README.es.md @@ -8,7 +8,7 @@ Los datos son hechos, información, observaciones y mediciones que son usados pa Esta lección se enfoca en la identificación y clasificación de datos por sus características y sus fuentes. -## [Examen previo a la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/4) +## [Examen previo a la lección](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/4) ## Cómo se describen los datos Los **datos en crudo** son datos que provienen de su fuente en su estado inicial y estos no han sido analizados u organizados. Con el fin de que tenga sentido lo que sucede con un conjunto de datos, es necesario organizarlos en un formato que pueda ser entendido tanto por humanos como por la tecnología usada para analizarla a mayor detalle. La estructura de un conjunto de datos describe como está organizado y puede ser clasificado de forma estructurada, no estructurada y semi-estructurada. Estos tipos de estructuras podrían variar, dependiendo de la fuente pero finalmente caerá en una de estas categorías. ### Datos cuantitativos @@ -56,7 +56,7 @@ Kaggle es una fuente excelente de conjuntos de datos abiertos. Usa los [conjunto - ¿Los datos son cuantitativos o cualitativos? - ¿Los datos son estruturados, no estructurados o semi-estructurados? -## [Examen posterior a la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5) +## [Examen posterior a la lección](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/5) diff --git a/1-Introduction/03-defining-data/translations/README.hi.md b/1-Introduction/03-defining-data/translations/README.hi.md index ecf49a11..d9c0f043 100644 --- a/1-Introduction/03-defining-data/translations/README.hi.md +++ b/1-Introduction/03-defining-data/translations/README.hi.md @@ -9,7 +9,7 @@ यह पाठ डेटा को उसके स्त्रोत के हिसाब से पहचानने और वर्गीकृत करने पर केंद्रित है। -## [पाठ के पूर्व की परीक्षा](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/4) +## [पाठ के पूर्व की परीक्षा](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/4) ## डेटा का वर्णन कैसे किया जाता है **अपरीपक्व डेटा** ऐसे प्रकार का डेटा होता जो उसके स्त्रोत से आते वक्त जिस अवस्था में था वैसे ही है और उसका विश्लेषण या वर्गीकरण नहीं किया गया है। ऐसे डेटासेट से जरूरी जानकारी निकलने के लिए उसे ऐसे प्रकार मे लाना आवश्यक है जो इंसान समझ सके और जिस तंत्रज्ञान का उपयोग डेटा के विश्लेषण में किया जाएगा उसको भी समझ आये। डेटाबेस की संरचना हमें बताती है कि डेटा किस प्रकार से वर्गीकृत किया गया है और उसका संरचित, मिश्र संरचित और असंरचित प्रकार में वर्गीकरण कैसे किया जाता है। संरचना के प्रकार डेटा के स्त्रोत के अनुसार बदल सकते हैं मगर आखिर में इन तीनों में से एक प्रकार के हो सकते हैं। @@ -54,7 +54,7 @@ Kaggle यह के मुक्त डेटाबेस का बहुत - डेटा परिमाणात्मक है या गुणात्मक है? - डेटा संरचित, असंरचित या फिर मिश्र संरचित है? -## [पाठ के पश्चात परीक्षा](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5) +## [पाठ के पश्चात परीक्षा](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/5) ## समीक्षा और स्वअध्ययन - माइक्रोसॉफ्ट लर्न का [अपना डेटा वर्गीकृत करें](https://docs.microsoft.com/en-us/learn/modules/choose-storage-approach-in-azure/2-classify-data) पाठ संरचित, असंरचित और मिश्र संरचित डेटा के बारे में और अच्छे से बताता है। diff --git a/1-Introduction/03-defining-data/translations/README.ko.md b/1-Introduction/03-defining-data/translations/README.ko.md index 9995df33..0cf729e5 100644 --- a/1-Introduction/03-defining-data/translations/README.ko.md +++ b/1-Introduction/03-defining-data/translations/README.ko.md @@ -8,7 +8,7 @@ 이 단원에서는 데이터의 특성과 소스를 기준으로 데이터를 식별하고 분류하는 데 중점을 둡니다. -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/4) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/4) ## 데이터 설명 방법 **원시 데이터**는 초기 상태의 소스에서 가져온, 분석이나 구조화되지 않은 데이터입니다. 데이터셋에서 무슨 일이 일어나고 있는지 이해하기 위해서는 데이터셋를 인간이 이해할 수 있는 형식과 추가 분석에 사용할 수 있는 기술로 구성해야 합니다. 데이터셋의 구조는 구성 방법을 설명하고 구조화, 비구조화 및 반구조화로 분류할 수 있습니다. 이러한 유형의 구조는 출처에 따라 다르지만 궁극적으로 이 세 가지 범주에 맞습니다. ### 정량적 데이터 @@ -56,7 +56,7 @@ Kaggle은 공개 데이터셋의 훌륭한 소스입니다. [데이터셋 검색 - 데이터는 양적입니까, 질적입니까? - 데이터가 정형, 비정형 또는 반정형입니까? -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/5) diff --git a/1-Introduction/03-defining-data/translations/README.pt-br.md b/1-Introduction/03-defining-data/translations/README.pt-br.md index 00ed7ba0..9ea39b41 100644 --- a/1-Introduction/03-defining-data/translations/README.pt-br.md +++ b/1-Introduction/03-defining-data/translations/README.pt-br.md @@ -8,7 +8,7 @@ Dados são fatos, informações, observações e medidas que são usadas para fa Essa aula irá focar em identificar e classificar dados baseados em sua características e fontes. -## [Quiz Pré Aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/4) +## [Quiz Pré Aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/4) ## Como Dados são Descritos **Dados Brutos (Raw data)** são dados que vieram em seu estado inicial de sua fonte e não foram analisados ou organizados. Para entender o que está acontecendo com um conjunto de dados, é necessário organizar os dados em um formato que possa ser entendido pelos humanos e também pela tecnologia que pode ser usada para analisar os mesmos. A estrutura do dataset descreve como estão organizados e pode ser classificada em estruturada, não estruturada e semi estruturada. Esses tipos de estruturas irão variar, dependendo da fonte mas irão ultimamente se encaixar nessas categorias. @@ -54,7 +54,7 @@ O Kaggle é uma excelente fonte para datasets abertos. Use a [ferramenta de busc - Os dados são quantitativos ou qualitativos? - Os dados são estruturados, não estruturados, ou semi estruturados? -## [Quiz Pós Aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5) +## [Quiz Pós Aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/5) diff --git a/1-Introduction/03-defining-data/translations/README.ru.md b/1-Introduction/03-defining-data/translations/README.ru.md index 89e8a45d..e47ba4e2 100644 --- a/1-Introduction/03-defining-data/translations/README.ru.md +++ b/1-Introduction/03-defining-data/translations/README.ru.md @@ -9,7 +9,7 @@ В данном уроке мы сосредоточимся на описании и классификации данных по их характеристикам и источникам. -## [Вступительный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/4) +## [Вступительный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/4) ## Классификация данных **"Сырые" данные** - это необработанные данные, полученные из источника без дополнительного анализа или организации. Для того, чтобы понять, что содержит в себе датасет, необходимо привести данные к формату, одинаково понятному как человеку, так и методам, которые могут быть использованы при их анализе. Структура датасета характеризует его содержание, которое делится на структурированные, неструктурированные и полуструктурированные данные. Эти типы структуры могут изменяться в зависимости от источника, но в конечном счёте все равно принадлежат одной из трёх упомянутых категорий. @@ -65,7 +65,7 @@ - Являются ли данные структурированными, неструктурированными, полуструктурированными? -## [Проверочный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5) +## [Проверочный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/5) ## Материалы для самостоятельного изучения diff --git a/1-Introduction/03-defining-data/translations/README.tr.md b/1-Introduction/03-defining-data/translations/README.tr.md index 7532ae7a..3fcc149f 100644 --- a/1-Introduction/03-defining-data/translations/README.tr.md +++ b/1-Introduction/03-defining-data/translations/README.tr.md @@ -8,7 +8,7 @@ Veri, keşifler yapmak ve bilinçli kararları desteklemek için kullanılan ger Bu ders veriyi karakteristiklerine ve kaynaklarına göre tanımlama ve sınıflandırma üzerine odaklanmaktadır. -## [Ders Öncesi Kısa Sınavı](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/4) +## [Ders Öncesi Kısa Sınavı](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/4) ## Veri nasıl tanımlanır @@ -65,7 +65,7 @@ Kriterler: - Bu veri nicel midir yoksa nitel midir? - Bu veri yapısal mıdır, yapısal değil midir yoksa yarı yapısal mıdır? -## [Ders Sonu Kısa Sınavı](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5) +## [Ders Sonu Kısa Sınavı](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/5) ## İnceleme & Öz Çalışma diff --git a/1-Introduction/04-stats-and-probability/README.md b/1-Introduction/04-stats-and-probability/README.md index 7b79d23c..ac801cb2 100644 --- a/1-Introduction/04-stats-and-probability/README.md +++ b/1-Introduction/04-stats-and-probability/README.md @@ -9,7 +9,7 @@ Statistics and Probability Theory are two highly related areas of Mathematics th [![Intro Video](images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw) -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/6) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/6) ## Probability and Random Variables @@ -244,7 +244,7 @@ Use the sample code in the notebook to test other hypothesis that: 2. First basemen are taller than third basemen 3. Shortstops are taller than second basemen -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/7) ## Review & Self Study diff --git a/1-Introduction/04-stats-and-probability/translations/README.hi.md b/1-Introduction/04-stats-and-probability/translations/README.hi.md index 65c27745..e2d4733b 100644 --- a/1-Introduction/04-stats-and-probability/translations/README.hi.md +++ b/1-Introduction/04-stats-and-probability/translations/README.hi.md @@ -9,7 +9,7 @@ [![Intro Video](/1-Introduction/04-stats-and-probability/images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw) -## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/6) +## [प्री-लेक्चर क्विज](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/6) ## प्रायिकता और यादृच्छिक चर @@ -249,7 +249,7 @@ array([[1. , 0.52959196], 2. पहले बेसमेन तीसरे बेसमेन से लम्बे होते हैं 3. शॉर्टस्टॉप दूसरे बेसमेन से लम्बे होते हैं -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/7) ## समीक्षा और आत्म अध्ययन diff --git a/1-Introduction/04-stats-and-probability/translations/README.ko.md b/1-Introduction/04-stats-and-probability/translations/README.ko.md index 77acb9b2..68406fca 100644 --- a/1-Introduction/04-stats-and-probability/translations/README.ko.md +++ b/1-Introduction/04-stats-and-probability/translations/README.ko.md @@ -9,7 +9,7 @@ [![인트로 영상](../images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw) -## [강의전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/6) +## [강의전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/6) ## 확률과 랜덤 변수 @@ -244,7 +244,7 @@ print(np.corrcoef(무게, 높이)) 2. 1루수는 3루수보다 키가 크다 3. 유격수는 2루수보다 키가 크다 -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/7) ## 복습 및 독학 diff --git a/1-Introduction/04-stats-and-probability/translations/README.pt-br.md b/1-Introduction/04-stats-and-probability/translations/README.pt-br.md index f70a160e..a92ce4b8 100644 --- a/1-Introduction/04-stats-and-probability/translations/README.pt-br.md +++ b/1-Introduction/04-stats-and-probability/translations/README.pt-br.md @@ -9,7 +9,7 @@ Teoria da Probabilidade e Estatística são duas áreas altamente relacionadas d [![Vídeo de Introdução](../images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw) -## [Quiz Pré Aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/6) +## [Quiz Pré Aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/6) ## Probabilidade e Variáveis Aleatórias @@ -242,7 +242,7 @@ Use o código de exemplo no notebook para testar outras hipóteses que: 2. Jogadores na primeira base e mais altos que jogadores na terceira base 3. Interbases (Shortstops) são maiores que jogadores na segunda base -## [Quis Pós Aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7) +## [Quis Pós Aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/7) ## Revisão e Autoestudo diff --git a/1-Introduction/04-stats-and-probability/translations/README.ru.md b/1-Introduction/04-stats-and-probability/translations/README.ru.md index 3493df13..a53c5d06 100644 --- a/1-Introduction/04-stats-and-probability/translations/README.ru.md +++ b/1-Introduction/04-stats-and-probability/translations/README.ru.md @@ -10,7 +10,7 @@ [![Вступительное видео](../images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw) -## [Вступительный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/6) +## [Вступительный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/6) ## Вероятность и случайная величина @@ -253,7 +253,7 @@ array([[1. , 0.52959196], 2. Игрок первой базы выше, чем игрок третьей 3. Шорт-стоп выше, чем игрок второй базы -## [Проверочный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7) +## [Проверочный тест](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/7) ## Материалы для самостоятельного изучения diff --git a/2-Working-With-Data/05-relational-databases/README.md b/2-Working-With-Data/05-relational-databases/README.md index e218068c..0212c211 100644 --- a/2-Working-With-Data/05-relational-databases/README.md +++ b/2-Working-With-Data/05-relational-databases/README.md @@ -6,7 +6,7 @@ Chances are you have used a spreadsheet in the past to store information. You had a set of rows and columns, where the rows contained the information (or data), and the columns described the information (sometimes called metadata). A relational database is built upon this core principle of columns and rows in tables, allowing you to have information spread across multiple tables. This allows you to work with more complex data, avoid duplication, and have flexibility in the way you explore the data. Let's explore the concepts of a relational database. -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/8) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/8) ## It all starts with tables @@ -166,7 +166,7 @@ There are numerous relational databases available on the internet. You can explo ## Post-Lecture Quiz -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/9) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/9) ## Review & Self Study diff --git a/2-Working-With-Data/05-relational-databases/translations/README.hi.md b/2-Working-With-Data/05-relational-databases/translations/README.hi.md index 71c60a04..0df7257f 100644 --- a/2-Working-With-Data/05-relational-databases/translations/README.hi.md +++ b/2-Working-With-Data/05-relational-databases/translations/README.hi.md @@ -6,7 +6,7 @@ संभावना है कि आपने जानकारी संग्रहीत करने के लिए अतीत में एक स्प्रेडशीट का उपयोग किया है। आपके पास पंक्तियों और स्तंभों का एक सेट था, जहाँ पंक्तियों में जानकारी (या डेटा) होती थी, और स्तंभों में जानकारी (कभी-कभी मेटाडेटा कहा जाता है) का वर्णन होता था। तालिकाओं में स्तंभों और पंक्तियों के इस मूल सिद्धांत पर एक संबंधपरक डेटाबेस बनाया गया है, जिससे आप कई तालिकाओं में जानकारी फैला सकते हैं। इससे आप अधिक जटिल डेटा के साथ काम कर सकते हैं, दोहराव से बच सकते हैं, और डेटा को एक्सप्लोर करने के तरीके में लचीलापन रख सकते हैं। आइए एक रिलेशनल डेटाबेस की अवधारणाओं का पता लगाएं। -## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/8) +## [प्री-लेक्चर क्विज](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/8) ## यह सब टेबल से शुरू होता है @@ -164,7 +164,7 @@ WHERE rainfall.year = 2019 इंटरनेट पर कई रिलेशनल डेटाबेस उपलब्ध हैं। आप ऊपर सीखे गए कौशल का उपयोग करके डेटा का पता लगा सकते हैं। -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/9) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/9) ## समीक्षा और आत्म अध्ययन diff --git a/2-Working-With-Data/05-relational-databases/translations/README.ko.md b/2-Working-With-Data/05-relational-databases/translations/README.ko.md index 8959f83b..3276e4fc 100644 --- a/2-Working-With-Data/05-relational-databases/translations/README.ko.md +++ b/2-Working-With-Data/05-relational-databases/translations/README.ko.md @@ -6,7 +6,7 @@ 과거에 스프레드 시트를 통해 정보를 저장한 경험이 있을 것입니다. 이는 행(rows)과 열(columns)을 가지고 있으며, 행(rows)에는 정보(혹은 데이터)를 나타내고 열(columns)에는 해당 정보(또는 메타데이터)를 정의합니다. 관계형 데이터베이스는 테이블의 행과 열의 핵심 원리를 기반으로 구축되며 여러 테이블에 정보를 분산시킬 수 있습니다. 이를 통해 더 복잡한 데이터를 다룰 수 있을 뿐만 아니라 중복을 방지하고, 데이터 탐색 방식에서 유연성을 가질 수 있습니다. 관계형 데이터베이스의 개념을 좀 더 살펴보겠습니다. -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/8) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/8) ## 모든 것의 시작 : 테이블(table) @@ -166,7 +166,7 @@ WHERE rainfall.year = 2019 ## 강의 후 퀴즈 -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/9) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/9) ## 리뷰 & 복습 diff --git a/2-Working-With-Data/06-non-relational/README.md b/2-Working-With-Data/06-non-relational/README.md index bb15e8fd..9b269546 100644 --- a/2-Working-With-Data/06-non-relational/README.md +++ b/2-Working-With-Data/06-non-relational/README.md @@ -4,7 +4,7 @@ |:---:| |Working with NoSQL Data - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/10) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/10) Data is not limited to relational databases. This lesson focuses on non-relational data and will cover the basics of spreadsheets and NoSQL. @@ -133,7 +133,7 @@ There is a `TwitterData.json` file that you can upload to the SampleDB database. Try to run a few select queries to find the documents that have Microsoft in the text field. Hint: try to use the [LIKE keyword](https://docs.microsoft.com/en-us/azure/cosmos-db/sql/sql-query-keywords#using-like-with-the--wildcard-character) -## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/11) +## [Post-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/11) diff --git a/2-Working-With-Data/06-non-relational/translations/README.hi.md b/2-Working-With-Data/06-non-relational/translations/README.hi.md index 3faa017a..b03d78ca 100644 --- a/2-Working-With-Data/06-non-relational/translations/README.hi.md +++ b/2-Working-With-Data/06-non-relational/translations/README.hi.md @@ -4,7 +4,7 @@ |:---:| |NoSQL डेटा के साथ काम करना - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/10) +## [प्री-लेक्चर क्विज](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/10) डेटा रिलेशनल डेटाबेस तक सीमित नहीं है। यह पाठ गैर-संबंधपरक डेटा पर केंद्रित है और इसमें स्प्रेडशीट और NoSQL की मूल बातें शामिल होंगी। @@ -131,7 +131,7 @@ NoSQL गैर-संबंधपरक डेटा को संग्रह टेक्स्ट फ़ील्ड में Microsoft वाले दस्तावेज़ ढूँढने के लिए कुछ चुनिंदा क्वेरीज़ चलाने का प्रयास करें। संकेत: [LIKE कीवर्ड](https://docs.microsoft.com/en-us/azure/cosmos-db/sql/sql-query-keywords#using-like-with-the--wildcard-character) का उपयोग करने का प्रयास करें -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/11) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/11) diff --git a/2-Working-With-Data/06-non-relational/translations/README.ko.md b/2-Working-With-Data/06-non-relational/translations/README.ko.md index 051bda99..2b97d374 100644 --- a/2-Working-With-Data/06-non-relational/translations/README.ko.md +++ b/2-Working-With-Data/06-non-relational/translations/README.ko.md @@ -4,7 +4,7 @@ |:---:| |데이터 처리: NoSQL 데이터 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/10) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/10) 데이터는 관계형 데이터베이스에만 국한되지 않습니다. 이 과정을 통해 비-관계형 데이터에 초점을 맞춰 스프레드시트와 NoSQL의 기초에 대해 설명하겠습니다. @@ -132,7 +132,7 @@ Cosmos DB 데이터베이스는 "Not Only SQL"의 정의에 부합하며, 여기 텍스트 필드에 Microsoft가 있는 문서를 찾기 위해 몇 가지 쿼리를 실행해 보십시오. 힌트: [LIKE 키워드](https://docs.microsoft.com/en-us/azure/cosmos-db/sql/sql-query-keywords#using-like-with-the--wildcard-character)를 사용해 보십시오. -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/11) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/11) diff --git a/2-Working-With-Data/07-python/README.md b/2-Working-With-Data/07-python/README.md index ff14cd5d..235fa70e 100644 --- a/2-Working-With-Data/07-python/README.md +++ b/2-Working-With-Data/07-python/README.md @@ -26,7 +26,7 @@ We will focus on a few examples of data processing, instead of giving you full o -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/12) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/12) ## Tabular Data and Dataframes @@ -258,7 +258,7 @@ Whether you already have structured or unstructured data, using Python you can p -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/13) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/13) ## Review & Self Study diff --git a/2-Working-With-Data/07-python/translations/README.ko.md b/2-Working-With-Data/07-python/translations/README.ko.md index 940ab89d..1d49f03f 100644 --- a/2-Working-With-Data/07-python/translations/README.ko.md +++ b/2-Working-With-Data/07-python/translations/README.ko.md @@ -26,7 +26,7 @@ -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/12) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/12) ## 표 형식 데이터 및 데이터 프레임 @@ -257,7 +257,7 @@ df = pd.read_csv('file.csv') 이미 정형 데이터이든 비정형 데이터이든 관계없이 Python을 사용하여 데이터 처리 및 이해와 관련된 모든 단계를 수행할 수 있습니다. 아마도 가장 유연한 데이터 처리 방법일 것이며, 이것이 대부분의 데이터 과학자들이 Python을 기본 도구로 사용하는 이유입니다. 데이터 과학 여정에 대해 진지하게 생각하고 있다면 Python을 깊이 있게 배우는 것이 좋습니다! -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/13) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/13) ## 리뷰 & 복습 diff --git a/2-Working-With-Data/08-data-preparation/README.md b/2-Working-With-Data/08-data-preparation/README.md index b9061482..a266f65f 100644 --- a/2-Working-With-Data/08-data-preparation/README.md +++ b/2-Working-With-Data/08-data-preparation/README.md @@ -4,7 +4,7 @@ |:---:| |Data Preparation - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/14) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/14) @@ -317,7 +317,7 @@ letters numbers All of the discussed materials are provided as a [Jupyter Notebook](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/2-Working-With-Data/08-data-preparation/notebook.ipynb). Additionally, there are exercises present after each section, give them a try! -## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/15) +## [Post-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/15) diff --git a/2-Working-With-Data/08-data-preparation/translations/README.ko.md b/2-Working-With-Data/08-data-preparation/translations/README.ko.md index 480db086..f8f4d288 100644 --- a/2-Working-With-Data/08-data-preparation/translations/README.ko.md +++ b/2-Working-With-Data/08-data-preparation/translations/README.ko.md @@ -4,7 +4,7 @@ |:---:| |데이터 전처리 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/14) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/14) @@ -321,7 +321,7 @@ letters numbers 논의된 모든 자료는 [Jupyter Notebook](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/2-Working-With-Data/08-data-preparation/notebook.ipynb)으로 제공됩니다. 또한, 각 섹션 후에 연습 문제가 있으므로 시도해 보세요! -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/15) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/15) diff --git a/2-Working-With-Data/08-data-preparation/translations/README.tr.md b/2-Working-With-Data/08-data-preparation/translations/README.tr.md index 6bd2402e..39a2f465 100644 --- a/2-Working-With-Data/08-data-preparation/translations/README.tr.md +++ b/2-Working-With-Data/08-data-preparation/translations/README.tr.md @@ -4,7 +4,7 @@ |:---:| |Veriyi Hazırlamak - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [Ders Öncesi Kısa Sınavı](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/14) +## [Ders Öncesi Kısa Sınavı](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/14) @@ -317,7 +317,7 @@ letters numbers Konuştuğumuz bütün materyaller burada sağlanıyor [Jupyter Notebook](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb). Ek olarak, her bölümden sonra alıştırmalar var, bunları yapmayı deneyin! -## [Ders Sonu Kısa Sınavı](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/15) +## [Ders Sonu Kısa Sınavı](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/15) diff --git a/3-Data-Visualization/09-visualization-quantities/README.md b/3-Data-Visualization/09-visualization-quantities/README.md index 313dd261..e6fa0063 100644 --- a/3-Data-Visualization/09-visualization-quantities/README.md +++ b/3-Data-Visualization/09-visualization-quantities/README.md @@ -5,7 +5,7 @@ | Visualizing Quantities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | In this lesson you will explore how to use one of the many available Python libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can learn many interesting facts about local wildlife. -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/16) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/16) ## Observe wingspan with Matplotlib @@ -194,7 +194,7 @@ In this plot, you can see the range per bird category of the Minimum Length and ## 🚀 Challenge This bird dataset offers a wealth of information about different types of birds within a particular ecosystem. Search around the internet and see if you can find other bird-oriented datasets. Practice building charts and graphs around these birds to discover facts you didn't realize. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/17) ## Review & Self Study diff --git a/3-Data-Visualization/09-visualization-quantities/translations/README.es.md b/3-Data-Visualization/09-visualization-quantities/translations/README.es.md index 6e7a5c71..e376283a 100644 --- a/3-Data-Visualization/09-visualization-quantities/translations/README.es.md +++ b/3-Data-Visualization/09-visualization-quantities/translations/README.es.md @@ -5,7 +5,7 @@ | Visualización de cantidades - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | En esta lección explorarás cómo utilizar una de las muchas librerías de Python disponibles para aprender a crear interesantes visualizaciones relacionadas al concepto de cantidad. Utilizando un conjunto de datos limpios sobre las aves de Minnesota, podrás aprender muchos datos interesantes sobre la vida silvestre local. -## [Cuestionario previo](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/16) +## [Cuestionario previo](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/16) ## Observar la envergadura con Matplotlib @@ -196,7 +196,7 @@ En este gráfico, puedes ver el rango por categoría de ave de la longitud míni Este conjunto de datos sobre aves ofrece una gran cantidad de información sobre diferentes tipos de aves dentro de un ecosistema concreto. Busca en Internet y comprueba si puedes encontrar otros conjuntos de datos orientados a las aves. Practica la construcción de tablas y gráficos en torno a estas aves para descubrir datos que no conocías. -## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17) +## [Cuestionario posterior a la clase](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/17) ## Repaso y Autoestudio diff --git a/3-Data-Visualization/09-visualization-quantities/translations/README.hi.md b/3-Data-Visualization/09-visualization-quantities/translations/README.hi.md index f00d5d87..13783f5d 100644 --- a/3-Data-Visualization/09-visualization-quantities/translations/README.hi.md +++ b/3-Data-Visualization/09-visualization-quantities/translations/README.hi.md @@ -5,7 +5,7 @@ | विज़ुअलाइज़िंग मात्रा - _सकेटच्नोते करने वाला [@nitya](https://twitter.com/nitya)_ | इस पाठ में आप यह पता लगाएंगे कि मात्रा की अवधारणा के चारों ओर दिलचस्प विज़ुअलाइज़ेशन कैसे बनाएं, यह जानने के लिए कई उपलब्ध पायथन पुस्तकालयों में से एक का उपयोग कैसे करें। मिनेसोटा के पक्षियों के बारे में साफ किए गए डेटासेट का उपयोग करके, आप स्थानीय वन्यजीवों के बारे में कई रोचक तथ्य जान सकते हैं। -## [प्री-रीडिंग क्विज](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/16) +## [प्री-रीडिंग क्विज](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/16) ## माटप्लोटलिब के साथ पंखों का निरीक्षण करें @@ -194,7 +194,7 @@ plt.show() ## 🚀 चुनौती यह पक्षी डेटासेट एक विशेष पारिस्थितिकी तंत्र के भीतर विभिन्न प्रकार के पक्षियों के बारे में जानकारी का खजाना प्रदान करता है। इंटरनेट के चारों ओर खोजें और देखें कि क्या आप अन्य पक्षी-उन्मुख डेटासेट पा सकते हैं। उन तथ्यों की खोज करने के लिए इन पक्षियों के चारों ओर चार्ट और ग्राफ़ बनाने का अभ्यास करें जिन्हें आपने महसूस नहीं किया है। -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/17) ## समीक्षा और स्व अध्ययन diff --git a/3-Data-Visualization/09-visualization-quantities/translations/README.ko.md b/3-Data-Visualization/09-visualization-quantities/translations/README.ko.md index 017a0b73..4acac0e9 100644 --- a/3-Data-Visualization/09-visualization-quantities/translations/README.ko.md +++ b/3-Data-Visualization/09-visualization-quantities/translations/README.ko.md @@ -5,7 +5,7 @@ | 수량 시각화 - _제작자 : [@nitya](https://twitter.com/nitya)_ | 이 강의에서는 사용할 수 있는 많은 파이썬 라이브러리 중에 하나를 사용하여 수량 개념과 관련된 흥미로운 시각화를 만드는 방법을 알아봅니다. 여러분은 미네소타의 새들에 대한 정리된 데이터 세트를 사용하여, 지역 야생동물에 대한 많은 흥미로운 사실들을 배울 수 있습니다. -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/16) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/16) ## Matplotlib으로 날개 길이 관찰하기 @@ -193,7 +193,7 @@ plt.show() ## 🚀 도전 이 새 데이터 셋은 특정 생태계 내의 다양한 종류의 새에 대한 풍부한 정보를 제공합니다. 인터넷을 검색하여 다른 조류 지향 데이터 셋을 찾을 수 있는지 확인해 보세요. 여러분이 깨닫지 못한 사실을 발견하기 위해 이 새들에 대한 차트와 그래프를 만드는 연습을 하세요. -## [이전 강의 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17) +## [이전 강의 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/17) ## 복습 & 자기주도학습 diff --git a/3-Data-Visualization/09-visualization-quantities/translations/README.pt-br.md b/3-Data-Visualization/09-visualization-quantities/translations/README.pt-br.md index 2cc0d6c0..2fc8cd79 100644 --- a/3-Data-Visualization/09-visualization-quantities/translations/README.pt-br.md +++ b/3-Data-Visualization/09-visualization-quantities/translations/README.pt-br.md @@ -5,7 +5,7 @@ | Visualizando quantidades - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | Nesta aula você irá explorar como usar uma das muitas bibliotecas disponíveis no Python para aprender a criar visualizações interessantes relacionadas ao conceito de quantidade. Usando um dataset já limpo sobre aves de Minnesota, você pode aprender muitos fatos interessantes sobre a fauna selvagem local. -## [Quiz pré-aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/16) +## [Quiz pré-aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/16) ## Observando envergadura da asa com Matplotlib @@ -201,7 +201,7 @@ Neste gráfico, você pode ver o intervalo de comprimento mínimo e máximo por Este dataset de aves oferece uma riqueza de informações sobre os diferentes tipos de aves de um ecossistema particular. Tente achar na internet outros datasets com dados sobre aves. Pratique construir gráficos com eles e tente descobrir fatos que você ainda não havia percebido. -## [Quiz pós-aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17) +## [Quiz pós-aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/17) ## Revisão e autoestudo diff --git a/3-Data-Visualization/10-visualization-distributions/README.md b/3-Data-Visualization/10-visualization-distributions/README.md index a34e0eb9..39797ae3 100644 --- a/3-Data-Visualization/10-visualization-distributions/README.md +++ b/3-Data-Visualization/10-visualization-distributions/README.md @@ -6,7 +6,7 @@ In the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length. -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/18) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/18) ## Explore the birds dataset Another way to dig into data is by looking at its distribution, or how the data is organized along an axis. Perhaps, for example, you'd like to learn about the general distribution, for this dataset, of the maximum wingspan or maximum body mass for the birds of Minnesota. @@ -192,7 +192,7 @@ Perhaps it's worth researching whether the cluster of 'Vulnerable' birds accordi Histograms are a more sophisticated type of chart than basic scatterplots, bar charts, or line charts. Go on a search on the internet to find good examples of the use of histograms. How are they used, what do they demonstrate, and in what fields or areas of inquiry do they tend to be used? -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/19) ## Review & Self Study diff --git a/3-Data-Visualization/10-visualization-distributions/translations/README.es.md b/3-Data-Visualization/10-visualization-distributions/translations/README.es.md index b05b2084..1724f497 100644 --- a/3-Data-Visualization/10-visualization-distributions/translations/README.es.md +++ b/3-Data-Visualization/10-visualization-distributions/translations/README.es.md @@ -6,7 +6,7 @@ En la lección anterior, aprendiste algunos datos interesantes sobre un conjunto de datos acerca de las aves de Minnesota. Encontraste algunos datos erróneos visualizando los valores atípicos y observaste las diferencias entre las categorías de aves según su longitud máxima. -## [Cuestionario previo](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/18) +## [Cuestionario previo](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/18) ## Explora el conjunto de datos sobre aves Otra forma de profundizar en los datos es observar su distribución, o cómo se organizan los datos a lo largo de un eje. Quizás, por ejemplo, te gustaría conocer la distribución general para este conjunto de datos, de la envergadura máxima o la masa corporal máxima de las aves de Minnesota. @@ -182,7 +182,7 @@ Tal vez valga la pena investigar si la agrupación de aves "Vulnerables" según Los histogramas son un tipo de gráfico más sofisticado que los gráficos de dispersión básicos, los gráficos de barras o los gráficos de líneas. Haz una búsqueda en internet para encontrar buenos ejemplos del uso de histogramas. ¿Cómo se utilizan, qué demuestran y en qué campos o áreas de investigación suelen utilizarse? -## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19) +## [Cuestionario posterior a la clase](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/19) ## Repaso y Autoestudio diff --git a/3-Data-Visualization/10-visualization-distributions/translations/README.hi.md b/3-Data-Visualization/10-visualization-distributions/translations/README.hi.md index 2f54fa9e..51b743a0 100644 --- a/3-Data-Visualization/10-visualization-distributions/translations/README.hi.md +++ b/3-Data-Visualization/10-visualization-distributions/translations/README.hi.md @@ -6,7 +6,7 @@ In the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length. -## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/18) +## [प्री-लेक्चर क्विज](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/18) ## पक्षियों के डेटासेट का अन्वेषण करें डेटा में खुदाई करने का दूसरा तरीका इसके वितरण को देखना है, या डेटा को एक अक्ष के साथ कैसे व्यवस्थित किया जाता है। शायद, उदाहरण के लिए, आप इस डेटासेट के सामान्य वितरण के बारे में जानना चाहेंगे, मिनेसोटा के पक्षियों के लिए अधिकतम पंख या अधिकतम शरीर द्रव्यमान। @@ -180,7 +180,7 @@ sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationS हिस्टोग्राम बुनियादी स्कैटरप्लॉट, बार चार्ट या लाइन चार्ट की तुलना में अधिक परिष्कृत प्रकार के चार्ट हैं। हिस्टोग्राम के उपयोग के अच्छे उदाहरण खोजने के लिए इंटरनेट पर खोज करें। उनका उपयोग कैसे किया जाता है, वे क्या प्रदर्शित करते हैं, और किन क्षेत्रों या पूछताछ के क्षेत्रों में उनका उपयोग किया जाता है? -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/19) ## समीक्षा और स्व अध्ययन diff --git a/3-Data-Visualization/10-visualization-distributions/translations/README.ko.md b/3-Data-Visualization/10-visualization-distributions/translations/README.ko.md index 0925fc01..2ce184ef 100644 --- a/3-Data-Visualization/10-visualization-distributions/translations/README.ko.md +++ b/3-Data-Visualization/10-visualization-distributions/translations/README.ko.md @@ -6,7 +6,7 @@ 이전 수업에서, 미네소타의 새에 대한 데이터셋에 대해서 몇몇 흥미로운 사실들을 배웠습니다. 이상치를 시각화하면서 잘못된 데이터들을 발견하고 새들의 최대 길이에 따라 새 카테고리들의 차이를 살펴보았습니다. -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/18) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/18) ## 새 데이터셋 탐색하기 데이터를 자세히 조사하는 또 다른 방법은 데이터의 분포, 또는 데이터가 축에 따라 구성되는 방식을 살펴보는 것입니다. 예를 들어, 미네소타 새들의 최대 날개 길이나 최대 체중의 일반적인 분포에 대해 알고 싶을 수도 있습니다. @@ -182,7 +182,7 @@ sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationS 히스토그램은 기본 산점도, 막대 차트 또는 꺾은선형 차트보다 더 정교한 유형의 차트입니다. 히스토그램 사용의 좋은 예를 찾으려면 인터넷에서 검색해보세요. 어떻게 사용되고, 무엇을 입증하며, 어떤 분야나 조사 분야에서 사용되는 경향이 있습니까? -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/19) ## 복습 & 자기주도학습 diff --git a/3-Data-Visualization/10-visualization-distributions/translations/README.pt-br.md b/3-Data-Visualization/10-visualization-distributions/translations/README.pt-br.md index 76914ec7..9d82fa68 100644 --- a/3-Data-Visualization/10-visualization-distributions/translations/README.pt-br.md +++ b/3-Data-Visualization/10-visualization-distributions/translations/README.pt-br.md @@ -6,7 +6,7 @@ Na aula anterior, você aprendeu fatos interessantes sobre um dataset de aves de Minnesota. Você encontrou dados incorretos ao visualizar outliers e olhou as diferenças entre categorias de aves com base no seu comprimento máximo. -## [Quiz pré-aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/18) +## [Quiz pré-aula](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/18) ## Explorando o dataset de aves Outra forma de explorar os dados é olhar para sua distribuição, ou como os dados estão organizados ao longo do eixo. Por exemplo, talvez você gostaria de aprender sobre a distribuição geral, neste dataset, do máximo de envergadura (wingspan) ou máximo de massa corporal (body mass) das aves de Minnesota. @@ -187,7 +187,7 @@ Talvez valha a pena pesquisar mais a fundo se o cluster de aves vulneráveis ('V Histogramas são um tipo mais sofisticado de gráfico em relação a simples gráficos de dispersão, barras ou linhas. Pesquise na internet bons exemplos de uso de histogramas. Como eles são usados, o que eles demonstram e em quais áreas ou campos de pesquisa eles são usados. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/19) ## Revisão e autoestudo diff --git a/3-Data-Visualization/11-visualization-proportions/README.md b/3-Data-Visualization/11-visualization-proportions/README.md index 8cc7fbbb..c930a49e 100644 --- a/3-Data-Visualization/11-visualization-proportions/README.md +++ b/3-Data-Visualization/11-visualization-proportions/README.md @@ -12,7 +12,7 @@ In this lesson, you will use a different nature-focused dataset to visualize pro > 💡 A very interesting project called [Charticulator](https://charticulator.com) by Microsoft Research offers a free drag and drop interface for data visualizations. In one of their tutorials they also use this mushroom dataset! So you can explore the data and learn the library at the same time: [Charticulator tutorial](https://charticulator.com/tutorials/tutorial4.html). -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/20) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/20) ## Get to know your mushrooms 🍄 @@ -170,7 +170,7 @@ In this lesson, you learned three ways to visualize proportions. First, you need ## 🚀 Challenge Try recreating these tasty charts in [Charticulator](https://charticulator.com). -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/21) ## Review & Self Study diff --git a/3-Data-Visualization/11-visualization-proportions/translations/README.es.md b/3-Data-Visualization/11-visualization-proportions/translations/README.es.md index 77d59bf0..db8a91f7 100644 --- a/3-Data-Visualization/11-visualization-proportions/translations/README.es.md +++ b/3-Data-Visualization/11-visualization-proportions/translations/README.es.md @@ -12,7 +12,7 @@ En esta lección, utilizarás un conjunto de datos diferente centrado en la natu > 💡 Un proyecto muy interesante llamado [Charticulator](https://charticulator.com) de Microsoft Research ofrece una interfaz gratuita de arrastrar y soltar para las visualizaciones de datos. ¡En uno de sus tutoriales también utilizan este conjunto de datos de hongos! Así que puedes explorar los datos y aprender la biblioteca al mismo tiempo: [Tutorial de Charticulator](https://charticulator.com/tutorials/tutorial4.html). -## [Cuestionario previo](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/20) +## [Cuestionario previo](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/20) ## Conoce tus hongos 🍄 @@ -163,7 +163,7 @@ En esta lección, aprendiste tres maneras de visualizar proporciones. En primer ## 🚀 Desafío Intenta recrear estos sabrosos gráficos en [Charticulator](https://charticulator.com). -## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21) +## [Cuestionario posterior a la clase](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/21) ## Repaso y autoestudio diff --git a/3-Data-Visualization/11-visualization-proportions/translations/README.hi.md b/3-Data-Visualization/11-visualization-proportions/translations/README.hi.md index 77986a87..86613962 100644 --- a/3-Data-Visualization/11-visualization-proportions/translations/README.hi.md +++ b/3-Data-Visualization/11-visualization-proportions/translations/README.hi.md @@ -13,7 +13,7 @@ > 💡 माइक्रोसॉफ्ट अनुसंधान द्वारा [चार्टिकुलेटर](https://charticulator.com) नामक एक बहुत ही रोचक परियोजना डेटा विज़ुअलाइज़ेशन के लिए एक निःशुल्क ड्रैग एंड ड्रॉप इंटरफ़ेस प्रदान करती है। अपने एक ट्यूटोरियल में वे इस मशरूम डेटासेट का भी उपयोग करते हैं! तो आप एक ही समय में डेटा का पता लगा सकते हैं और पुस्तकालय सीख सकते हैं: [चार्टिकुलेटर ट्यूटोरियल](https://charticulator.com/tutorials/tutorial4.html)। -## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/20) +## [प्री-लेक्चर क्विज](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/20) ## अपने मशरूम को जानें 🍄 @@ -164,7 +164,7 @@ fig = plt.figure( ## 🚀 चुनौती इन स्वादिष्ट चार्ट को फिर से बनाने का प्रयास करें [चार्टिकुलेटर](https://charticulator.com). -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/21) ## समीक्षा और आत्म अध्ययन diff --git a/3-Data-Visualization/12-visualization-relationships/README.md b/3-Data-Visualization/12-visualization-relationships/README.md index ef8c02be..7b7f8cf6 100644 --- a/3-Data-Visualization/12-visualization-relationships/README.md +++ b/3-Data-Visualization/12-visualization-relationships/README.md @@ -10,7 +10,7 @@ This dataset of about 600 items displays honey production in many U.S. states. S It will be interesting to visualize the relationship between a given state's production per year and, for example, the price of honey in that state. Alternately, you could visualize the relationship between states' honey yield per colony. This year span covers the devastating 'CCD' or 'Colony Collapse Disorder' first seen in 2006 (http://npic.orst.edu/envir/ccd.html), so it is a poignant dataset to study. 🐝 -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/22) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/22) In this lesson, you can use Seaborn, which you have used before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn's `relplot` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', which allow the data scientist to better understand how variables relate to each other. @@ -164,7 +164,7 @@ Go, bees, go! ## 🚀 Challenge In this lesson, you learned a bit more about other uses of scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, maybe one you used prior to these lessons. Note how long they take to create and how you need to be careful about how many grids you need to draw using these techniques. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/23) ## Review & Self Study diff --git a/3-Data-Visualization/12-visualization-relationships/translations/README.es.md b/3-Data-Visualization/12-visualization-relationships/translations/README.es.md index c2239271..5473c73e 100644 --- a/3-Data-Visualization/12-visualization-relationships/translations/README.es.md +++ b/3-Data-Visualization/12-visualization-relationships/translations/README.es.md @@ -10,7 +10,7 @@ Este conjunto de datos, compuesto por unos 600 elementos, muestra la producción Será interesante visualizar la relación entre la producción de un estado determinado por año y, por ejemplo, el precio de la miel en ese estado. También se podría visualizar la relación entre la producción de miel por colonia de los estados. Este intervalo de años abarca el devastador "CCD" o "Colony Collapse Disorder" que se observó por primera vez en 2006 (http://npic.orst.edu/envir/ccd.html), por lo que es un conjunto de datos conmovedor para estudiar. 🐝 -## [Cuestionario previo](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/22) +## [Cuestionario previo](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/22) En esta lección, puedes utilizar Seaborn, que ya has utilizado anteriormente, como una buena librería para visualizar las relaciones entre las variables. Es especialmente interesante el uso de la función `relplot` de Seaborn, que permite realizar gráficos de dispersión y de líneas para visualizar rápidamente las '[relaciones estadísticas](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', que permiten al científico de datos comprender mejor cómo se relacionan las variables entre sí. @@ -163,7 +163,7 @@ Aunque no hay nada que salte a la vista en torno al año 2003, nos permite termi ## 🚀 Desafío En esta lección, has aprendido un poco más sobre otros usos de los gráficos de dispersión y las cuadrículas de líneas, incluyendo las cuadrículas de facetas. Desafíate a crear una cuadrícula de facetas utilizando un conjunto de datos diferente, tal vez uno que hayas utilizado antes de estas lecciones. Fíjate en el tiempo que se tarda en crearlas y en la necesidad de tener cuidado con el número de cuadrículas que necesitas dibujar utilizando estas técnicas. -## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23) +## [Cuestionario posterior a la clase](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/23) ## Repaso y autoestudio diff --git a/3-Data-Visualization/12-visualization-relationships/translations/README.hi.md b/3-Data-Visualization/12-visualization-relationships/translations/README.hi.md index 20086e9a..06c510ca 100644 --- a/3-Data-Visualization/12-visualization-relationships/translations/README.hi.md +++ b/3-Data-Visualization/12-visualization-relationships/translations/README.hi.md @@ -10,7 +10,7 @@ किसी दिए गए राज्य के प्रति वर्ष उत्पादन और, उदाहरण के लिए, उस राज्य में शहद की कीमत के बीच संबंधों की कल्पना करना दिलचस्प होगा। वैकल्पिक रूप से, आप प्रति कॉलोनी राज्यों की शहद उपज के बीच संबंधों की कल्पना कर सकते हैं। इस वर्ष की अवधि में विनाशकारी 'सीसीडी' या 'कॉलोनी पतन विकार' शामिल है जिसे पहली बार 2006 में देखा गया था (http://npic.orst.edu/envir/ccd.html), इसलिए यह अध्ययन करने के लिए एक मार्मिक डेटासेट है।🐝 -## [व्याख्यान पूर्व प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/22) +## [व्याख्यान पूर्व प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/22) इस पाठ में, आप सीबॉर्न का उपयोग कर सकते हैं, जिसका उपयोग आपने पहले किया है, चरों के बीच संबंधों की कल्पना करने के लिए एक अच्छे पुस्तकालय के रूप में। सीबॉर्न के `रिलप्लॉट` फ़ंक्शन का उपयोग विशेष रूप से दिलचस्प है जो स्कैटर प्लॉट्स और लाइन प्लॉट्स को जल्दी से '[सांख्यिकीय संबंध](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)' की कल्पना करने की अनुमति देता है, जो डेटा वैज्ञानिक को बेहतर ढंग से समझने की अनुमति दें कि चर एक दूसरे से कैसे संबंधित हैं। @@ -163,7 +163,7 @@ ax.figure.legend(); ## चुनौती इस पाठ में, आपने फैसेट ग्रिड सहित स्कैटरप्लॉट और लाइन ग्रिड के अन्य उपयोगों के बारे में कुछ और सीखा। किसी भिन्न डेटासेट का उपयोग करके फ़ैसिट ग्रिड बनाने के लिए स्वयं को चुनौती दें, शायद एक जिसे आपने इन पाठों से पहले उपयोग किया था। ध्यान दें कि उन्हें बनाने में कितना समय लगता है और इन तकनीकों का उपयोग करके आपको कितने ग्रिड बनाने की आवश्यकता है, इस बारे में आपको सावधान रहने की आवश्यकता है। -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/23) ## समीक्षा और आत्म अध्ययन diff --git a/3-Data-Visualization/12-visualization-relationships/translations/README.ko.md b/3-Data-Visualization/12-visualization-relationships/translations/README.ko.md index 0fb46d80..0b558d90 100644 --- a/3-Data-Visualization/12-visualization-relationships/translations/README.ko.md +++ b/3-Data-Visualization/12-visualization-relationships/translations/README.ko.md @@ -10,7 +10,7 @@ 예를 들어 해당 주의 연간 생산량과 해당 주의 꿀 가격 간의 관계를 시각화하는 것은 흥미로울 것입니다. 또는 각 주의 군집 당 꿀 생산량 간의 관계를 시각화할 수 있습니다. 올해에는 2006년(http://npic.orst.edu/envir/ccd.html)에 처음 발견된 파괴적인 'CCD' 또는 '봉군붕괴증후군'을 다루는데, 이것은 연구하기에 가슴 아픈 데이터 셋입니다. 🐝 -## [이전 강의 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/22) +## [이전 강의 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/22) 이 강의에서는 변수 간의 관계를 시각화하는 좋은 라이브러리로, 전에 사용했던 Seaborn을 사용할 수 있습니다. 특히 흥미로운 점은 산점도와 선 플롯이 '[통계적 관계](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)'를 빠르게 시각화할 수 있도록 해주는 Seaborn의 'relplot' 기능입니다. 'replot'은 데이터 과학자가 변수들이 서로 어떻게 관련되어 있는지 더 잘 이해할 수 있도록 합니다. @@ -164,7 +164,7 @@ ax.figure.legend(); ## 🚀 도전 이번 강의에서는 facet grid를 비롯한 산점도 및 꺾은선 그래프의 다른 용도에 대해 조금 더 알아봤습니다. 다른 데이터 셋(이 교육 전에 사용했을 수도 있습니다.)을 사용하여 facet grid를 만드는 데 도전해보세요. 이러한 기술을 사용하여 그리드를 만드는 데 걸리는 시간과 그리드를 몇 개 그려야 하는지 주의할 필요가 있습니다. -## [이전 강의 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23) +## [이전 강의 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/23) ## 복습 & 자기 주도 학습 diff --git a/3-Data-Visualization/13-meaningful-visualizations/README.md b/3-Data-Visualization/13-meaningful-visualizations/README.md index 4cd93603..a51f9fd0 100644 --- a/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -17,7 +17,7 @@ In this lesson, you will review: 5. How to build animated or 3D charting solutions 6. How to build a creative visualization -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/24) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/24) ## Choose the right chart type @@ -145,7 +145,7 @@ Run your app from the terminal (npm run serve) and enjoy the visualization! Take a tour of the internet to discover deceptive visualizations. How does the author fool the user, and is it intentional? Try correcting the visualizations to show how they should look. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/25) ## Review & Self Study diff --git a/3-Data-Visualization/13-meaningful-visualizations/translations/README.es.md b/3-Data-Visualization/13-meaningful-visualizations/translations/README.es.md index d38c9f13..95410607 100644 --- a/3-Data-Visualization/13-meaningful-visualizations/translations/README.es.md +++ b/3-Data-Visualization/13-meaningful-visualizations/translations/README.es.md @@ -17,7 +17,7 @@ En esta lección, revisarás: 5. Cómo construir soluciones de gráficos animados o en 3D 6. Cómo construir una visualización creativa -## [Cuestionario previo](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/24) +## [Cuestionario previo](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/24) ## Elegir el tipo de gráfico adecuado @@ -144,7 +144,7 @@ Recorre el objeto .json para capturar los datos "to" y "from" de las letras y co Date una vuelta por internet para descubrir visualizaciones engañosas. ¿Cómo engaña el autor al usuario, y, si es intencionado? Intenta corregir las visualizaciones para mostrar cómo deberían ser. -## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25) +## [Cuestionario posterior a la clase](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/25) ## Revisión y Autoestudio diff --git a/3-Data-Visualization/13-meaningful-visualizations/translations/README.hi.md b/3-Data-Visualization/13-meaningful-visualizations/translations/README.hi.md index d4eda82b..9fe495fb 100644 --- a/3-Data-Visualization/13-meaningful-visualizations/translations/README.hi.md +++ b/3-Data-Visualization/13-meaningful-visualizations/translations/README.hi.md @@ -17,7 +17,7 @@ 5. एनिमेटेड या 3डी चार्टिंग समाधान कैसे तैयार करें 6. क्रिएटिव विज़ुअलाइज़ेशन कैसे बनाएं -## [व्याख्यान पूर्व प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/24) +## [व्याख्यान पूर्व प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/24) ## सही चार्ट प्रकार चुनें @@ -145,7 +145,7 @@ भ्रामक विज़ुअलाइज़ेशन खोजने के लिए इंटरनेट का भ्रमण करें. लेखक उपयोगकर्ता को कैसे मूर्ख बनाता है, और क्या यह जानबूझकर किया गया है? विज़ुअलाइज़ेशन को यह दिखाने के लिए सही करने का प्रयास करें कि उन्हें कैसा दिखना चाहिए। -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/25) ## समीक्षा और आत्म अध्ययन diff --git a/3-Data-Visualization/R/09-visualization-quantities/README.md b/3-Data-Visualization/R/09-visualization-quantities/README.md index 768a18fb..afdc23f2 100644 --- a/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -4,7 +4,7 @@ | Visualizing Quantities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | In this lesson you will explore how to use some of the many available R packages libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can learn many interesting facts about local wildlife. -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/16) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/16) ## Observe wingspan with ggplot2 An excellent library to create both simple and sophisticated plots and charts of various kinds is [ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html). In general terms, the process of plotting data using these libraries includes identifying the parts of your dataframe that you want to target, performing any transforms on that data necessary, assigning its x and y axis values, deciding what kind of plot to show, and then showing the plot. @@ -206,7 +206,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + ## 🚀 Challenge This bird dataset offers a wealth of information about different types of birds within a particular ecosystem. Search around the internet and see if you can find other bird-oriented datasets. Practice building charts and graphs around these birds to discover facts you didn't realize. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/17) ## Review & Self Study diff --git a/3-Data-Visualization/R/10-visualization-distributions/README.md b/3-Data-Visualization/R/10-visualization-distributions/README.md index eba34c69..7ee991f0 100644 --- a/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -6,7 +6,7 @@ In the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length. -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/18) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/18) ## Explore the birds dataset Another way to dig into data is by looking at its distribution, or how the data is organized along an axis. Perhaps, for example, you'd like to learn about the general distribution, for this dataset, of the maximum wingspan or maximum body mass for the birds of Minnesota. @@ -158,7 +158,7 @@ ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + Histograms are a more sophisticated type of chart than basic scatterplots, bar charts, or line charts. Go on a search on the internet to find good examples of the use of histograms. How are they used, what do they demonstrate, and in what fields or areas of inquiry do they tend to be used? -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/19) ## Review & Self Study diff --git a/3-Data-Visualization/R/11-visualization-proportions/README.md b/3-Data-Visualization/R/11-visualization-proportions/README.md index cdaf51bb..c3a99a0d 100644 --- a/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -12,7 +12,7 @@ In this lesson, you will use a different nature-focused dataset to visualize pro > 💡 A very interesting project called [Charticulator](https://charticulator.com) by Microsoft Research offers a free drag and drop interface for data visualizations. In one of their tutorials they also use this mushroom dataset! So you can explore the data and learn the library at the same time: [Charticulator tutorial](https://charticulator.com/tutorials/tutorial4.html). -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/20) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/20) ## Get to know your mushrooms 🍄 @@ -167,7 +167,7 @@ In this lesson, you learned three ways to visualize proportions. First, you need ## 🚀 Challenge Try recreating these tasty charts in [Charticulator](https://charticulator.com). -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/21) ## Review & Self Study diff --git a/3-Data-Visualization/R/12-visualization-relationships/README.md b/3-Data-Visualization/R/12-visualization-relationships/README.md index 416e6e9e..a54b11fc 100644 --- a/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -10,7 +10,7 @@ This dataset of about 600 items displays honey production in many U.S. states. S It will be interesting to visualize the relationship between a given state's production per year and, for example, the price of honey in that state. Alternately, you could visualize the relationship between states' honey yield per colony. This year span covers the devastating 'CCD' or 'Colony Collapse Disorder' first seen in 2006 (http://npic.orst.edu/envir/ccd.html), so it is a poignant dataset to study. 🐝 -## [Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/22) +## [Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/22) In this lesson, you can use Seaborn, which you have used before, as a good library to visualize relationships between variables. Particularly interesting is the use of ggplot2's `ggplot`and `geom_point` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://ggplot2.tidyverse.org/)', which allow the data scientist to better understand how variables relate to each other. @@ -156,7 +156,7 @@ Go, bees, go! ## 🚀 Challenge In this lesson, you learned a bit more about other uses of scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, maybe one you used prior to these lessons. Note how long they take to create and how you need to be careful about how many grids you need to draw using these techniques. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/23) ## Review & Self Study diff --git a/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 46ed0169..01bc52eb 100644 --- a/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -17,7 +17,7 @@ In this lesson, you will review: 5. How to build animated or 3D charting solutions 6. How to build a creative visualization -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/24) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/24) ## Choose the right chart type @@ -145,7 +145,7 @@ Run your app from the terminal (npm run serve) and enjoy the visualization! Take a tour of the internet to discover deceptive visualizations. How does the author fool the user, and is it intentional? Try correcting the visualizations to show how they should look. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/25) ## Review & Self Study diff --git a/4-Data-Science-Lifecycle/15-analyzing/README.md b/4-Data-Science-Lifecycle/15-analyzing/README.md index 88e3c47d..2a29a55b 100644 --- a/4-Data-Science-Lifecycle/15-analyzing/README.md +++ b/4-Data-Science-Lifecycle/15-analyzing/README.md @@ -6,7 +6,7 @@ ## Pre-Lecture Quiz -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/28) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/28) Analyzing in the data lifecycle confirms that the data can answer the questions that are proposed or solving a particular problem. This step can also focus on confirming a model is correctly addressing these questions and problems. This lesson is focused on Exploratory Data Analysis or EDA, which are techniques for defining features and relationships within the data and can be used to prepare the data for modeling. @@ -40,7 +40,7 @@ You don’t have to wait until the data is thoroughly cleaned and analyzed to st All the topics in this lesson can help identify missing or inconsistent values, but Pandas provides functions to check for some of these. [isna() or isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) can check for missing values. One important piece of exploring for these values within your data is to explore why they ended up that way in the first place. This can help you decide on what [actions to take to resolve them](/2-Working-With-Data/08-data-preparation/notebook.ipynb). -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/27) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/27) ## Assignment diff --git a/4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md b/4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md index 829c5c0f..f4d9ddfc 100644 --- a/4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md +++ b/4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md @@ -6,7 +6,7 @@ ## 강의 전 퀴즈 -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/28) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/28) 데이터의 라이프사이클을 분석하면 데이터가 제안된 질문에 답하거나 특정 문제를 해결할 수 있음을 확인할 수 있습니다. 또한 이 단계는 모델이 이러한 질문과 문제를 올바르게 해결하는지 확인하는 데 초점을 맞출 수 있습니다. 이 과정에서는 데이터 내의 특징과 관계를 정의하는 기술이며 모델링을 위한 데이터를 준비하는 데 사용할 수 있는 탐색 데이터 분석(Exploratory Data Analysis) 또는 EDA에 초점을 맞춥니다. @@ -39,7 +39,7 @@ Pandas 라이브러리의 [`query()` 함수](https://pandas.pydata.org/pandas-do ## 불일치 식별을 위한 탐색 이 강의의 모든 주제는 누락되거나 일치하지 않는 값을 식별하는 데 도움이 될 수 있지만 Pandas는 이러한 값 중 일부를 확인하는 기능을 제공합니다. [isna() 또는 isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html)에서 결측값을 확인할 수 있습니다. 데이터 내에서 이러한 값을 탐구할 때 중요한 한 가지 요소는 처음에 이러한 값이 왜 이렇게 되었는지 이유를 탐구하는 것입니다. 이는 [문제 해결을 위해 취해야 할 조치](2-Working-With-Data\08-data-preparation/notebook.ipynb)를 결정하는 데 도움이 될 수 있습니다. -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/27) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/27) ## 과제 diff --git a/4-Data-Science-Lifecycle/16-communication/README.md b/4-Data-Science-Lifecycle/16-communication/README.md index a01170bd..f1641e75 100644 --- a/4-Data-Science-Lifecycle/16-communication/README.md +++ b/4-Data-Science-Lifecycle/16-communication/README.md @@ -4,7 +4,7 @@ |:---:| | Data Science Lifecycle: Communication - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/30) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/30) Test your knowledge of what's to come with the Pre-Lecture Quiz above! @@ -213,7 +213,7 @@ If Emerson took approach #2, it is much more likely that the team leads will tak -## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/31) +## [Post-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/31) Review what you've just learned with the Post-Lecture Quiz above! diff --git a/4-Data-Science-Lifecycle/16-communication/translations/README.hi.md b/4-Data-Science-Lifecycle/16-communication/translations/README.hi.md index 4be0e0b5..14b8b212 100644 --- a/4-Data-Science-Lifecycle/16-communication/translations/README.hi.md +++ b/4-Data-Science-Lifecycle/16-communication/translations/README.hi.md @@ -4,7 +4,7 @@ |:---:| | डेटा विज्ञान के जीवनचक्र: संचार - _[@nitya](https://twitter.com/nitya) द्वारा स्केचनोट_| -## [प्री-लेक्चर क्विज ](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/30) +## [प्री-लेक्चर क्विज ](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/30) ऊपर दिए गए प्री-लेक्चर क्विज़ के साथ क्या करना है, इसके बारे में अपने ज्ञान का परीक्षण करें! ### संचार क्या है? आइए इस पाठ की शुरुआत यह परिभाषित करते हुए करें कि संचार के साधन क्या हैं। **संचार करना सूचनाओं को संप्रेषित करना या उनका आदान-प्रदान करना है।** सूचना विचार, विचार, भावनाएं, संदेश, गुप्त संकेत, डेटा हो सकती है - कुछ भी जो एक **_प्रेषक_** (सूचना भेजने वाला) एक **_रिसीवर_** चाहता है ( जानकारी प्राप्त करने वाला कोई व्यक्ति) समझने के लिए। इस पाठ में, हम प्रेषकों को संचारक के रूप में और रिसीवर को श्रोता के रूप में संदर्भित करेंगे। @@ -166,7 +166,7 @@ - सार्थक शब्दों और वाक्यांशों का प्रयोग करें - भावना का प्रयोग करें -## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/31) +## [व्याख्यान के बाद प्रश्नोत्तरी](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/31) ### स्व अध्ययन के लिए अनुशंसित संसाधन [द फाइव सी ऑफ़ स्टोरीटेलिंग - आर्टिक्यूलेट पर्सुएशन](http://articulatepersuasion.com/the-five-cs-of-storytelling/) diff --git a/4-Data-Science-Lifecycle/16-communication/translations/README.ko.md b/4-Data-Science-Lifecycle/16-communication/translations/README.ko.md index dc1e9a08..093fa8e4 100644 --- a/4-Data-Science-Lifecycle/16-communication/translations/README.ko.md +++ b/4-Data-Science-Lifecycle/16-communication/translations/README.ko.md @@ -4,7 +4,7 @@ |:---:| | 데이터 사이언스 생활주기 : 소통 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/30) +## [강의 전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/30) 위의 사전 강의 퀴즈와 함께 제공되는 내용을 테스트해 보십시오! @@ -165,7 +165,7 @@ Emerson이 #2번 접근 방식을 택했다면, 팀 책임자는 Emerson이 의 - 의미 있는 단어와 구문을 사용 - 감정 사용 -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/31) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/31) ### 자습을 위한 추천 자료 [스토리텔링의 5대 C - 분명한 설득](http://articulatepersuasion.com/the-five-cs-of-storytelling/) diff --git a/5-Data-Science-In-Cloud/17-Introduction/README.md b/5-Data-Science-In-Cloud/17-Introduction/README.md index d8c9624f..4bdcad7c 100644 --- a/5-Data-Science-In-Cloud/17-Introduction/README.md +++ b/5-Data-Science-In-Cloud/17-Introduction/README.md @@ -8,7 +8,7 @@ In this lesson, you will learn the fundamental principles of the Cloud, then you will see why it can be interesting for you to use Cloud services to run your data science projects and we'll look at some examples of data science projects run in the Cloud. -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/32) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/32) ## What is the Cloud? @@ -92,7 +92,7 @@ Sources: ## Post-Lecture Quiz -[Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33) +[Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/33) ## Assignment diff --git a/5-Data-Science-In-Cloud/17-Introduction/translations/README.hi.md b/5-Data-Science-In-Cloud/17-Introduction/translations/README.hi.md index 2acd81dd..f5290874 100644 --- a/5-Data-Science-In-Cloud/17-Introduction/translations/README.hi.md +++ b/5-Data-Science-In-Cloud/17-Introduction/translations/README.hi.md @@ -8,7 +8,7 @@ इस पाठ में, आप क्लाउड के मूलभूत सिद्धांतों को जानेंगे, फिर आप देखेंगे कि आपके डेटा साइंस परियोजनाओं को चलाने के लिए क्लाउड सेवाओं का उपयोग करना आपके लिए दिलचस्प क्यों हो सकता है और हम क्लाउड में चलने वाले डेटा साइंस प्रोजेक्ट के कुछ उदाहरण देखेंगे। -## [प्री-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/32) +## [प्री-लेक्चर क्विज़](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/32) ## क्लाउड क्या है? @@ -93,7 +93,7 @@ ## पोस्ट-लेक्चर क्विज़ -[पोस्ट-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33) +[पोस्ट-लेक्चर क्विज़](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/33) ## असाइनमेंट diff --git a/5-Data-Science-In-Cloud/17-Introduction/translations/README.ko.md b/5-Data-Science-In-Cloud/17-Introduction/translations/README.ko.md index bb1bec70..ab9b901b 100644 --- a/5-Data-Science-In-Cloud/17-Introduction/translations/README.ko.md +++ b/5-Data-Science-In-Cloud/17-Introduction/translations/README.ko.md @@ -8,7 +8,7 @@ 이 강의에서는 클라우드의 기본 원칙을 배운 다음 클라우드 서비스를 사용하여 데이터 사이언스 프로젝트를 실행하는 것이 왜 흥미로운지 알게 되고, 클라우드에서 실행되는 데이터 사이언스 프로젝트들 중 몇가지 예시를 보게 될 것이다. -## [강의전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/32) +## [강의전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/32) ## 클라우드란? @@ -92,7 +92,7 @@ Dmitry는 COVID 논문을 분석하는 도구를 만들었습니다. 이 프로 ## 강의 후 퀴즈 -[강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33) +[강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/33) ## 과제 diff --git a/5-Data-Science-In-Cloud/17-Introduction/translations/README.ne.md b/5-Data-Science-In-Cloud/17-Introduction/translations/README.ne.md index af7f95a6..573ca3b7 100644 --- a/5-Data-Science-In-Cloud/17-Introduction/translations/README.ne.md +++ b/5-Data-Science-In-Cloud/17-Introduction/translations/README.ne.md @@ -8,7 +8,7 @@ यो पाठमा ,तपाईले क्लाउडको मूलभूत सिद्धांत सिक्नहुनेछ,अनि देख्नुहुनेछ कि तपाईले डाटा साइंस परियोजना चलाउनका लागि क्लाउड सेवाको उपयोग कत दिलचस्प हुन सक्छ । त्यसपछि हामी क्लाउडमा चल्ने वाला डाटा साइंस प्रोजेक्टको केही उदाहरण हेर्नेछौ। -## [प्री-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/32) +## [प्री-लेक्चर क्विज़](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/32) ## क्लाउड के हो? @@ -94,7 +94,7 @@ ## पोस्ट-लेक्चर क्विज़ -[पोस्ट-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33) +[पोस्ट-लेक्चर क्विज़](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/33) ## असाइनमेंट diff --git a/5-Data-Science-In-Cloud/18-Low-Code/README.md b/5-Data-Science-In-Cloud/18-Low-Code/README.md index 40965c93..ce431bfa 100644 --- a/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -27,7 +27,7 @@ Table of contents: - [Review & Self Study](#review--self-study) - [Assignment](#assignment) -## [Pre-Lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/34) +## [Pre-Lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/34) ## 1. Introduction ### 1.1 What is Azure Machine Learning? @@ -325,7 +325,7 @@ Congratulations! You just consumed the model deployed and trained it on Azure ML Look closely at the model explanations and details that AutoML generated for the top models. Try to understand why the best model is better than the other ones. What algorithms were compared? What are the differences between them? Why is the best one performing better in this case? -## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/35) +## [Post-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/35) ## Review & Self Study diff --git a/5-Data-Science-In-Cloud/18-Low-Code/translations/README.ko.md b/5-Data-Science-In-Cloud/18-Low-Code/translations/README.ko.md index 086f162b..1dc17046 100644 --- a/5-Data-Science-In-Cloud/18-Low-Code/translations/README.ko.md +++ b/5-Data-Science-In-Cloud/18-Low-Code/translations/README.ko.md @@ -27,7 +27,7 @@ - [리뷰&자습](#리뷰--자습) - [과제](#과제) -## [강의전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/34) +## [강의전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/34) ## 1. 소개 ### 1.1 Azure 기계 학습(Machine Learning)이란 무엇입니까? @@ -325,7 +325,7 @@ data = { AutoML이 상위 모델에 대해 생성한 모델 설명 및 세부정보를 자세히 살펴보세요. 최고의 모델이 다른 모델보다 나은 이유를 이해하려고 노력하십시오. 어떤 알고리즘이 비교되었습니까? 이들의 차이점은 무엇인가요? 이 경우 왜 최고 성능이 더 나은가요? -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/35) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/35) ## 복습 및 독학 diff --git a/5-Data-Science-In-Cloud/19-Azure/README.md b/5-Data-Science-In-Cloud/19-Azure/README.md index ab87e7ca..d9557e1a 100644 --- a/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/5-Data-Science-In-Cloud/19-Azure/README.md @@ -28,7 +28,7 @@ Table of contents: - [Review & Self Study](#review--self-study) - [Assignment](#assignment) -## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/36) +## [Pre-Lecture Quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/36) ## 1. Introduction @@ -288,7 +288,7 @@ Congratulations! You just consumed the model deployed and trained on Azure ML wi **HINT:** Go to the [SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-40229-cxa&ocid=AID3041109) and type keywords in the search bar like "Pipeline". You should have the `azureml.pipeline.core.Pipeline` class in the search results. -## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/37) +## [Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/37) ## Review & Self Study diff --git a/5-Data-Science-In-Cloud/19-Azure/translations/README.ko.md b/5-Data-Science-In-Cloud/19-Azure/translations/README.ko.md index 6d11ed69..191620e1 100644 --- a/5-Data-Science-In-Cloud/19-Azure/translations/README.ko.md +++ b/5-Data-Science-In-Cloud/19-Azure/translations/README.ko.md @@ -28,7 +28,7 @@ - [리뷰&자습](#리뷰--자습) - [과제](#과제) -## [강의전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/36) +## [강의전 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/36) ## 1. 소개 @@ -288,7 +288,7 @@ response **힌트:** [SDK 설명서](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-40229-cxa&ocid=AID3041109) 로 이동합니다. 검색창에 "파이프라인"과 같은 키워드를 입력합니다. 검색 결과에 `azureml.pipeline.core.Pipeline` 클래스가 있어야 합니다. -## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/37) +## [강의 후 퀴즈](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/37) ## 복습 및 독학 diff --git a/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 3934ac7b..448209d3 100644 --- a/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -11,7 +11,7 @@ We started with definitions of data science and ethics, explored various tools & In this lesson, we'll explore real-world applications of data science across industry and dive into specific examples in the research, digital humanities, and sustainability, contexts. We'll look at student project opportunities and conclude with useful resources to help you continue your learning journey! ## Pre-Lecture Quiz -[Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/38) +[Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/38) ## Data Science + Industry Thanks to the democratization of AI, developers are now finding it easier to design and integrate AI-driven decision-making and data-driven insights into user experiences and development workflows. Here are a few examples of how data science is "applied" to real-world applications across the industry: @@ -129,7 +129,7 @@ Here are some examples of data science student projects to inspire you. Search for articles that recommend data science projects that are beginner friendly - like [these 50 topic areas](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/) or [these 21 project ideas](https://www.intellspot.com/data-science-project-ideas) or [these 16 projects with source code](https://data-flair.training/blogs/data-science-project-ideas/) that you can deconstruct and remix. And don't forget to blog about your learning journeys and share your insights with all of us. ## Post-Lecture Quiz -[Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/39) +[Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/39) ## Review & Self Study Want to explore more use cases? Here are a few relevant articles: diff --git a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md index 80d3ff03..d43c9e60 100644 --- a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md +++ b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md @@ -11,7 +11,7 @@ Empezamos con las definiciones de ciencia de datos y ética, se exploraron diver En esta lección, exploraremos la aplicación de la ciencia de datos en el mundo real en la industria y profundizaremos en ejemplos específicos en la investigación, humanidades digitales y sustentabilidad. Analizaremos oportunidades de proyectos para estudiantes y concluiremos con recursos útiles que te ayuden en tu aventura de aprendizaje. ## Examen previo a la lección -[Examen previo a la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/38) +[Examen previo a la lección](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/38) ## Ciencia de Datos + Industria Gracias a la democratización de la AI, los desarrolladores encuentran más fácil el diseñar e integrar tanto la toma de decisiones dirigidas por AI como el conocimiento práctico dirigido por datos en experiencias de usuario y desarrollar flujos de trabajo. Aquí algunos ejemplos de cómo la ciencia de datos es "aplicada" en aplicaciones del mundo real a través de la industria: @@ -131,7 +131,7 @@ Busca artículos que recomienden proyectos de ciencia de datos que son amigables ## Examen posterior a la lección -[Examen posterior a la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/39) +[Examen posterior a la lección](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/39) ## Revisión y auto-estudio ¿Quieres explorar más casos de uso? Aquí hay algunos artículos relevantes: diff --git a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.ko.md b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.ko.md index cf43322d..b0e7604a 100644 --- a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.ko.md +++ b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.ko.md @@ -12,7 +12,7 @@ ## 강의 전 퀴즈 -[Pre-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/38) +[Pre-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/38) ## 데이터 과학 + 산업 @@ -129,7 +129,7 @@ AI의 민주화 덕분에, 개발자들은 이제 사용자 경험과 개발 워 ## 강의 후 퀴즈 -[Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/39) +[Post-lecture quiz](https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/39) ## 리뷰 & 혼자 공부해보기 diff --git a/README.md b/README.md index 1e734d1d..7221bd82 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ In addition, a low-stakes quiz before a class sets the intention of the student - Assignment - Post-lesson quiz -> **A note about quizzes**: All quizzes are contained [in this app](https://red-bay-0a991ec0f.1.azurestaticapps.net), for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally; follow the instruction in the `quiz-app` folder. They are gradually being localized. +> **A note about quizzes**: All quizzes are contained [in this app](https://witty-beach-04b13e603.1.azurestaticapps.net/), for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally; follow the instruction in the `quiz-app` folder. They are gradually being localized. ## Lessons @@ -106,7 +106,7 @@ A PDF of all of the lessons can be found [here](https://microsoft.github.io/Data ## Help Wanted! -If you would like to translate all or part of the curriculum, please follow our [Translations](TRANSLATIONS.md) guide. +If you would like to translate all or part of the curriculum, please follow our [Translations](TRANSLATIONS.md) guide ## Other Curricula diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md index 86db8132..d23b7ac9 100644 --- a/TRANSLATIONS.md +++ b/TRANSLATIONS.md @@ -28,7 +28,7 @@ Similar to Readme's, please translate the assignments as well. 3. Edit the quiz-app's [translations index.js file](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/quiz-app/src/assets/translations/index.js) to add your language. -4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1 becomes https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1?loc=id +4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1 becomes https://witty-beach-04b13e603.1.azurestaticapps.net/quiz/1?loc=id **THANK YOU** diff --git a/quiz-app/dist/index.html b/quiz-app/dist/index.html index e895d323..b0a3297e 100644 --- a/quiz-app/dist/index.html +++ b/quiz-app/dist/index.html @@ -1 +1 @@ -quizzes
\ No newline at end of file +quizzes
\ No newline at end of file diff --git a/quiz-app/dist/js/app.37274da6.js b/quiz-app/dist/js/app.37274da6.js new file mode 100644 index 00000000..7ed37963 --- /dev/null +++ b/quiz-app/dist/js/app.37274da6.js @@ -0,0 +1,2 @@ +(function(e){function s(s){for(var a,o,n=s[0],c=s[1],l=s[2],d=0,w=[];d{t()});var K=G,X=t("a925");a["a"].config.productionTip=!1,a["a"].use(X["a"]);const Z=new X["a"]({locale:"en",fallbackLocale:"en"});new a["a"]({i18n:Z,router:K,render:e=>e(N)}).$mount("#app")},"7b56":function(e){e.exports=JSON.parse('[{"title":"Data Science for Beginners: Quizzes","complete":"Congratulations, you completed the quiz!","error":"Sorry, try again","quizzes":[{"id":38,"title":"Data Science In the Wild - Pre-Quiz","quiz":[{"questionText":"Data Science can be used in many industries such as","answerOptions":[{"answerText":"Finance and the Humanities","isCorrect":"false"},{"answerText":"Agriculture and Manufacturing","isCorrect":"false"},{"answerText":"All of these and more","isCorrect":"true"}]},{"questionText":"Data Science in the context of research can be focus on:","answerOptions":[{"answerText":"innovation opportunities","isCorrect":"true"},{"answerText":"error handling","isCorrect":"false"},{"answerText":"boosting sales","isCorrect":"false"}]},{"questionText":"The \'Gender Shades Study\' focused on","answerOptions":[{"answerText":"transforming gender discourse","isCorrect":"false"},{"answerText":"the biases inherent in facial analytics","isCorrect":"true"},{"answerText":"neither of these","isCorrect":"false"}]}]},{"id":39,"title":"Data Science In the Wild - Post-Quiz","quiz":[{"questionText":"Digital Humanities is a practice that combines computational methods with humanistic inquiry","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"Use Data Science for sustainability research by","answerOptions":[{"answerText":"studying deforestation","isCorrect":"false"},{"answerText":"studying climate change data","isCorrect":"false"},{"answerText":"both of these","isCorrect":"true"}]},{"questionText":"In finance, you can use data science for","answerOptions":[{"answerText":"research on your financial services","isCorrect":"false"},{"answerText":"to customize mutual fund options based on the type of customer","isCorrect":"false"},{"answerText":"both of the above","isCorrect":"true"}]}]}]}]')},"85ec":function(e,s,t){},8834:function(e){e.exports=JSON.parse('[{"title":"Data Science pour Débutants: Quizzes","complete":"Felicitations, you completed the quiz!","error":"Oups, try again","quizzes":[{"id":0,"title":"Premier quiz","quiz":[{"questionText":"premier question","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"deuxieme question","answerOptions":[{"answerText":"classical ML was invented first","isCorrect":"false"},{"answerText":"the use of neural networks","isCorrect":"true"},{"answerText":"deep learning is used in robots","isCorrect":"false"}]},{"questionText":"troisieme question","answerOptions":[{"answerText":"to automate the solving of multi-dimensional problems","isCorrect":"false"},{"answerText":"to customize a shopping experience based on the type of customer","isCorrect":"false"},{"answerText":"both of the above","isCorrect":"true"}]}]}]}]')},a420:function(e){e.exports=JSON.parse('[{"title":"Ciencia de datos para principiantes: Cuestionarios","complete":"Enhorabuena, has completado el cuestionario!","error":"Lo siento, inténtalo de nuevo","quizzes":[{"id":8,"title":"Bases de datos relacionales - Precuestionario","quiz":[{"questionText":"Una base de datos puede considerarse una tabla con columnas y filas","answerOptions":[{"answerText":"Verdadero","isCorrect":"true"},{"answerText":"Falso","isCorrect":"false"}]},{"questionText":"La mayoría de las bases de datos se componen de","answerOptions":[{"answerText":"una tabla","isCorrect":"false"},{"answerText":"una hoja de cálculo","isCorrect":"false"},{"answerText":"muchas tablas","isCorrect":"true"}]},{"questionText":"Puede evitar la duplicación de nombres de columnas si","answerOptions":[{"answerText":"creando muchas tablas","isCorrect":"false"},{"answerText":"creando tablas con relaciones incorporadas","isCorrect":"true"},{"answerText":"creando una única tabla gigante","isCorrect":"false"}]}]},{"id":9,"title":"Bases de datos relacionales - Cuestionario final","quiz":[{"questionText":"Una clave primaria es","answerOptions":[{"answerText":"un valor utilizado para identificar una fila específica en una tabla","isCorrect":"true"},{"answerText":"un valor utilizado para hacer que los valores sean únicos","isCorrect":"false"},{"answerText":"un valor utilizado para forzar la capitalización","isCorrect":"false"}]},{"questionText":"Una columna numérica \'ID\' sería una buena clave primari","answerOptions":[{"answerText":"Verdadero","isCorrect":"true"},{"answerText":"Falso","isCorrect":"false"}]},{"questionText":"Una clave foránea se utiliza para","answerOptions":[{"answerText":"valores que hacen referencia a los identificadores en una tabla separada","isCorrect":"true"},{"answerText":"valores que hacen referencia a las cadenas en una tabla separada","isCorrect":"false"},{"answerText":"conservar los valores que cambian con el tiempo","isCorrect":"false"}]}]},{"id":10,"title":"Bases de datos no relacionales - Precuestionario","quiz":[{"questionText":"Las hojas de cálculo son datos no relacionales","answerOptions":[{"answerText":"Verdadero","isCorrect":"true"},{"answerText":"Falso","isCorrect":"false"}]},{"questionText":"Identifica la diferencia entre datos no relacionales y relacionales.","answerOptions":[{"answerText":"Las bases de datos relacionales siempre contienen columnas y filas","isCorrect":"true"},{"answerText":"Algunos tipos de datos no relacionales utilizan columnas","isCorrect":"false"},{"answerText":"No hay ninguna diferencia","isCorrect":"false"}]},{"questionText":"¿Qué significa NoSQL?","answerOptions":[{"answerText":"Nope SQL","isCorrect":"false"},{"answerText":"No sólo SQL","isCorrect":"true"},{"answerText":"No más SQL","isCorrect":"false"}]}]},{"id":11,"title":"Bases de datos no relacionales - Cuestionario final","quiz":[{"questionText":"¿Cuál de estos NO es un tipo de NoSQL?","answerOptions":[{"answerText":"Object oriented","isCorrect":"true"},{"answerText":"Clave-valor","isCorrect":"false"},{"answerText":"Columnas","isCorrect":"false"}]},{"questionText":"¿Qué utilizas para hacer cálculos en las hojas de cálculo?","answerOptions":[{"answerText":"Python","isCorrect":"false"},{"answerText":"Alias","isCorrect":"false"},{"answerText":"Fórmulas","isCorrect":"true"}]},{"questionText":"¿A qué otro tipo de datos no relacionales se parece un documento en una base de datos de documentos?","answerOptions":[{"answerText":"Claves","isCorrect":"false"},{"answerText":"Columnas","isCorrect":"false"},{"answerText":"JSON","isCorrect":"true"}]}]},{"id":12,"title":"Python - Precuestionario","quiz":[{"questionText":"Python es un buen lenguaje para","answerOptions":[{"answerText":"La ciencia de datos","isCorrect":"false"},{"answerText":"Princiantes","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]},{"questionText":"Python es un buen lenguaje para la Ciencia de Datos porque","answerOptions":[{"answerText":"tiene muchas bibliotecas","isCorrect":"false"},{"answerText":"es un lenguaje rico pero sencillo","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]},{"questionText":"No puedes hacer ciencia de datos si no sabes Python","answerOptions":[{"answerText":"Verdadero","isCorrect":"false"},{"answerText":"Falso","isCorrect":"true"}]}]},{"id":13,"title":"Python - Cuestionario final","quiz":[{"questionText":"¿Qué biblioteca utilizarías para representar una lista de notas de los alumnos en clase?","answerOptions":[{"answerText":"Numpy","isCorrect":"false"},{"answerText":"SciPy","isCorrect":"false"},{"answerText":"Pandas","isCorrect":"true"}]},{"questionText":"Tienes un DataFrame con una lista de alumnos, su número de grupo y la nota media. ¿Qué operación utilizarías para calcular la nota media por grupo?","answerOptions":[{"answerText":"average","isCorrect":"false"},{"answerText":"avg","isCorrect":"false"},{"answerText":"groupby","isCorrect":"true"}]},{"questionText":"Tienes 100 amigos y quieres representar la información sobre la frecuencia con la que se hacen fotos entre ellos. ¿Qué estructura de datos utilizarías?","answerOptions":[{"answerText":"Numpy array","isCorrect":"true"},{"answerText":"Pandas DataFrame","isCorrect":"false"},{"answerText":"Pandas Series","isCorrect":"false"}]}]},{"id":14,"title":"Preparación de datos - Precuestionario","quiz":[{"questionText":"¿Cuál de ellos forma parte del proceso de preparación de datos?","answerOptions":[{"answerText":"Validación del modelo","isCorrect":"false"},{"answerText":"Clasificación","isCorrect":"false"},{"answerText":"Limpieza de los datos","isCorrect":"true"}]},{"questionText":"¿Por qué es tan importante la preparación de los datos?","answerOptions":[{"answerText":"Hace que los modelos sean más precisos","isCorrect":"true"},{"answerText":"No es importante","isCorrect":"false"},{"answerText":"Los ordenadores pueden ayudar a limpiar los datos","isCorrect":"false"}]},{"questionText":"¿Cuál es el objetivo de la limpieza de datos?","answerOptions":[{"answerText":"Corrección de problemas de formato","isCorrect":"false"},{"answerText":"Fijar los tipos de datos","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]}]},{"id":15,"title":"Preparación de los datos - Cuestionario final","quiz":[{"questionText":"Fusionar o unir dos conjuntos de datos en uno solo puede afectar a la coherencia de los datos","answerOptions":[{"answerText":"Verdadero","isCorrect":"true"},{"answerText":"Falso","isCorrect":"false"}]},{"questionText":"¿Qué es lo primero que hay que hacer ante la falta de datos?","answerOptions":[{"answerText":"Borrar los datos relacionados a esa falta","isCorrect":"false"},{"answerText":"Evaluar por qué falta","isCorrect":"true"},{"answerText":"Intenta rellenar los valores vacíos","isCorrect":"false"}]},{"questionText":"La unión de dos o más conjuntos de datos puede provocar los siguientes problemas","answerOptions":[{"answerText":"Duplicados","isCorrect":"false"},{"answerText":"Un formato inconsistente","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]}]}]}]')},ab5f:function(e){e.exports=JSON.parse('[{"title":"Data Science for Beginners: Quizzes","complete":"Congratulations, you completed the quiz!","error":"Sorry, try again","quizzes":[{"id":16,"title":"Visualizing Quantities - Pre-Quiz","quiz":[{"questionText":"A useful library for data visualizations is:","answerOptions":[{"answerText":"Matplotlib","isCorrect":"true"},{"answerText":"Matchartlib","isCorrect":"false"},{"answerText":"Matgraphtlib","isCorrect":"false"}]},{"questionText":"You can visualize quantities using:","answerOptions":[{"answerText":"scatterplots","isCorrect":"false"},{"answerText":"line charts","isCorrect":"false"},{"answerText":"both the above","isCorrect":"true"}]},{"questionText":"Bar plots are useful for visualizing quantity","answerOptions":[{"answerText":"true","isCorrect":"true"},{"answerText":"false","isCorrect":"false"}]}]},{"id":17,"title":"Visualizing Quantities - Post-Quiz","quiz":[{"questionText":"You can analyze trends over time using a:","answerOptions":[{"answerText":"line chart","isCorrect":"true"},{"answerText":"bar chart","isCorrect":"false"},{"answerText":"pie chart","isCorrect":"false"}]},{"questionText":"You can compare values using this type of chart:","answerOptions":[{"answerText":"line chart","isCorrect":"false"},{"answerText":"bar chart","isCorrect":"false"},{"answerText":"both of these","isCorrect":"true"}]},{"questionText":"Show how parts relate to a whole using this type of chart:","answerOptions":[{"answerText":"line","isCorrect":"false"},{"answerText":"column","isCorrect":"false"},{"answerText":"pie","isCorrect":"true"}]}]},{"id":18,"title":"Visualizing Distributions - Pre-Quiz","quiz":[{"questionText":"Histograms are overall more sophisticated types of chart than scatterplots","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"You can visualize distributions using this type of chart:","answerOptions":[{"answerText":"scatterplot","isCorrect":"true"},{"answerText":"pie chart","isCorrect":"false"},{"answerText":"column chart","isCorrect":"false"}]},{"questionText":"This library is particularly useful for building histograms","answerOptions":[{"answerText":"TensorFlow","isCorrect":"false"},{"answerText":"PyTorch","isCorrect":"false"},{"answerText":"Matplotlib","isCorrect":"true"}]}]},{"id":19,"title":"Visualizing Distributions - Post-Quiz","quiz":[{"questionText":"Histograms can be used to analyze this type of data:","answerOptions":[{"answerText":"textual","isCorrect":"false"},{"answerText":"numeric","isCorrect":"false"},{"answerText":"both of the above","isCorrect":"true"}]},{"questionText":"In a histogram, a \'bin\' refers to:","answerOptions":[{"answerText":"a class of data","isCorrect":"false"},{"answerText":"a grouping of data","isCorrect":"true"},{"answerText":"throwaway data","isCorrect":"false"}]},{"questionText":"To build a smooth density plot, use this library:","answerOptions":[{"answerText":"Seaborn","isCorrect":"true"},{"answerText":"Matplotlib","isCorrect":"false"},{"answerText":"PyTorch","isCorrect":"false"}]}]},{"id":20,"title":"Visualizing Proportions - Pre-Quiz","quiz":[{"questionText":"To visualize proprtions, use this type of chart:","answerOptions":[{"answerText":"pie","isCorrect":"false"},{"answerText":"waffle","isCorrect":"false"},{"answerText":"either of the above","isCorrect":"true"}]},{"questionText":"A free tool for visualizing your data is:","answerOptions":[{"answerText":"Popculator","isCorrect":"false"},{"answerText":"Graphculator","isCorrect":"false"},{"answerText":"Charticulator","isCorrect":"true"}]},{"questionText":"Use `plt.pie` to display a:","answerOptions":[{"answerText":"pie chart","isCorrect":"true"},{"answerText":"waffle chart","isCorrect":"false"},{"answerText":"scatterplot","isCorrect":"false"}]}]},{"id":21,"title":"Visualizing Proportions - Post-Quiz","quiz":[{"questionText":"You can edit the colors of your waffle charts","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"Use this library to build waffle charts:","answerOptions":[{"answerText":"edwaffle","isCorrect":"false"},{"answerText":"pywaffle","isCorrect":"true"},{"answerText":"yumwaffle","isCorrect":"false"}]},{"questionText":"In a donut chart, build your center circle using this syntax:","answerOptions":[{"answerText":"plt.Oval","isCorrect":"false"},{"answerText":"plt.Circle","isCorrect":"true"},{"answerText":"plt.Edit","isCorrect":"false"}]}]},{"id":22,"title":"Visualizing Relationships - Pre-Quiz","quiz":[{"questionText":"Use this library to visualize relationships:","answerOptions":[{"answerText":"Seaborn","isCorrect":"true"},{"answerText":"Merborn","isCorrect":"false"},{"answerText":"Reborn","isCorrect":"false"}]},{"questionText":"Use Seaborn\'s `relplot` to visualize","answerOptions":[{"answerText":"categorical relationships","isCorrect":"false"},{"answerText":"statistical relationships","isCorrect":"true"},{"answerText":"special relationships","isCorrect":"false"}]},{"questionText":"By editing a scatterplot\'s hue, you can:","answerOptions":[{"answerText":"display a dataset\'s distribution","isCorrect":"true"},{"answerText":"display item colors","isCorrect":"false"},{"answerText":"display temperatures","isCorrect":"false"}]}]},{"id":23,"title":"Visualizing Relationships - Post-Quiz","quiz":[{"questionText":"A more accessible way to display distribution is:","answerOptions":[{"answerText":"Using datapoint shape variation","isCorrect":"false"},{"answerText":"Using datapoint size variation","isCorrect":"false"},{"answerText":"Using either of the above","isCorrect":"true"}]},{"questionText":"Using Seaborn\'s `relplot` you can view an aggregation of data around a line chart","answerOptions":[{"answerText":"true","isCorrect":"true"},{"answerText":"false","isCorrect":"false"}]},{"questionText":"Facet grids help visualize","answerOptions":[{"answerText":"a given facet of your data","isCorrect":"true"},{"answerText":"outliers in your data","isCorrect":"false"},{"answerText":"progression in your data","isCorrect":"false"}]}]},{"id":24,"title":"Meaningful Visualizations - Pre-Quiz","quiz":[{"questionText":"It\'s relatively easy to deceive users by editing charts","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"Selecting the kind of chart to build depends on the type of data you have and the story you are telling about it","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"To create a deceptive chart, some creators manipulate:","answerOptions":[{"answerText":"the chart colors","isCorrect":"false"},{"answerText":"the X and Y axes","isCorrect":"false"},{"answerText":"either of the above","isCorrect":"true"}]}]},{"id":25,"title":"Meaningful Visualizations - Post-Quiz","quiz":[{"questionText":"Ensure that your charts are:","answerOptions":[{"answerText":"accessible","isCorrect":"false"},{"answerText":"readable","isCorrect":"false"},{"answerText":"both of the above","isCorrect":"true"}]},{"questionText":"D3 is an excellent charting library for use to create:","answerOptions":[{"answerText":"animated visualizations","isCorrect":"true"},{"answerText":"infographics","isCorrect":"false"},{"answerText":"deep learning","isCorrect":"false"}]},{"questionText":"Ensure the readability of your chart by:","answerOptions":[{"answerText":"adding tags","isCorrect":"false"},{"answerText":"editing color","isCorrect":"false"},{"answerText":"aligning labels properly","isCorrect":"true"}]}]}]}]')},b1e9:function(e){e.exports=JSON.parse('[{"title":"Data Science for Beginners: Quizzes","complete":"Congratulations, you completed the quiz!","error":"Sorry, try again","quizzes":[{"id":8,"title":"Relational Databases - Pre Quiz","quiz":[{"questionText":"A database can be considered a table with columns and rows","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"Most databases are comprised of","answerOptions":[{"answerText":"one table","isCorrect":"false"},{"answerText":"one spreadsheet","isCorrect":"false"},{"answerText":"many tables","isCorrect":"true"}]},{"questionText":"You can avoid duplicating column names by","answerOptions":[{"answerText":"creating many tables","isCorrect":"false"},{"answerText":"creating tables with relationships built in","isCorrect":"true"},{"answerText":"sticking to one big table","isCorrect":"false"}]}]},{"id":9,"title":"Relational Databases - Post Quiz","quiz":[{"questionText":"A primary key is","answerOptions":[{"answerText":"a value used to identify one specific row in a table","isCorrect":"true"},{"answerText":"a value used to make values unique","isCorrect":"false"},{"answerText":"a value used to force capitalization","isCorrect":"false"}]},{"questionText":"A numeric \'ID\' column would be a good primary key","answerOptions":[{"answerText":"true","isCorrect":"true"},{"answerText":"false","isCorrect":"false"}]},{"questionText":"A foreign key is used to","answerOptions":[{"answerText":"values that reference the ids in a separate table","isCorrect":"true"},{"answerText":"values that reference the strings in a separate table","isCorrect":"false"},{"answerText":"preserve values that change over time","isCorrect":"false"}]}]},{"id":10,"title":"Non-Relational Databases - Pre Quiz","quiz":[{"questionText":"Spreadsheets are non-relational data","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"Identify the difference between non-relational and relational data?","answerOptions":[{"answerText":"Relational databases always contain columns and rows","isCorrect":"true"},{"answerText":"Some types of non-relational data use columns","isCorrect":"false"},{"answerText":"There is no difference","isCorrect":"false"}]},{"questionText":"What does NoSQL stand for?","answerOptions":[{"answerText":"Nope SQL","isCorrect":"false"},{"answerText":"Not Only SQL","isCorrect":"true"},{"answerText":"No more SQL","isCorrect":"false"}]}]},{"id":11,"title":"Non-Relational Databases - Post Quiz","quiz":[{"questionText":"Which one of these is NOT a type of NoSQL?","answerOptions":[{"answerText":"Object oriented","isCorrect":"true"},{"answerText":"Key-value","isCorrect":"false"},{"answerText":"Columnar","isCorrect":"false"}]},{"questionText":"What do you use to do calculations in spreadsheets?","answerOptions":[{"answerText":"Python","isCorrect":"false"},{"answerText":"Aliases","isCorrect":"false"},{"answerText":"Formulas","isCorrect":"true"}]},{"questionText":"What other type of non-relational data does a document in a document database resemble?","answerOptions":[{"answerText":"Keys","isCorrect":"false"},{"answerText":"Column","isCorrect":"false"},{"answerText":"JSON","isCorrect":"true"}]}]},{"id":12,"title":"Python - Pre Quiz","quiz":[{"questionText":"Python is a good language for","answerOptions":[{"answerText":"Data Science","isCorrect":"false"},{"answerText":"Beginners","isCorrect":"false"},{"answerText":"Both","isCorrect":"true"}]},{"questionText":"Python is a good language for Data Science because","answerOptions":[{"answerText":"it has a lot of libraries","isCorrect":"false"},{"answerText":"it is a rich yet simple language","isCorrect":"false"},{"answerText":"all of the above","isCorrect":"true"}]},{"questionText":"You cannot do Data Science if you do not know Python","answerOptions":[{"answerText":"True","isCorrect":"false"},{"answerText":"False","isCorrect":"true"}]}]},{"id":13,"title":"Python - Post Quiz","quiz":[{"questionText":"Which library would you use to represent a list of grades of students in class?","answerOptions":[{"answerText":"Numpy","isCorrect":"false"},{"answerText":"SciPy","isCorrect":"false"},{"answerText":"Pandas","isCorrect":"true"}]},{"questionText":"You have a DataFrame with a list of students, their group number, and average grade. Which operation would you use to compute the average grade per group?","answerOptions":[{"answerText":"average","isCorrect":"false"},{"answerText":"avg","isCorrect":"false"},{"answerText":"groupby","isCorrect":"true"}]},{"questionText":"You have 100 friends and you want to represent the information on how frequently they take pictures with each other. Which data structure would you use?","answerOptions":[{"answerText":"Numpy array","isCorrect":"true"},{"answerText":"Pandas DataFrame","isCorrect":"false"},{"answerText":"Pandas Series","isCorrect":"false"}]}]},{"id":14,"title":"Data Preparation - Pre Quiz","quiz":[{"questionText":"Which one of these is part of the data preparation process?","answerOptions":[{"answerText":"Model validation","isCorrect":"false"},{"answerText":"Classification","isCorrect":"false"},{"answerText":"Cleaning","isCorrect":"true"}]},{"questionText":"Why is data preparation so important?","answerOptions":[{"answerText":"It makes models more accurate","isCorrect":"true"},{"answerText":"It\'s not important","isCorrect":"false"},{"answerText":"Computers can help clean the data","isCorrect":"false"}]},{"questionText":"What is a goal of data cleaning?","answerOptions":[{"answerText":"Fixing formatting issues","isCorrect":"false"},{"answerText":"Fixing data types","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"true"}]}]},{"id":15,"title":"Data Preparation - Post Quiz","quiz":[{"questionText":"Merging or joining two datasets into one can affect consistency in data","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"What\'s the first thing that should be done when faced with missing data?","answerOptions":[{"answerText":"Delete it","isCorrect":"false"},{"answerText":"Assess why it\'s missing","isCorrect":"true"},{"answerText":"Try to fill in empty values","isCorrect":"false"}]},{"questionText":"Joining two or more datasets can cause which following issue(s)?","answerOptions":[{"answerText":"Duplicates","isCorrect":"false"},{"answerText":"Inconsistent formatting","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"true"}]}]}]}]')},b26f:function(e){e.exports=JSON.parse('[{"title":"Data Science for Beginners: Quizzes","complete":"Congratulations, you completed the quiz!","error":"Sorry, try again","quizzes":[{"id":0,"title":"Defining Data Science - Pre Quiz","quiz":[{"questionText":"Why is the word _Science_ in Data Science?","answerOptions":[{"answerText":"It uses scientific methods to analyze data","isCorrect":"true"},{"answerText":"Only people with academic degrees can understand it","isCorrect":"false"},{"answerText":"To make is sound cool","isCorrect":"false"}]},{"questionText":"Learning Data Science is only useful for developers","answerOptions":[{"answerText":"True","isCorrect":"false"},{"answerText":"False","isCorrect":"true"}]},{"questionText":"What do we need to demonstrate that basketball players are taller than average people?","answerOptions":[{"answerText":"Collect some data","isCorrect":"false"},{"answerText":"Know some probability and statistics","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"true"}]}]},{"id":1,"title":"Defining Data Science: Post-Quiz","quiz":[{"questionText":"Which areas are closely related to data science?","answerOptions":[{"answerText":"Artificial Intelligence","isCorrect":"false"},{"answerText":"Machine Learning","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"true"}]},{"questionText":"Which of the following is an example of non-structured data?","answerOptions":[{"answerText":"List of students in class","isCorrect":"false"},{"answerText":"Collection of student essays","isCorrect":"true"},{"answerText":"Graph of friends of social network users","isCorrect":"false"}]},{"questionText":"What is the main goal of data science?","answerOptions":[{"answerText":"to collect data","isCorrect":"false"},{"answerText":"to process data","isCorrect":"false"},{"answerText":"to be able to make decisions based on data","isCorrect":"true"}]}]},{"id":2,"title":"Ethics - Pre-Quiz","quiz":[{"questionText":"What is a consideration of ethics in Data Science?","answerOptions":[{"answerText":"Data collection","isCorrect":"false"},{"answerText":"Algorithm Design","isCorrect":"false"},{"answerText":"All the above","isCorrect":"true"}]},{"questionText":"Which of the following are a part of ethical challenges?","answerOptions":[{"answerText":"Transparency","isCorrect":"false"},{"answerText":"Data privacy","isCorrect":"true"},{"answerText":"Reliability and safety","isCorrect":"false"}]},{"questionText":"Data ethics also includes AI and machine learning algorithms ","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False, they are separate","isCorrect":"false"}]}]},{"id":3,"title":"Ethics - Post-Quiz","quiz":[{"questionText":"What is the main difference between ethics and applied ethics?","answerOptions":[{"answerText":"Applied ethics is a specific type of ethics","isCorrect":"false"},{"answerText":"Applied ethics is not a real term","isCorrect":"false"},{"answerText":"Applied ethics is the process of finding and correcting ethical issues","isCorrect":"true"}]},{"questionText":"What is the difference between ethics regulation vs ethics principles","answerOptions":[{"answerText":"Ethics regulation focuses on regular ethics","isCorrect":"false"},{"answerText":"There is no difference","isCorrect":"false"},{"answerText":"Ethics principles do not relate to a particular law, while regulations do","isCorrect":"true"}]},{"questionText":"Which of these are real ethical principles and practices that can be applied?","answerOptions":[{"answerText":"Collection bias","isCorrect":"false"},{"answerText":"Compliance with ethics regulations","isCorrect":"false"},{"answerText":"both of the above","isCorrect":"true"}]}]},{"id":4,"title":"Defining Data - Pre-Quiz","quiz":[{"questionText":"Which of these could be quantitative data?","answerOptions":[{"answerText":"Photos of dogs","isCorrect":"false"},{"answerText":"Hotel reviews","isCorrect":"false"},{"answerText":"Student’s marks or grades","isCorrect":"true"}]},{"questionText":"Which of these could be qualitative data?","answerOptions":[{"answerText":"List of employees\' salaries","isCorrect":"false"},{"answerText":"Hotel reviews","isCorrect":"true"},{"answerText":"Students\' marks or grades","isCorrect":"false"}]},{"questionText":"What is the main goal of classifying data?","answerOptions":[{"answerText":"To store the data correctly","isCorrect":"false"},{"answerText":"To give the data a proper name","isCorrect":"false"},{"answerText":"To know which method is best to organize it for readability and analysis","isCorrect":"true"}]}]},{"id":5,"title":"Defining Data - Post-Quiz","quiz":[{"questionText":"The teacher is reviewing students’ number of correct answers, what type of data is that?","answerOptions":[{"answerText":"Qualitative Data","isCorrect":"false"},{"answerText":"Quantitative Data","isCorrect":"true"},{"answerText":"All the above","isCorrect":"false"}]},{"questionText":"A company is collecting surveys from their customers to improve their products. What type of data source is this?","answerOptions":[{"answerText":"Primary","isCorrect":"true"},{"answerText":"Secondary","isCorrect":"false"},{"answerText":"Tertiary","isCorrect":"false"}]},{"questionText":"A student is collecting data using queries. What data source could that be?","answerOptions":[{"answerText":"Local Files","isCorrect":"false"},{"answerText":"API","isCorrect":"false"},{"answerText":"Database","isCorrect":"true"}]}]},{"id":6,"title":"Statistics and Probability - Pre-Quiz","quiz":[{"questionText":"Why statistics and probability are important for data science?","answerOptions":[{"answerText":"Because you cannot operate on data without knowing math","isCorrect":"false"},{"answerText":"Because data science is a science and has strong formal foundation","isCorrect":"true"},{"answerText":"Because we want to keep uneducated people from doing data science","isCorrect":"false"}]},{"questionText":"Can you get heads 10 times in a row when throwing a coin?","answerOptions":[{"answerText":"yes","isCorrect":"true"},{"answerText":"no","isCorrect":"false"}]},{"questionText":"When you throw a dice, what is the probability of getting an even number?","answerOptions":[{"answerText":"1/2","isCorrect":"true"},{"answerText":"1/3","isCorrect":"false"},{"answerText":"impossible to tell","isCorrect":"false"}]}]},{"id":7,"title":"Statistics and Probability - Post-Quiz","quiz":[{"questionText":"We want to prove that basketball players are taller than average people. We have collected heights of 20 people from both groups. What do we need to do?","answerOptions":[{"answerText":"compare means","isCorrect":"false"},{"answerText":"collect more data, 20 is not enough!","isCorrect":"false"},{"answerText":"use t-test","isCorrect":"true"}]},{"questionText":"How can we demonstrate that income of a person is dependent on the level of education?","answerOptions":[{"answerText":"compute correlation coefficient","isCorrect":"true"},{"answerText":"divide into educated and non-educated cohorts and compute means","isCorrect":"false"},{"answerText":"use t-test","isCorrect":"false"}]},{"questionText":"We throw the dice 100 times and compute the average value. What would be the distribution of the result?","answerOptions":[{"answerText":"uniform","isCorrect":"false"},{"answerText":"normal","isCorrect":"true"},{"answerText":"none of the above","isCorrect":"false"}]}]}]}]')},babc:function(e){e.exports=JSON.parse('[{"title":"Ciencia de datos para principiantes: Cuestionarios","complete":"Enhorabuena, has completado el cuestionario!","error":"Lo siento, inténtalo de nuevo","quizzes":[{"id":38,"title":"Ciencia de los datos en la naturaleza","quiz":[{"questionText":"La ciencia de los datos puede utilizarse en muchos sectores, como","answerOptions":[{"answerText":"Finanzas y Humanidades","isCorrect":"false"},{"answerText":"Agricultura y manufactura","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]},{"questionText":"La ciencia de los datos en el contexto de la investigación puede centrarse en:","answerOptions":[{"answerText":"oportunidades de innovación","isCorrect":"true"},{"answerText":"gestión de errores","isCorrect":"false"},{"answerText":"impulsar las ventas","isCorrect":"false"}]},{"questionText":"El \'Estudio de los matices de género\' se centró en","answerOptions":[{"answerText":"transformar el discurso de género","isCorrect":"false"},{"answerText":"los sesgos inherentes al análisis facial","isCorrect":"true"},{"answerText":"Ninguna de las respuestas anteriores","isCorrect":"false"}]}]},{"id":39,"title":"Ciencia de los datos en la naturaleza - Cuestionario final","quiz":[{"questionText":"Las humanidades digitales son una práctica que combina los métodos informáticos con la investigación humanística","answerOptions":[{"answerText":"Verdadera","isCorrect":"true"},{"answerText":"Falso","isCorrect":"false"}]},{"questionText":"Utilizar la ciencia de los datos para la investigación de la sostenibilidad","answerOptions":[{"answerText":"Estudiar la deforestación","isCorrect":"false"},{"answerText":"Estudio de los datos del cambio climático","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]},{"questionText":"En finanzas, se puede utilizar la ciencia de los datos para","answerOptions":[{"answerText":"Investigación sobre sus servicios financieros","isCorrect":"false"},{"answerText":"Personalizar las opciones de fondos de inversión en función del tipo de cliente","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]}]}]}]')},c42b:function(e){e.exports=JSON.parse('[{"title":"Data Science for Beginners: Quizzes","complete":"Congratulations, you completed the quiz!","error":"Sorry, try again","quizzes":[{"id":32,"title":"Data Science in the Cloud - Introduction - Pre-Quiz","quiz":[{"questionText":"What is the cloud?","answerOptions":[{"answerText":"A collection of databases for storing big data.","isCorrect":"false"},{"answerText":"A collection of pay-as-you-go computing services over the internet.","isCorrect":"true"},{"answerText":"A visible mass of particles suspended in the air.","isCorrect":"false"}]},{"questionText":"What is cloud computing?","answerOptions":[{"answerText":"Delivery of computing services over the internet.","isCorrect":"true"},{"answerText":"Setting up your own datacenter.","isCorrect":"false"},{"answerText":"Using the internet.","isCorrect":"false"}]},{"questionText":"What are some advantages of the cloud?","answerOptions":[{"answerText":"Flexibility, scalability, reliability, security","isCorrect":"true"},{"answerText":"Flexibility, scalability, variability, security","isCorrect":"false"},{"answerText":"Clarity, scalability, reliability, variability","isCorrect":"false"}]}]},{"id":33,"title":"Data Science in the Cloud - Introduction - Post-Quiz","quiz":[{"questionText":"Which one is NOT necessarily a good reason for choosing the cloud?","answerOptions":[{"answerText":"Using Machine Learning and data intelligence services","isCorrect":"false"},{"answerText":"Processing large amounts of data","isCorrect":"false"},{"answerText":"Storing sensitive/confidential governmental data","isCorrect":"true"}]},{"questionText":"In which way(s) do data scientists use the cloud?","answerOptions":[{"answerText":"Infrastructure tasks","isCorrect":"false"},{"answerText":"Storing large amounts of data","isCorrect":"true"},{"answerText":"Configuring networking options","isCorrect":"false"}]},{"questionText":"Which cloud computing service provides access to software applications without the need to maintain the software?","answerOptions":[{"answerText":"Infrastructure as a Service","isCorrect":"false"},{"answerText":"Platform as a Service","isCorrect":"false"},{"answerText":"Software as a Service","isCorrect":"true"}]}]},{"id":34,"title":"Data Science in the Cloud - Low-Code - Pre-Quiz","quiz":[{"questionText":"One benefit of using Jupyter Notebooks is that you can quickly prototype models.","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"Why would a data scientist use Azure Machine Learning?","answerOptions":[{"answerText":"To save time exploring and pre-processing data.","isCorrect":"false"},{"answerText":"To produce accurate models","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"true"}]},{"questionText":"You don\'t need to have expertise in coding to use Azure ML.","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False, code is always needed","isCorrect":"false"}]}]},{"id":35,"title":"Data Science in the Cloud - Low-Code - Post-Quiz","quiz":[{"questionText":"What do you need to create before accessing Azure ML Studio?","answerOptions":[{"answerText":"A workspace","isCorrect":"true"},{"answerText":"A compute instance","isCorrect":"false"},{"answerText":"A compute cluster","isCorrect":"false"}]},{"questionText":"Which of the following tasks are supported by Automated ML?","answerOptions":[{"answerText":"Image generation","isCorrect":"false"},{"answerText":"Classification","isCorrect":"true"},{"answerText":"Natural language generation","isCorrect":"false"}]},{"questionText":"In which case do you need GPU over CPU?","answerOptions":[{"answerText":"When you have tabular data","isCorrect":"false"},{"answerText":"When you have enough money to afford it","isCorrect":"false"},{"answerText":"When you do Deep Learning","isCorrect":"true"}]}]},{"id":36,"title":"Data Science in the Cloud - Azure - Pre-Quiz","quiz":[{"questionText":"Which of these would be affected by increasing the cluster size?","answerOptions":[{"answerText":"Responsiveness","isCorrect":"false"},{"answerText":"Cost","isCorrect":"false"},{"answerText":"Model performance","isCorrect":"true"}]},{"questionText":"What is a benefit of using low code tools?","answerOptions":[{"answerText":"No expertise of code required","isCorrect":"true"},{"answerText":"Automatically label the dataset","isCorrect":"false"},{"answerText":"Better security of the model","isCorrect":"false"}]},{"questionText":"What is AutoML?","answerOptions":[{"answerText":"A tool for automating the preprocessing of data","isCorrect":"false"},{"answerText":"A tool for automating the deployment of models","isCorrect":"true"},{"answerText":"All of the above","isCorrect":"false"}]}]},{"id":37,"title":"Data Science in the Cloud - Azure - Post-Quiz","quiz":[{"questionText":"What is the reason for creating an AutoMLConfig?","answerOptions":[{"answerText":"It is where the training and the testing data are split","isCorrect":"false"},{"answerText":"It is where you specify the model to be trained","isCorrect":"false"},{"answerText":"It provides all the details of your AutoML experiment","isCorrect":"true"}]},{"questionText":"Which of the following metrics is supported by Automated ML for a classification task?","answerOptions":[{"answerText":"Accuracy","isCorrect":"true"},{"answerText":"r2_score","isCorrect":"false"},{"answerText":"normalized_root_mean_error","isCorrect":"false"}]},{"questionText":"What is NOT an advantage of using the SDK?","answerOptions":[{"answerText":"It can be used to automate multiple tasks and runs","isCorrect":"false"},{"answerText":"It makes it easier to programmatically edit runs","isCorrect":"false"},{"answerText":"It can be used through a Graphical User Interface","isCorrect":"true"}]}]}]}]')},c6bf:function(e){e.exports=JSON.parse('[{"title":"Data Science for Beginners: Quizzes","complete":"Congratulations, you completed the quiz!","error":"Sorry, try again","quizzes":[{"id":26,"title":"Data Science LifeCycle - Introduction Pre-Quiz","quiz":[{"questionText":"What is the first step of the data science lifecycle?","answerOptions":[{"answerText":"Analyzing data","isCorrect":"false"},{"answerText":"Cleaning data","isCorrect":"false"},{"answerText":"Acquiring data and the problem to solve","isCorrect":"true"}]},{"questionText":"Once the next step of the data science lifecycle is reached, you can\'t go back to previous steps","answerOptions":[{"answerText":"True","isCorrect":"false"},{"answerText":"False","isCorrect":"true"}]},{"questionText":"What considerations should be taken when maintaining data?","answerOptions":[{"answerText":"Cleaning data","isCorrect":"false"},{"answerText":"Keeping the data secure","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"true"}]}]},{"id":27,"title":"Data Science LifeCycle - Introduction Post-Quiz","quiz":[{"questionText":"What step of the data science lifecycle is most likely to produce a model? ","answerOptions":[{"answerText":"Processing","isCorrect":"true"},{"answerText":"Maintenance","isCorrect":"false"},{"answerText":"Capturing","isCorrect":"false"}]},{"questionText":"What questions would a data scientist ask in the capture phase of the data science lifecycle?","answerOptions":[{"answerText":"What are the constraints?","isCorrect":"true"},{"answerText":"Does the data make sense?","isCorrect":"false"},{"answerText":"Does the model make sense?","isCorrect":"false"}]},{"questionText":"What are some common techniques in the processing stage?","answerOptions":[{"answerText":"Clustering","isCorrect":"true"},{"answerText":"Artificial intelligence","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"false"}]}]},{"id":28,"title":"Data Science LifeCycle - Analyzing Pre-Quiz","quiz":[{"questionText":"Analyzing can refer to analyzing models or data","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"What is important about exploring data before using it in a model or further analysis?","answerOptions":[{"answerText":"To remove data","isCorrect":"false"},{"answerText":"Identify challenges in the data","isCorrect":"true"},{"answerText":"Exploring data isn\'t important","isCorrect":"false"}]},{"questionText":"What are some things a data scientist will do when exploring data? ","answerOptions":[{"answerText":"Sampling","isCorrect":"false"},{"answerText":"Visualizations","isCorrect":"false"},{"answerText":"All of the above","isCorrect":"true"}]}]},{"id":29,"title":"Data Science LifeCycle - Analyzing Post-Quiz","quiz":[{"questionText":"Visualization is never part of exploratory data analysis (EDA)","answerOptions":[{"answerText":"True","isCorrect":"false"},{"answerText":"False","isCorrect":"true"}]},{"questionText":"Which Panda functions provides basic data profiling?","answerOptions":[{"answerText":"pandas()","isCorrect":"false"},{"answerText":"isnull()","isCorrect":"false"},{"answerText":"describe()","isCorrect":"true"}]},{"questionText":"What is the purpose of sampling data?","answerOptions":[{"answerText":"Sampling is better than data profiling when looking for errors","isCorrect":"false"},{"answerText":"Sampling doesn\'t serve a purpose","isCorrect":"false"},{"answerText":"Sampling is used to analyze data in a large data set because it\'s hard to analyze all of it","isCorrect":"true"}]}]},{"id":30,"title":"Data Science LifeCycle - Communication Pre-Quiz","quiz":[{"questionText":"When communicating data to an audience, it is a best practice to focus solely on the numbers and not the story.","answerOptions":[{"answerText":"True","isCorrect":"false"},{"answerText":"False","isCorrect":"true"}]},{"questionText":"Which is an example of Two-Way Communication?","answerOptions":[{"answerText":"When a presenter presents to an audience and leaves the floor open for questions & feedback.","isCorrect":"true"},{"answerText":"When a leader puts out a TV or Radio message to their constituents.","isCorrect":"false"},{"answerText":"When a brand advertises to potential customers.","isCorrect":"false"}]},{"questionText":"When an individual is presenting to an audience, and the audience is not providing any feedback, who is acting as the information receiver?","answerOptions":[{"answerText":"The presenter","isCorrect":"false"},{"answerText":"The audience","isCorrect":"true"},{"answerText":"Specific members of the audience","isCorrect":"false"}]}]},{"id":31,"title":"Data Science LifeCycle - Communication Post-Quiz","quiz":[{"questionText":"When communicating data, colors can be used to evoke emotion from the audience.","answerOptions":[{"answerText":"True","isCorrect":"true"},{"answerText":"False","isCorrect":"false"}]},{"questionText":"Of the options below, which is the most meaningful explanation of a company\'s yearly progress?","answerOptions":[{"answerText":"We had an excellent year! Our users grew by 30%, our revenue increased by 21%, and we\'ve added 100 new team members.","isCorrect":"true"},{"answerText":"We had a great year! Our users grew tremendously, our revenue increased by a lot, and we even added several new team members across all divisions.","isCorrect":"false"},{"answerText":"Our progress year was simply phenomenal. I can\'t overstate how much of an increase we\'ve had with growing our users, our revenue, or our team.","isCorrect":"false"}]},{"questionText":"When communicating data to an Executive Audience, which would be an acceptable strategy to take?","answerOptions":[{"answerText":"Go in depth with the small details and spend less time on the significance of the data.","isCorrect":"false"},{"answerText":"Focus primarily on the significance of the data and the recommended next steps based on the data.","isCorrect":"true"},{"answerText":"Explain the entire context and try to brainstorm potential solutions with the executives.","isCorrect":"false"}]}]}]}]')},ca70:function(e){e.exports=JSON.parse('[{"title":"Ciencia de datos para principiantes: Cuestionarios","complete":"Enhorabuena, has completado el cuestionario!","error":"Lo siento, inténtalo de nuevo","quizzes":[{"id":26,"title":"Ciclo de vida de la ciencia de datos - Introducción Cuestionario previo","quiz":[{"questionText":"¿Cuál es el primer paso del ciclo de vida de la ciencia de datos?","answerOptions":[{"answerText":"Análisis de datos","isCorrect":"false"},{"answerText":"Limpieza de datos","isCorrect":"false"},{"answerText":"Adquisición de datos y problema a resolver","isCorrect":"true"}]},{"questionText":"Una vez alcanzado el siguiente paso del ciclo de vida de la ciencia de datos, no se puede volver a los pasos anteriores","answerOptions":[{"answerText":"Verdadero","isCorrect":"false"},{"answerText":"Falso","isCorrect":"true"}]},{"questionText":"¿Qué consideraciones hay que tener en cuenta a la hora de conservar los datos?","answerOptions":[{"answerText":"Limpieza de los datos","isCorrect":"false"},{"answerText":"Mantener la seguridad de los datos","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]}]},{"id":27,"title":"Ciclo de vida de la ciencia de datos - Cuestionario posterior","quiz":[{"questionText":"¿Qué paso del ciclo de vida de la ciencia de datos es más probable que produzca un modelo? ","answerOptions":[{"answerText":"Procesamiento","isCorrect":"true"},{"answerText":"Mantenimiento","isCorrect":"false"},{"answerText":"Captura","isCorrect":"false"}]},{"questionText":"¿Qué preguntas se haría un científico de datos en la fase de captura del ciclo de vida de la ciencia de datos?","answerOptions":[{"answerText":"¿Cuáles son las limitaciones?","isCorrect":"true"},{"answerText":"¿Tienen sentido los datos?","isCorrect":"false"},{"answerText":"¿Tiene sentido el modelo?","isCorrect":"false"}]},{"questionText":"¿Cuáles son las técnicas más comunes en la etapa de procesamiento?","answerOptions":[{"answerText":"Agrupación","isCorrect":"true"},{"answerText":"Inteligencia artificial","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"false"}]}]},{"id":28,"title":"Ciclo de vida de la ciencia de los datos - Análisis previo","quiz":[{"questionText":"Analizar puede referirse a analizar modelos o datos","answerOptions":[{"answerText":"Verdadero","isCorrect":"true"},{"answerText":"Falso","isCorrect":"false"}]},{"questionText":"¿Qué importancia tiene la exploración de los datos antes de utilizarlos en un modelo o en un análisis posterior?","answerOptions":[{"answerText":"Para eliminar los datos","isCorrect":"false"},{"answerText":"Identificar los desafíos en los datos","isCorrect":"true"},{"answerText":"Explorar los datos no es importante","isCorrect":"false"}]},{"questionText":"¿Qué hace un científico de datos cuando explora los datos?","answerOptions":[{"answerText":"Muestreo","isCorrect":"false"},{"answerText":"Visualización","isCorrect":"false"},{"answerText":"Las dos respuestas anteriores","isCorrect":"true"}]}]},{"id":29,"title":"Ciclo de vida de la ciencia de los datos - Análisis cuestionario final","quiz":[{"questionText":"La visualización nunca forma parte del análisis exploratorio de datos (AED)","answerOptions":[{"answerText":"Verdadero","isCorrect":"false"},{"answerText":"Falso","isCorrect":"true"}]},{"questionText":"¿Qué funciones de Panda proporcionan un perfil de datos básico?","answerOptions":[{"answerText":"pandas()","isCorrect":"false"},{"answerText":"isnull()","isCorrect":"false"},{"answerText":"describe()","isCorrect":"true"}]},{"questionText":"¿Para qué sirve el muestreo de datos?","answerOptions":[{"answerText":"El muestreo es mejor que la elaboración de perfiles de datos cuando se buscan errores","isCorrect":"false"},{"answerText":"El muestreo no sirve para nada","isCorrect":"false"},{"answerText":"El muestreo se utiliza para analizar los datos de un gran conjunto de datos porque es difícil analizarlos todos","isCorrect":"true"}]}]},{"id":30,"title":"Ciclo de vida de la ciencia de los datos - comunicación","quiz":[{"questionText":"Cuando se comunican datos a un público, es una buena práctica centrarse únicamente en las cifras y no en la historia.","answerOptions":[{"answerText":"Verdadero","isCorrect":"false"},{"answerText":"Falso","isCorrect":"true"}]},{"questionText":"¿Cuál es un ejemplo de comunicación bidireccional?","answerOptions":[{"answerText":"Cuando un presentador se dirige a un público y deja la palabra abierta para preguntas y comentarios.","isCorrect":"true"},{"answerText":"Cuando un líder lanza un mensaje de televisión o radio a sus electores.","isCorrect":"false"},{"answerText":"Cuando una marca se anuncia a los clientes potenciales.","isCorrect":"false"}]},{"questionText":"Cuando una persona hace una presentación ante un público, y éste no proporciona ninguna información, ¿quién actúa como receptor de la información?","answerOptions":[{"answerText":"El presentador","isCorrect":"false"},{"answerText":"La audiencia","isCorrect":"true"},{"answerText":"Miembros específicos de la audiencia","isCorrect":"false"}]}]},{"id":31,"title":"Ciclo de vida de la ciencia de los datos - comunicación cuestionario final","quiz":[{"questionText":"A la hora de comunicar datos, los colores pueden servir para evocar emociones en el público.","answerOptions":[{"answerText":"Verdadero","isCorrect":"true"},{"answerText":"Falso","isCorrect":"false"}]},{"questionText":"De las siguientes opciones, ¿cuál es la explicación más significativa del progreso anual de una empresa?","answerOptions":[{"answerText":"Hemos tenido un año excelente. Nuestros usuarios han crecido un 30%, nuestros ingresos han aumentado un 21% y hemos incorporado 100 nuevos miembros al equipo.","isCorrect":"true"},{"answerText":"Hemos tenido un gran año. Nuestros usuarios han crecido enormemente, nuestros ingresos han aumentado mucho e incluso hemos incorporado varios miembros nuevos al equipo en todas las divisiones.","isCorrect":"false"},{"answerText":"Nuestro año de progreso fue simplemente fenomenal. No puedo exagerar el aumento que hemos tenido con el crecimiento de nuestros usuarios, nuestros ingresos o nuestro equipo.","isCorrect":"false"}]},{"questionText":"A la hora de comunicar datos a una audiencia ejecutiva, ¿cuál sería una estrategia aceptable?","answerOptions":[{"answerText":"Profundiza en los pequeños detalles y dedique menos tiempo a la importancia de los datos.","isCorrect":"false"},{"answerText":"Concentrate principalmente en la importancia de los datos y en los próximos pasos recomendados sobre la base de los datos.","isCorrect":"true"},{"answerText":"Explica todo el contexto e intente hacer una lluvia de ideas sobre posibles soluciones con los ejecutivos.","isCorrect":"false"}]}]}]}]')},f1f5:function(e){e.exports=JSON.parse('[{"title":"Ciencia de datos para principiantes: Cuestionarios","complete":"Enhorabuena, has completado el cuestionario!","error":"Lo siento, inténtalo de nuevo","quizzes":[{"id":16,"title":"Visualización de cantidades - Cuestionario previo","quiz":[{"questionText":"Una biblioteca útil para las visualizaciones de datos es:","answerOptions":[{"answerText":"Matplotlib","isCorrect":"true"},{"answerText":"Matchartlib","isCorrect":"false"},{"answerText":"Matgraphtlib","isCorrect":"false"}]},{"questionText":"Puede visualizar las cantidades utilizando:","answerOptions":[{"answerText":"gráficos de dispersión","isCorrect":"false"},{"answerText":"gráfico líneas","isCorrect":"false"},{"answerText":"las dos respuestas anteriores","isCorrect":"true"}]},{"questionText":"Los gráficos de barras son útiles para visualizar la cantidad","answerOptions":[{"answerText":"verdadero","isCorrect":"true"},{"answerText":"falso","isCorrect":"false"}]}]},{"id":17,"title":"Visualización de cantidades - Cuestionario final","quiz":[{"questionText":"Puede analizar las tendencias a lo largo del tiempo utilizando un:","answerOptions":[{"answerText":"gráfico de líneas","isCorrect":"true"},{"answerText":"gráfico de barras","isCorrect":"false"},{"answerText":"gráfico circular","isCorrect":"false"}]},{"questionText":"Puede comparar valores utilizando este tipo de gráfico:","answerOptions":[{"answerText":"gráfico de líneas","isCorrect":"false"},{"answerText":"gráfico de barras","isCorrect":"false"},{"answerText":"las dos respuestas anteriores","isCorrect":"true"}]},{"questionText":"Mostra cómo se relacionan las partes con un todo utilizando este tipo de gráfico:","answerOptions":[{"answerText":"líneas","isCorrect":"false"},{"answerText":"columnas","isCorrect":"false"},{"answerText":"circular","isCorrect":"true"}]}]},{"id":18,"title":"Visualización de distribuciones - Cuestionario previo","quiz":[{"questionText":"Los histogramas son en general tipos de gráficos más sofisticados que los de dispersión","answerOptions":[{"answerText":"verdaderos","isCorrect":"true"},{"answerText":"falso","isCorrect":"false"}]},{"questionText":"Puede visualizar las distribuciones utilizando este tipo de gráfico:","answerOptions":[{"answerText":"gráfico de dispersión","isCorrect":"true"},{"answerText":"gráfico circular","isCorrect":"false"},{"answerText":"gráfico de columnas","isCorrect":"false"}]},{"questionText":"Esta biblioteca es especialmente útil para construir histogramas","answerOptions":[{"answerText":"TensorFlow","isCorrect":"false"},{"answerText":"PyTorch","isCorrect":"false"},{"answerText":"Matplotlib","isCorrect":"true"}]}]},{"id":19,"title":"Visualización de distribuciones - Cuestionario final","quiz":[{"questionText":"Los histogramas pueden utilizarse para analizar este tipo de datos:","answerOptions":[{"answerText":"textual","isCorrect":"false"},{"answerText":"numérico","isCorrect":"false"},{"answerText":"las dos respuestas anteriores","isCorrect":"true"}]},{"questionText":"En un histograma, un \'bin\' se refiere a:","answerOptions":[{"answerText":"una clase de datos","isCorrect":"false"},{"answerText":"una agrupación de datos","isCorrect":"true"},{"answerText":"datos desechables","isCorrect":"false"}]},{"questionText":" Para construir un gráfico de densidad suave, utilice esta biblioteca:","answerOptions":[{"answerText":"Seaborn","isCorrect":"true"},{"answerText":"Matplotlib","isCorrect":"false"},{"answerText":"PyTorch","isCorrect":"false"}]}]},{"id":20,"title":"Visualización de las proporciones - Cuestionario previo","quiz":[{"questionText":"Para visualizar las proporciones, utilice este tipo de gráfico:","answerOptions":[{"answerText":"circular","isCorrect":"false"},{"answerText":"waffle","isCorrect":"false"},{"answerText":"ninguna de las respuestas anteriores","isCorrect":"true"}]},{"questionText":"Una herramienta gratuita para visualizar sus datos es:","answerOptions":[{"answerText":"Popculator","isCorrect":"false"},{"answerText":"Graphculator","isCorrect":"false"},{"answerText":"Charticulator","isCorrect":"true"}]},{"questionText":"Utiliza `plt.pie` para mostrar un:","answerOptions":[{"answerText":"gráfico circular","isCorrect":"true"},{"answerText":"gráfico de gofres","isCorrect":"false"},{"answerText":"gráfico de dispersión","isCorrect":"false"}]}]},{"id":21,"title":"Visualización de las proporciones - Cuestionario final","quiz":[{"questionText":"Puedes editar los colores de tus gráficos de gofres","answerOptions":[{"answerText":"verdadero","isCorrect":"true"},{"answerText":"falso","isCorrect":"false"}]},{"questionText":"Utiliza esta biblioteca para construir gráficos de gofres:","answerOptions":[{"answerText":"edwaffle","isCorrect":"false"},{"answerText":"pywaffle","isCorrect":"true"},{"answerText":"yumwaffle","isCorrect":"false"}]},{"questionText":"En un gráfico de rosca, construye el círculo central utilizando esta sintaxis:","answerOptions":[{"answerText":"plt.Oval","isCorrect":"false"},{"answerText":"plt.Circle","isCorrect":"true"},{"answerText":"plt.Edit","isCorrect":"false"}]}]},{"id":22,"title":"Visualización de las relaciones - Cuestionario previo","quiz":[{"questionText":"Utiliza esta biblioteca para visualizar las relaciones:","answerOptions":[{"answerText":"Seaborn","isCorrect":"true"},{"answerText":"Merborn","isCorrect":"false"},{"answerText":"Reborn","isCorrect":"false"}]},{"questionText":"Utiliza la función `relplot` de Seaborn para visualizar","answerOptions":[{"answerText":"relaciones categóricas","isCorrect":"false"},{"answerText":"relaciones estadísticas","isCorrect":"true"},{"answerText":"relaciones especiales","isCorrect":"false"}]},{"questionText":"Al editar el tono de un gráfico de dispersión, puedes:","answerOptions":[{"answerText":"mostrar la distribución de un conjunto de datos","isCorrect":"true"},{"answerText":"mostrar los colores de los artículos","isCorrect":"false"},{"answerText":"mostrar las temperaturas","isCorrect":"false"}]}]},{"id":23,"title":"Visualización de las relaciones - Cuestionario final","quiz":[{"questionText":"Una forma más accesible de mostrar la distribución es:","answerOptions":[{"answerText":"Uso de la variación de la forma de los puntos de datos","isCorrect":"false"},{"answerText":"Empleo de la variación del tamaño de los puntos de datos","isCorrect":"false"},{"answerText":"Utilizando cualquiera de los respuestas anteriores","isCorrect":"true"}]},{"questionText":"Utilizando la función `relplot` de Seaborn puedes ver una agregación de datos en torno a un gráfico de líneas","answerOptions":[{"answerText":"verdadero","isCorrect":"true"},{"answerText":"falso","isCorrect":"false"}]},{"questionText":"Las cuadrículas de facetas ayudan a visualizar","answerOptions":[{"answerText":"una faceta determinada de los datos","isCorrect":"true"},{"answerText":"valores atípicos en los datos","isCorrect":"false"},{"answerText":"progresión en los datos","isCorrect":"false"}]}]},{"id":24,"title":"Visualizaciones significativas - Cuestionario previo","quiz":[{"questionText":"Es relativamente fácil engañar a los usuarios editando 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_c('div',[_c('nav',[_c('ul',[_c('li',[_c('router-link',{staticClass:\"navlink\",attrs:{\"to\":\"/\"}},[_vm._v(\"Home\")])],1),_vm._m(0),_c('li',[_c('select',{directives:[{name:\"model\",rawName:\"v-model\",value:(_vm.locale),expression:\"locale\"}],on:{\"change\":function($event){var $$selectedVal = Array.prototype.filter.call($event.target.options,function(o){return o.selected}).map(function(o){var val = \"_value\" in o ? o._value : o.value;return val}); _vm.locale=$event.target.multiple ? $$selectedVal : $$selectedVal[0]}}},[_c('option',[_vm._v(\"en\")]),_c('option',[_vm._v(\"es\")])])]),_c('li',{staticClass:\"title\"},[_vm._v(_vm._s(_vm.questions[_vm.locale][0].title))])])]),_c('div',{attrs:{\"id\":\"app\"}},[_c('router-view',[_c('Quiz')],1)],1)])}\nvar staticRenderFns = [function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('li',[_c('label',{attrs:{\"for\":\"locale\"}},[_vm._v(\"locale\")])])}]\n\nexport { render, staticRenderFns }","var render = function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:\"card\"},_vm._l((_vm.questions[_vm.currLocale]),function(q){return _c('div',{key:q.quizzes[0].id},_vm._l((q.quizzes),function(quiz){return _c('div',{key:quiz.id},[(_vm.route == quiz.id)?_c('div',[_c('div',[(_vm.complete)?_c('h3',{staticClass:\"message complete\"},[_vm._v(_vm._s(q.complete))]):_c('div',[(_vm.error)?_c('h3',{staticClass:\"error\"},[_vm._v(_vm._s(q.error))]):_vm._e()]),_c('h1',[_vm._v(_vm._s(quiz.title))]),_c('h2',[_vm._v(\" \"+_vm._s(quiz.quiz[_vm.currentQuestion].questionText)+\" \")]),_c('div',_vm._l((quiz.quiz[_vm.currentQuestion]\n .answerOptions),function(option,index){return _c('button',{key:index,staticClass:\"btn ans-btn\",on:{\"click\":function($event){return _vm.handleAnswerClick(option.isCorrect)}}},[_vm._v(\" \"+_vm._s(option.answerText)+\" \")])}),0)])]):_vm._e()])}),0)}),0)}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","import en0 from \"./group-1.json\";\r\nimport en1 from \"./group-2.json\";\r\nimport en2 from \"./group-3.json\";\r\n\r\nimport en3 from \"./group-4.json\";\r\n\r\nimport en4 from \"./group-5.json\";\r\n\r\nimport en5 from \"./group-6.json\";\r\n\r\n\r\nconst quiz = {\r\n 0: en0[0],\r\n 1: en1[0],\r\n 2: en2[0],\r\n 3: en3[0],\r\n 4: en4[0],\r\n 5: en5[0],\r\n};\r\n\r\nexport default quiz;\r\n","import fr0 from \"./group-1.json\";\r\n//import fr1 from \"./group-2.json\";\r\n\r\nconst quiz = {\r\n 0: fr0[0],\r\n //1: fr1[0],\r\n};\r\n\r\nexport default quiz;\r\n","import es0 from './group-1.json';\r\nimport es1 from './group-2.json';\r\nimport es2 from './group-3.json';\r\n\r\nimport es3 from './group-4.json';\r\n\r\nimport es4 from './group-5.json';\r\n\r\nimport es5 from './group-6.json';\r\n\r\nconst quiz = {\r\n 0: es0[0],\r\n 1: es1[0],\r\n 2: es2[0],\r\n 3: es3[0],\r\n 4: es4[0],\r\n 5: es5[0],\r\n};\r\n\r\nexport default quiz;\r\n","import englishQuizzes from './en/';\r\nimport frenchQuizzes from './fr/';\r\nimport spanishQuizzes from './es/';\r\n\r\nconst messages = {\r\n en: englishQuizzes,\r\n fr: frenchQuizzes,\r\n es: spanishQuizzes,\r\n};\r\nexport default messages;\r\n","\r\n\r\n\r\n","import mod from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Quiz.vue?vue&type=script&lang=js&\"; export default mod; export * from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Quiz.vue?vue&type=script&lang=js&\"","import { render, staticRenderFns } from \"./Quiz.vue?vue&type=template&id=0e3ab080&\"\nimport script from \"./Quiz.vue?vue&type=script&lang=js&\"\nexport * from \"./Quiz.vue?vue&type=script&lang=js&\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","\r\n\r\n\r\n\r\n\r\n","import mod from \"-!../node_modules/cache-loader/dist/cjs.js??ref--13-0!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js!../node_modules/cache-loader/dist/cjs.js??ref--1-0!../node_modules/vue-loader/lib/index.js??vue-loader-options!./App.vue?vue&type=script&lang=js&\"; export default mod; export * from \"-!../node_modules/cache-loader/dist/cjs.js??ref--13-0!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js!../node_modules/cache-loader/dist/cjs.js??ref--1-0!../node_modules/vue-loader/lib/index.js??vue-loader-options!./App.vue?vue&type=script&lang=js&\"","import { render, staticRenderFns } from \"./App.vue?vue&type=template&id=a5b3bb08&\"\nimport script from \"./App.vue?vue&type=script&lang=js&\"\nexport * from \"./App.vue?vue&type=script&lang=js&\"\nimport style0 from \"./App.vue?vue&type=style&index=0&lang=css&\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',_vm._l((_vm.questions[_vm.currLocale]),function(q){return _c('div',{key:q.id},_vm._l((q.quizzes),function(quiz){return _c('router-link',{key:quiz.id,staticClass:\"link\",attrs:{\"to\":(\"quiz/\" + (quiz.id))}},[_vm._v(\" \"+_vm._s(quiz.title)+\" \")])}),1)}),0)}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\r\n\r\n","import mod from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Home.vue?vue&type=script&lang=js&\"; export default mod; export * from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Home.vue?vue&type=script&lang=js&\"","import { render, staticRenderFns } from \"./Home.vue?vue&type=template&id=542e9314&\"\nimport script from \"./Home.vue?vue&type=script&lang=js&\"\nexport * from \"./Home.vue?vue&type=script&lang=js&\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _vm._m(0)}\nvar staticRenderFns = [function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',[_c('p',[_vm._v(\"Sorry, wrong number!\")])])}]\n\nexport { render, staticRenderFns }","import { render, staticRenderFns } from \"./NotFound.vue?vue&type=template&id=53884ed4&\"\nvar script = {}\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","import Vue from 'vue';\r\nimport Router from 'vue-router';\r\nimport Home from '@/views/Home.vue';\r\nimport Quiz from '@/components/Quiz.vue';\r\nimport NotFound from '@/views/NotFound.vue';\r\nVue.use(Router);\r\n\r\nconst router = new Router({\r\n\tmode: 'history',\r\n\tbase: process.env.BASE_URL,\r\n\troutes: [\r\n\t\t{\r\n\t\t\tpath: '/',\r\n\t\t\tname: 'home',\r\n\t\t\tcomponent: Home,\r\n\t\t},\r\n\t\t{\r\n\t\t\tpath: '/quiz/:id',\r\n\t\t\tname: 'Quiz',\r\n\t\t\tcomponent: Quiz,\r\n\t\t},\r\n\t\t{\r\n\t\t\tpath: '/:pathMatch(.*)*',\r\n\t\t\tname: 'NotFound',\r\n\t\t\tcomponent: NotFound,\r\n\t\t\tmeta: { title: 'Not Found' },\r\n\t\t},\r\n\t],\r\n});\r\n\r\nrouter.beforeEach((to, from, next) => {\r\n\tnext();\r\n});\r\nexport default router;\r\n","import Vue from 'vue';\r\nimport App from './App.vue';\r\nVue.config.productionTip = false;\r\nimport router from './router';\r\n\r\nimport VueI18n from 'vue-i18n';\r\nVue.use(VueI18n);\r\n\r\nconst i18n = new VueI18n({\r\n\tlocale: 'en',\r\n\tfallbackLocale: 'en',\r\n});\r\n\r\nnew Vue({ i18n, router, render: (h) => h(App) }).$mount('#app');\r\n"],"sourceRoot":""} \ No newline at end of file diff --git a/quiz-app/dist/js/app.8ead00f7.js b/quiz-app/dist/js/app.8ead00f7.js deleted file mode 100644 index dff4b7ed..00000000 --- a/quiz-app/dist/js/app.8ead00f7.js +++ /dev/null @@ -1,2 +0,0 @@ -(function(e){function s(s){for(var a,o,n=s[0],c=s[1],l=s[2],d=0,w=[];d\r\n
\r\n
\r\n
\r\n \r\n
\r\n
\r\n

{{ q.complete }}

\r\n
\r\n

{{ q.error }}

\r\n
\r\n\r\n

{{quiz.title}}

\r\n\r\n\r\n

\r\n {{ quiz.quiz[currentQuestion].questionText }}\r\n

\r\n
\r\n \r\n {{ option.answerText }}\r\n \r\n
\r\n
\r\n
\r\n
\r\n
\r\n
\r\n\r\n\r\n\r\n","import mod from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--12-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--0-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Quiz.vue?vue&type=script&lang=js&\"; export default mod; export * from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--12-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--0-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Quiz.vue?vue&type=script&lang=js&\"","import { render, staticRenderFns } from \"./Quiz.vue?vue&type=template&id=0e3ab080&\"\nimport script from \"./Quiz.vue?vue&type=script&lang=js&\"\nexport * from \"./Quiz.vue?vue&type=script&lang=js&\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","\r\n\r\n\r\n\r\n\r\n","import mod from \"-!../node_modules/cache-loader/dist/cjs.js??ref--12-0!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js!../node_modules/cache-loader/dist/cjs.js??ref--0-0!../node_modules/vue-loader/lib/index.js??vue-loader-options!./App.vue?vue&type=script&lang=js&\"; export default mod; export * from \"-!../node_modules/cache-loader/dist/cjs.js??ref--12-0!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js!../node_modules/cache-loader/dist/cjs.js??ref--0-0!../node_modules/vue-loader/lib/index.js??vue-loader-options!./App.vue?vue&type=script&lang=js&\"","import { render, staticRenderFns } from \"./App.vue?vue&type=template&id=a5b3bb08&\"\nimport script from \"./App.vue?vue&type=script&lang=js&\"\nexport * from \"./App.vue?vue&type=script&lang=js&\"\nimport style0 from \"./App.vue?vue&type=style&index=0&lang=css&\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',_vm._l((_vm.questions[_vm.currLocale]),function(q){return _c('div',{key:q.id},_vm._l((q.quizzes),function(quiz){return _c('router-link',{key:quiz.id,staticClass:\"link\",attrs:{\"to\":(\"quiz/\" + (quiz.id))}},[_vm._v(\" \"+_vm._s(quiz.title)+\" \")])}),1)}),0)}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\r\n\r\n","import mod from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--12-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--0-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Home.vue?vue&type=script&lang=js&\"; export default mod; export * from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--12-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--0-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Home.vue?vue&type=script&lang=js&\"","import { render, staticRenderFns } from \"./Home.vue?vue&type=template&id=542e9314&\"\nimport script from \"./Home.vue?vue&type=script&lang=js&\"\nexport * from \"./Home.vue?vue&type=script&lang=js&\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _vm._m(0)}\nvar staticRenderFns = [function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',[_c('p',[_vm._v(\"Sorry, wrong number!\")])])}]\n\nexport { render, staticRenderFns }","import { render, staticRenderFns } from \"./NotFound.vue?vue&type=template&id=53884ed4&\"\nvar script = {}\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","import Vue from 'vue';\r\nimport Router from 'vue-router';\r\nimport Home from '@/views/Home.vue';\r\nimport Quiz from '@/components/Quiz.vue';\r\nimport NotFound from '@/views/NotFound.vue';\r\nVue.use(Router);\r\n\r\nconst router = new Router({\r\n\tmode: 'history',\r\n\tbase: process.env.BASE_URL,\r\n\troutes: [\r\n\t\t{\r\n\t\t\tpath: '/',\r\n\t\t\tname: 'home',\r\n\t\t\tcomponent: Home,\r\n\t\t},\r\n\t\t{\r\n\t\t\tpath: '/quiz/:id',\r\n\t\t\tname: 'Quiz',\r\n\t\t\tcomponent: Quiz,\r\n\t\t},\r\n\t\t{\r\n\t\t\tpath: '/:pathMatch(.*)*',\r\n\t\t\tname: 'NotFound',\r\n\t\t\tcomponent: NotFound,\r\n\t\t\tmeta: { title: 'Not Found' },\r\n\t\t},\r\n\t],\r\n});\r\n\r\nrouter.beforeEach((to, from, next) => {\r\n\tnext();\r\n});\r\nexport default router;\r\n","import Vue from 'vue';\r\nimport App from './App.vue';\r\nVue.config.productionTip = false;\r\nimport router from './router';\r\n\r\nimport VueI18n from 'vue-i18n';\r\nVue.use(VueI18n);\r\n\r\nconst i18n = new VueI18n({\r\n\tlocale: 'en',\r\n\tfallbackLocale: 'en',\r\n});\r\n\r\nnew Vue({ i18n, router, render: (h) => h(App) }).$mount('#app');\r\n"],"sourceRoot":""} \ No newline at end of file diff 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Math.min;\n\n// Helper for a popular repeating case of the spec:\n// Let integer be ? 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'.' : '#') + key, options.forced);\n // contained in target\n if (!FORCED && targetProperty !== undefined) {\n if (typeof sourceProperty === typeof targetProperty) continue;\n copyConstructorProperties(sourceProperty, targetProperty);\n }\n // add a flag to not completely full polyfills\n if (options.sham || (targetProperty && targetProperty.sham)) {\n createNonEnumerableProperty(sourceProperty, 'sham', true);\n }\n // extend global\n redefine(target, key, sourceProperty, options);\n }\n};\n","var internalObjectKeys = require('../internals/object-keys-internal');\nvar enumBugKeys = require('../internals/enum-bug-keys');\n\nvar hiddenKeys = enumBugKeys.concat('length', 'prototype');\n\n// `Object.getOwnPropertyNames` method\n// https://tc39.github.io/ecma262/#sec-object.getownpropertynames\nexports.f = Object.getOwnPropertyNames || function getOwnPropertyNames(O) {\n return internalObjectKeys(O, hiddenKeys);\n};\n","'use strict';\nvar getBuiltIn = require('../internals/get-built-in');\nvar definePropertyModule = require('../internals/object-define-property');\nvar wellKnownSymbol = require('../internals/well-known-symbol');\nvar DESCRIPTORS = require('../internals/descriptors');\n\nvar SPECIES = wellKnownSymbol('species');\n\nmodule.exports = function (CONSTRUCTOR_NAME) {\n var Constructor = getBuiltIn(CONSTRUCTOR_NAME);\n var defineProperty = definePropertyModule.f;\n\n if (DESCRIPTORS && Constructor && !Constructor[SPECIES]) {\n defineProperty(Constructor, SPECIES, {\n configurable: true,\n get: function () { return this; }\n });\n }\n};\n","/* globals __VUE_SSR_CONTEXT__ */\n\n// IMPORTANT: Do NOT use ES2015 features in this file (except for modules).\n// This module is a runtime utility for cleaner component module output and will\n// be included in the final webpack user bundle.\n\nexport default function normalizeComponent (\n scriptExports,\n render,\n staticRenderFns,\n functionalTemplate,\n injectStyles,\n scopeId,\n moduleIdentifier, /* server only */\n shadowMode /* vue-cli only */\n) {\n // Vue.extend constructor export interop\n var options = typeof scriptExports === 'function'\n ? scriptExports.options\n : scriptExports\n\n // render functions\n if (render) {\n options.render = render\n options.staticRenderFns = staticRenderFns\n options._compiled = true\n }\n\n // functional template\n if (functionalTemplate) {\n options.functional = true\n }\n\n // scopedId\n if (scopeId) {\n options._scopeId = 'data-v-' + scopeId\n }\n\n var hook\n if (moduleIdentifier) { // server build\n hook = function (context) {\n // 2.3 injection\n context =\n context || // cached call\n (this.$vnode && this.$vnode.ssrContext) || // stateful\n (this.parent && this.parent.$vnode && this.parent.$vnode.ssrContext) // functional\n // 2.2 with runInNewContext: true\n if (!context && typeof __VUE_SSR_CONTEXT__ !== 'undefined') {\n context = __VUE_SSR_CONTEXT__\n }\n // inject component styles\n if (injectStyles) {\n injectStyles.call(this, context)\n }\n // register component module identifier for async chunk inferrence\n if (context && context._registeredComponents) {\n context._registeredComponents.add(moduleIdentifier)\n }\n }\n // used by ssr in case component is cached and beforeCreate\n // never gets called\n options._ssrRegister = hook\n } else if (injectStyles) {\n hook = shadowMode\n ? function () {\n injectStyles.call(\n this,\n (options.functional ? this.parent : this).$root.$options.shadowRoot\n )\n }\n : injectStyles\n }\n\n if (hook) {\n if (options.functional) {\n // for template-only hot-reload because in that case the render fn doesn't\n // go through the normalizer\n options._injectStyles = hook\n // register for functional component in vue file\n var originalRender = options.render\n options.render = function renderWithStyleInjection (h, context) {\n hook.call(context)\n return originalRender(h, context)\n }\n } else {\n // inject component registration as beforeCreate hook\n var existing = options.beforeCreate\n options.beforeCreate = existing\n ? 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Only returns true\n * for plain JavaScript objects.\n */\nfunction isPlainObject (obj) {\n return _toString.call(obj) === '[object Object]'\n}\n\nfunction isRegExp (v) {\n return _toString.call(v) === '[object RegExp]'\n}\n\n/**\n * Check if val is a valid array index.\n */\nfunction isValidArrayIndex (val) {\n var n = parseFloat(String(val));\n return n >= 0 && Math.floor(n) === n && isFinite(val)\n}\n\nfunction isPromise (val) {\n return (\n isDef(val) &&\n typeof val.then === 'function' &&\n typeof val.catch === 'function'\n )\n}\n\n/**\n * Convert a value to a string that is actually rendered.\n */\nfunction toString (val) {\n return val == null\n ? ''\n : Array.isArray(val) || (isPlainObject(val) && val.toString === _toString)\n ? JSON.stringify(val, null, 2)\n : String(val)\n}\n\n/**\n * Convert an input value to a number for persistence.\n * If the conversion fails, return original string.\n */\nfunction toNumber (val) {\n var n = parseFloat(val);\n return isNaN(n) ? val : n\n}\n\n/**\n * Make a map and return a function for checking if a key\n * is in that map.\n */\nfunction makeMap (\n str,\n expectsLowerCase\n) {\n var map = Object.create(null);\n var list = str.split(',');\n for (var i = 0; i < list.length; i++) {\n map[list[i]] = true;\n }\n return expectsLowerCase\n ? function (val) { return map[val.toLowerCase()]; }\n : function (val) { return map[val]; }\n}\n\n/**\n * Check if a tag is a built-in tag.\n */\nvar isBuiltInTag = makeMap('slot,component', true);\n\n/**\n * Check if an attribute is a reserved attribute.\n */\nvar isReservedAttribute = makeMap('key,ref,slot,slot-scope,is');\n\n/**\n * Remove an item from an array.\n */\nfunction remove (arr, item) {\n if (arr.length) {\n var index = arr.indexOf(item);\n if (index > -1) {\n return arr.splice(index, 1)\n }\n }\n}\n\n/**\n * Check whether an object has the property.\n */\nvar hasOwnProperty = Object.prototype.hasOwnProperty;\nfunction hasOwn (obj, key) {\n return hasOwnProperty.call(obj, key)\n}\n\n/**\n * Create a cached version of a pure function.\n */\nfunction cached (fn) {\n var cache = Object.create(null);\n return (function cachedFn (str) {\n var hit = cache[str];\n return hit || (cache[str] = fn(str))\n })\n}\n\n/**\n * Camelize a hyphen-delimited string.\n */\nvar camelizeRE = /-(\\w)/g;\nvar camelize = cached(function (str) {\n return str.replace(camelizeRE, function (_, c) { return c ? c.toUpperCase() : ''; })\n});\n\n/**\n * Capitalize a string.\n */\nvar capitalize = cached(function (str) {\n return str.charAt(0).toUpperCase() + str.slice(1)\n});\n\n/**\n * Hyphenate a camelCase string.\n */\nvar hyphenateRE = /\\B([A-Z])/g;\nvar hyphenate = cached(function (str) {\n return str.replace(hyphenateRE, '-$1').toLowerCase()\n});\n\n/**\n * Simple bind polyfill for environments that do not support it,\n * e.g., PhantomJS 1.x. Technically, we don't need this anymore\n * since native bind is now performant enough in most browsers.\n * But removing it would mean breaking code that was able to run in\n * PhantomJS 1.x, so this must be kept for backward compatibility.\n */\n\n/* istanbul ignore next */\nfunction polyfillBind (fn, ctx) {\n function boundFn (a) {\n var l = arguments.length;\n return l\n ? l > 1\n ? fn.apply(ctx, arguments)\n : fn.call(ctx, a)\n : fn.call(ctx)\n }\n\n boundFn._length = fn.length;\n return boundFn\n}\n\nfunction nativeBind (fn, ctx) {\n return fn.bind(ctx)\n}\n\nvar bind = Function.prototype.bind\n ? nativeBind\n : polyfillBind;\n\n/**\n * Convert an Array-like object to a real Array.\n */\nfunction toArray (list, start) {\n start = start || 0;\n var i = list.length - start;\n var ret = new Array(i);\n while (i--) {\n ret[i] = list[i + start];\n }\n return ret\n}\n\n/**\n * Mix properties into target object.\n */\nfunction extend (to, _from) {\n for (var key in _from) {\n to[key] = _from[key];\n }\n return to\n}\n\n/**\n * Merge an Array of Objects into a single Object.\n */\nfunction toObject (arr) {\n var res = {};\n for (var i = 0; i < arr.length; i++) {\n if (arr[i]) {\n extend(res, arr[i]);\n }\n }\n return res\n}\n\n/* eslint-disable no-unused-vars */\n\n/**\n * Perform no operation.\n * Stubbing args to make Flow happy without leaving useless transpiled code\n * with ...rest (https://flow.org/blog/2017/05/07/Strict-Function-Call-Arity/).\n */\nfunction noop (a, b, c) {}\n\n/**\n * Always return false.\n */\nvar no = function (a, b, c) { return false; };\n\n/* eslint-enable no-unused-vars */\n\n/**\n * Return the same value.\n */\nvar identity = function (_) { return _; };\n\n/**\n * Check if two values are loosely equal - that is,\n * if they are plain objects, do they have the same shape?\n */\nfunction looseEqual (a, b) {\n if (a === b) { return true }\n var isObjectA = isObject(a);\n var isObjectB = isObject(b);\n if (isObjectA && isObjectB) {\n try {\n var isArrayA = Array.isArray(a);\n var isArrayB = Array.isArray(b);\n if (isArrayA && isArrayB) {\n return a.length === b.length && a.every(function (e, i) {\n return looseEqual(e, b[i])\n })\n } else if (a instanceof Date && b instanceof Date) {\n return a.getTime() === b.getTime()\n } else if (!isArrayA && !isArrayB) {\n var keysA = Object.keys(a);\n var keysB = Object.keys(b);\n return keysA.length === keysB.length && keysA.every(function (key) {\n return looseEqual(a[key], b[key])\n })\n } else {\n /* istanbul ignore next */\n return false\n }\n } catch (e) {\n /* istanbul ignore next */\n return false\n }\n } else if (!isObjectA && !isObjectB) {\n return String(a) === String(b)\n } else {\n return false\n }\n}\n\n/**\n * Return the first index at which a loosely equal value can be\n * found in the array (if value is a plain object, the array must\n * contain an object of the same shape), or -1 if it is not present.\n */\nfunction looseIndexOf (arr, val) {\n for (var i = 0; i < arr.length; i++) {\n if (looseEqual(arr[i], val)) { return i }\n }\n return -1\n}\n\n/**\n * Ensure a function is called only once.\n */\nfunction once (fn) {\n var called = false;\n return function () {\n if (!called) {\n called = true;\n fn.apply(this, arguments);\n }\n }\n}\n\nvar SSR_ATTR = 'data-server-rendered';\n\nvar ASSET_TYPES = [\n 'component',\n 'directive',\n 'filter'\n];\n\nvar LIFECYCLE_HOOKS = [\n 'beforeCreate',\n 'created',\n 'beforeMount',\n 'mounted',\n 'beforeUpdate',\n 'updated',\n 'beforeDestroy',\n 'destroyed',\n 'activated',\n 'deactivated',\n 'errorCaptured',\n 'serverPrefetch'\n];\n\n/* */\n\n\n\nvar config = ({\n /**\n * Option merge strategies (used in core/util/options)\n */\n // $flow-disable-line\n optionMergeStrategies: Object.create(null),\n\n /**\n * Whether to suppress warnings.\n */\n silent: false,\n\n /**\n * Show production mode tip message on boot?\n */\n productionTip: process.env.NODE_ENV !== 'production',\n\n /**\n * Whether to enable devtools\n */\n devtools: process.env.NODE_ENV !== 'production',\n\n /**\n * Whether to record perf\n */\n performance: false,\n\n /**\n * Error handler for watcher errors\n */\n errorHandler: null,\n\n /**\n * Warn handler for watcher warns\n */\n warnHandler: null,\n\n /**\n * Ignore certain custom elements\n */\n ignoredElements: [],\n\n /**\n * Custom user key aliases for v-on\n */\n // $flow-disable-line\n keyCodes: Object.create(null),\n\n /**\n * Check if a tag is reserved so that it cannot be registered as a\n * component. This is platform-dependent and may be overwritten.\n */\n isReservedTag: no,\n\n /**\n * Check if an attribute is reserved so that it cannot be used as a component\n * prop. This is platform-dependent and may be overwritten.\n */\n isReservedAttr: no,\n\n /**\n * Check if a tag is an unknown element.\n * Platform-dependent.\n */\n isUnknownElement: no,\n\n /**\n * Get the namespace of an element\n */\n getTagNamespace: noop,\n\n /**\n * Parse the real tag name for the specific platform.\n */\n parsePlatformTagName: identity,\n\n /**\n * Check if an attribute must be bound using property, e.g. value\n * Platform-dependent.\n */\n mustUseProp: no,\n\n /**\n * Perform updates asynchronously. Intended to be used by Vue Test Utils\n * This will significantly reduce performance if set to false.\n */\n async: true,\n\n /**\n * Exposed for legacy reasons\n */\n _lifecycleHooks: LIFECYCLE_HOOKS\n});\n\n/* */\n\n/**\n * unicode letters used for parsing html tags, component names and property paths.\n * using https://www.w3.org/TR/html53/semantics-scripting.html#potentialcustomelementname\n * skipping \\u10000-\\uEFFFF due to it freezing up PhantomJS\n */\nvar unicodeRegExp = /a-zA-Z\\u00B7\\u00C0-\\u00D6\\u00D8-\\u00F6\\u00F8-\\u037D\\u037F-\\u1FFF\\u200C-\\u200D\\u203F-\\u2040\\u2070-\\u218F\\u2C00-\\u2FEF\\u3001-\\uD7FF\\uF900-\\uFDCF\\uFDF0-\\uFFFD/;\n\n/**\n * Check if a string starts with $ or _\n */\nfunction isReserved (str) {\n var c = (str + '').charCodeAt(0);\n return c === 0x24 || c === 0x5F\n}\n\n/**\n * Define a property.\n */\nfunction def (obj, key, val, enumerable) {\n Object.defineProperty(obj, key, {\n value: val,\n enumerable: !!enumerable,\n writable: true,\n configurable: true\n });\n}\n\n/**\n * Parse simple path.\n */\nvar bailRE = new RegExp((\"[^\" + (unicodeRegExp.source) + \".$_\\\\d]\"));\nfunction parsePath (path) {\n if (bailRE.test(path)) {\n return\n }\n var segments = path.split('.');\n return function (obj) {\n for (var i = 0; i < segments.length; i++) {\n if (!obj) { return }\n obj = obj[segments[i]];\n }\n return obj\n }\n}\n\n/* */\n\n// can we use __proto__?\nvar hasProto = '__proto__' in {};\n\n// Browser environment sniffing\nvar inBrowser = typeof window !== 'undefined';\nvar inWeex = typeof WXEnvironment !== 'undefined' && !!WXEnvironment.platform;\nvar weexPlatform = inWeex && WXEnvironment.platform.toLowerCase();\nvar UA = inBrowser && window.navigator.userAgent.toLowerCase();\nvar isIE = UA && /msie|trident/.test(UA);\nvar isIE9 = UA && UA.indexOf('msie 9.0') > 0;\nvar isEdge = UA && UA.indexOf('edge/') > 0;\nvar isAndroid = (UA && UA.indexOf('android') > 0) || (weexPlatform === 'android');\nvar isIOS = (UA && /iphone|ipad|ipod|ios/.test(UA)) || (weexPlatform === 'ios');\nvar isChrome = UA && /chrome\\/\\d+/.test(UA) && !isEdge;\nvar isPhantomJS = UA && /phantomjs/.test(UA);\nvar isFF = UA && UA.match(/firefox\\/(\\d+)/);\n\n// Firefox has a \"watch\" function on Object.prototype...\nvar nativeWatch = ({}).watch;\n\nvar supportsPassive = false;\nif (inBrowser) {\n try {\n var opts = {};\n Object.defineProperty(opts, 'passive', ({\n get: function get () {\n /* istanbul ignore next */\n supportsPassive = true;\n }\n })); // https://github.com/facebook/flow/issues/285\n window.addEventListener('test-passive', null, opts);\n } catch (e) {}\n}\n\n// this needs to be lazy-evaled because vue may be required before\n// vue-server-renderer can set VUE_ENV\nvar _isServer;\nvar isServerRendering = function () {\n if (_isServer === undefined) {\n /* istanbul ignore if */\n if (!inBrowser && !inWeex && typeof global !== 'undefined') {\n // detect presence of vue-server-renderer and avoid\n // Webpack shimming the process\n _isServer = global['process'] && global['process'].env.VUE_ENV === 'server';\n } else {\n _isServer = false;\n }\n }\n return _isServer\n};\n\n// detect devtools\nvar devtools = inBrowser && window.__VUE_DEVTOOLS_GLOBAL_HOOK__;\n\n/* istanbul ignore next */\nfunction isNative (Ctor) {\n return typeof Ctor === 'function' && /native code/.test(Ctor.toString())\n}\n\nvar hasSymbol =\n typeof Symbol !== 'undefined' && isNative(Symbol) &&\n typeof Reflect !== 'undefined' && isNative(Reflect.ownKeys);\n\nvar _Set;\n/* istanbul ignore if */ // $flow-disable-line\nif (typeof Set !== 'undefined' && isNative(Set)) {\n // use native Set when available.\n _Set = Set;\n} else {\n // a non-standard Set polyfill that only works with primitive keys.\n _Set = /*@__PURE__*/(function () {\n function Set () {\n this.set = Object.create(null);\n }\n Set.prototype.has = function has (key) {\n return this.set[key] === true\n };\n Set.prototype.add = function add (key) {\n this.set[key] = true;\n };\n Set.prototype.clear = function clear () {\n this.set = Object.create(null);\n };\n\n return Set;\n }());\n}\n\n/* */\n\nvar warn = noop;\nvar tip = noop;\nvar generateComponentTrace = (noop); // work around flow check\nvar formatComponentName = (noop);\n\nif (process.env.NODE_ENV !== 'production') {\n var hasConsole = typeof console !== 'undefined';\n var classifyRE = /(?:^|[-_])(\\w)/g;\n var classify = function (str) { return str\n .replace(classifyRE, function (c) { return c.toUpperCase(); })\n .replace(/[-_]/g, ''); };\n\n warn = function (msg, vm) {\n var trace = vm ? generateComponentTrace(vm) : '';\n\n if (config.warnHandler) {\n config.warnHandler.call(null, msg, vm, trace);\n } else if (hasConsole && (!config.silent)) {\n console.error((\"[Vue warn]: \" + msg + trace));\n }\n };\n\n tip = function (msg, vm) {\n if (hasConsole && (!config.silent)) {\n console.warn(\"[Vue tip]: \" + msg + (\n vm ? generateComponentTrace(vm) : ''\n ));\n }\n };\n\n formatComponentName = function (vm, includeFile) {\n if (vm.$root === vm) {\n return ''\n }\n var options = typeof vm === 'function' && vm.cid != null\n ? vm.options\n : vm._isVue\n ? vm.$options || vm.constructor.options\n : vm;\n var name = options.name || options._componentTag;\n var file = options.__file;\n if (!name && file) {\n var match = file.match(/([^/\\\\]+)\\.vue$/);\n name = match && match[1];\n }\n\n return (\n (name ? (\"<\" + (classify(name)) + \">\") : \"\") +\n (file && includeFile !== false ? (\" at \" + file) : '')\n )\n };\n\n var repeat = function (str, n) {\n var res = '';\n while (n) {\n if (n % 2 === 1) { res += str; }\n if (n > 1) { str += str; }\n n >>= 1;\n }\n return res\n };\n\n generateComponentTrace = function (vm) {\n if (vm._isVue && vm.$parent) {\n var tree = [];\n var currentRecursiveSequence = 0;\n while (vm) {\n if (tree.length > 0) {\n var last = tree[tree.length - 1];\n if (last.constructor === vm.constructor) {\n currentRecursiveSequence++;\n vm = vm.$parent;\n continue\n } else if (currentRecursiveSequence > 0) {\n tree[tree.length - 1] = [last, currentRecursiveSequence];\n currentRecursiveSequence = 0;\n }\n }\n tree.push(vm);\n vm = vm.$parent;\n }\n return '\\n\\nfound in\\n\\n' + tree\n .map(function (vm, i) { return (\"\" + (i === 0 ? '---> ' : repeat(' ', 5 + i * 2)) + (Array.isArray(vm)\n ? ((formatComponentName(vm[0])) + \"... (\" + (vm[1]) + \" recursive calls)\")\n : formatComponentName(vm))); })\n .join('\\n')\n } else {\n return (\"\\n\\n(found in \" + (formatComponentName(vm)) + \")\")\n }\n };\n}\n\n/* */\n\nvar uid = 0;\n\n/**\n * A dep is an observable that can have multiple\n * directives subscribing to it.\n */\nvar Dep = function Dep () {\n this.id = uid++;\n this.subs = [];\n};\n\nDep.prototype.addSub = function addSub (sub) {\n this.subs.push(sub);\n};\n\nDep.prototype.removeSub = function removeSub (sub) {\n remove(this.subs, sub);\n};\n\nDep.prototype.depend = function depend () {\n if (Dep.target) {\n Dep.target.addDep(this);\n }\n};\n\nDep.prototype.notify = function notify () {\n // stabilize the subscriber list first\n var subs = this.subs.slice();\n if (process.env.NODE_ENV !== 'production' && !config.async) {\n // subs aren't sorted in scheduler if not running async\n // we need to sort them now to make sure they fire in correct\n // order\n subs.sort(function (a, b) { return a.id - b.id; });\n }\n for (var i = 0, l = subs.length; i < l; i++) {\n subs[i].update();\n }\n};\n\n// The current target watcher being evaluated.\n// This is globally unique because only one watcher\n// can be evaluated at a time.\nDep.target = null;\nvar targetStack = [];\n\nfunction pushTarget (target) {\n targetStack.push(target);\n Dep.target = target;\n}\n\nfunction popTarget () {\n targetStack.pop();\n Dep.target = targetStack[targetStack.length - 1];\n}\n\n/* */\n\nvar VNode = function VNode (\n tag,\n data,\n children,\n text,\n elm,\n context,\n componentOptions,\n asyncFactory\n) {\n this.tag = tag;\n this.data = data;\n this.children = children;\n this.text = text;\n this.elm = elm;\n this.ns = undefined;\n this.context = context;\n this.fnContext = undefined;\n this.fnOptions = undefined;\n this.fnScopeId = undefined;\n this.key = data && data.key;\n this.componentOptions = componentOptions;\n this.componentInstance = undefined;\n this.parent = undefined;\n this.raw = false;\n this.isStatic = false;\n this.isRootInsert = true;\n this.isComment = false;\n this.isCloned = false;\n this.isOnce = false;\n this.asyncFactory = asyncFactory;\n this.asyncMeta = undefined;\n this.isAsyncPlaceholder = false;\n};\n\nvar prototypeAccessors = { child: { configurable: true } };\n\n// DEPRECATED: alias for componentInstance for backwards compat.\n/* istanbul ignore next */\nprototypeAccessors.child.get = function () {\n return this.componentInstance\n};\n\nObject.defineProperties( VNode.prototype, prototypeAccessors );\n\nvar createEmptyVNode = function (text) {\n if ( text === void 0 ) text = '';\n\n var node = new VNode();\n node.text = text;\n node.isComment = true;\n return node\n};\n\nfunction createTextVNode (val) {\n return new VNode(undefined, undefined, undefined, String(val))\n}\n\n// optimized shallow clone\n// used for static nodes and slot nodes because they may be reused across\n// multiple renders, cloning them avoids errors when DOM manipulations rely\n// on their elm reference.\nfunction cloneVNode (vnode) {\n var cloned = new VNode(\n vnode.tag,\n vnode.data,\n // #7975\n // clone children array to avoid mutating original in case of cloning\n // a child.\n vnode.children && vnode.children.slice(),\n vnode.text,\n vnode.elm,\n vnode.context,\n vnode.componentOptions,\n vnode.asyncFactory\n );\n cloned.ns = vnode.ns;\n cloned.isStatic = vnode.isStatic;\n cloned.key = vnode.key;\n cloned.isComment = vnode.isComment;\n cloned.fnContext = vnode.fnContext;\n cloned.fnOptions = vnode.fnOptions;\n cloned.fnScopeId = vnode.fnScopeId;\n cloned.asyncMeta = vnode.asyncMeta;\n cloned.isCloned = true;\n return cloned\n}\n\n/*\n * not type checking this file because flow doesn't play well with\n * dynamically accessing methods on Array prototype\n */\n\nvar arrayProto = Array.prototype;\nvar arrayMethods = Object.create(arrayProto);\n\nvar methodsToPatch = [\n 'push',\n 'pop',\n 'shift',\n 'unshift',\n 'splice',\n 'sort',\n 'reverse'\n];\n\n/**\n * Intercept mutating methods and emit events\n */\nmethodsToPatch.forEach(function (method) {\n // cache original method\n var original = arrayProto[method];\n def(arrayMethods, method, function mutator () {\n var args = [], len = arguments.length;\n while ( len-- ) args[ len ] = arguments[ len ];\n\n var result = original.apply(this, args);\n var ob = this.__ob__;\n var inserted;\n switch (method) {\n case 'push':\n case 'unshift':\n inserted = args;\n break\n case 'splice':\n inserted = args.slice(2);\n break\n }\n if (inserted) { ob.observeArray(inserted); }\n // notify change\n ob.dep.notify();\n return result\n });\n});\n\n/* */\n\nvar arrayKeys = Object.getOwnPropertyNames(arrayMethods);\n\n/**\n * In some cases we may want to disable observation inside a component's\n * update computation.\n */\nvar shouldObserve = true;\n\nfunction toggleObserving (value) {\n shouldObserve = value;\n}\n\n/**\n * Observer class that is attached to each observed\n * object. Once attached, the observer converts the target\n * object's property keys into getter/setters that\n * collect dependencies and dispatch updates.\n */\nvar Observer = function Observer (value) {\n this.value = value;\n this.dep = new Dep();\n this.vmCount = 0;\n def(value, '__ob__', this);\n if (Array.isArray(value)) {\n if (hasProto) {\n protoAugment(value, arrayMethods);\n } else {\n copyAugment(value, arrayMethods, arrayKeys);\n }\n this.observeArray(value);\n } else {\n this.walk(value);\n }\n};\n\n/**\n * Walk through all properties and convert them into\n * getter/setters. This method should only be called when\n * value type is Object.\n */\nObserver.prototype.walk = function walk (obj) {\n var keys = Object.keys(obj);\n for (var i = 0; i < keys.length; i++) {\n defineReactive$$1(obj, keys[i]);\n }\n};\n\n/**\n * Observe a list of Array items.\n */\nObserver.prototype.observeArray = function observeArray (items) {\n for (var i = 0, l = items.length; i < l; i++) {\n observe(items[i]);\n }\n};\n\n// helpers\n\n/**\n * Augment a target Object or Array by intercepting\n * the prototype chain using __proto__\n */\nfunction protoAugment (target, src) {\n /* eslint-disable no-proto */\n target.__proto__ = src;\n /* eslint-enable no-proto */\n}\n\n/**\n * Augment a target Object or Array by defining\n * hidden properties.\n */\n/* istanbul ignore next */\nfunction copyAugment (target, src, keys) {\n for (var i = 0, l = keys.length; i < l; i++) {\n var key = keys[i];\n def(target, key, src[key]);\n }\n}\n\n/**\n * Attempt to create an observer instance for a value,\n * returns the new observer if successfully observed,\n * or the existing observer if the value already has one.\n */\nfunction observe (value, asRootData) {\n if (!isObject(value) || value instanceof VNode) {\n return\n }\n var ob;\n if (hasOwn(value, '__ob__') && value.__ob__ instanceof Observer) {\n ob = value.__ob__;\n } else if (\n shouldObserve &&\n !isServerRendering() &&\n (Array.isArray(value) || isPlainObject(value)) &&\n Object.isExtensible(value) &&\n !value._isVue\n ) {\n ob = new Observer(value);\n }\n if (asRootData && ob) {\n ob.vmCount++;\n }\n return ob\n}\n\n/**\n * Define a reactive property on an Object.\n */\nfunction defineReactive$$1 (\n obj,\n key,\n val,\n customSetter,\n shallow\n) {\n var dep = new Dep();\n\n var property = Object.getOwnPropertyDescriptor(obj, key);\n if (property && property.configurable === false) {\n return\n }\n\n // cater for pre-defined getter/setters\n var getter = property && property.get;\n var setter = property && property.set;\n if ((!getter || setter) && arguments.length === 2) {\n val = obj[key];\n }\n\n var childOb = !shallow && observe(val);\n Object.defineProperty(obj, key, {\n enumerable: true,\n configurable: true,\n get: function reactiveGetter () {\n var value = getter ? getter.call(obj) : val;\n if (Dep.target) {\n dep.depend();\n if (childOb) {\n childOb.dep.depend();\n if (Array.isArray(value)) {\n dependArray(value);\n }\n }\n }\n return value\n },\n set: function reactiveSetter (newVal) {\n var value = getter ? getter.call(obj) : val;\n /* eslint-disable no-self-compare */\n if (newVal === value || (newVal !== newVal && value !== value)) {\n return\n }\n /* eslint-enable no-self-compare */\n if (process.env.NODE_ENV !== 'production' && customSetter) {\n customSetter();\n }\n // #7981: for accessor properties without setter\n if (getter && !setter) { return }\n if (setter) {\n setter.call(obj, newVal);\n } else {\n val = newVal;\n }\n childOb = !shallow && observe(newVal);\n dep.notify();\n }\n });\n}\n\n/**\n * Set a property on an object. Adds the new property and\n * triggers change notification if the property doesn't\n * already exist.\n */\nfunction set (target, key, val) {\n if (process.env.NODE_ENV !== 'production' &&\n (isUndef(target) || isPrimitive(target))\n ) {\n warn((\"Cannot set reactive property on undefined, null, or primitive value: \" + ((target))));\n }\n if (Array.isArray(target) && isValidArrayIndex(key)) {\n target.length = Math.max(target.length, key);\n target.splice(key, 1, val);\n return val\n }\n if (key in target && !(key in Object.prototype)) {\n target[key] = val;\n return val\n }\n var ob = (target).__ob__;\n if (target._isVue || (ob && ob.vmCount)) {\n process.env.NODE_ENV !== 'production' && warn(\n 'Avoid adding reactive properties to a Vue instance or its root $data ' +\n 'at runtime - declare it upfront in the data option.'\n );\n return val\n }\n if (!ob) {\n target[key] = val;\n return val\n }\n defineReactive$$1(ob.value, key, val);\n ob.dep.notify();\n return val\n}\n\n/**\n * Delete a property and trigger change if necessary.\n */\nfunction del (target, key) {\n if (process.env.NODE_ENV !== 'production' &&\n (isUndef(target) || isPrimitive(target))\n ) {\n warn((\"Cannot delete reactive property on undefined, null, or primitive value: \" + ((target))));\n }\n if (Array.isArray(target) && isValidArrayIndex(key)) {\n target.splice(key, 1);\n return\n }\n var ob = (target).__ob__;\n if (target._isVue || (ob && ob.vmCount)) {\n process.env.NODE_ENV !== 'production' && warn(\n 'Avoid deleting properties on a Vue instance or its root $data ' +\n '- just set it to null.'\n );\n return\n }\n if (!hasOwn(target, key)) {\n return\n }\n delete target[key];\n if (!ob) {\n return\n }\n ob.dep.notify();\n}\n\n/**\n * Collect dependencies on array elements when the array is touched, since\n * we cannot intercept array element access like property getters.\n */\nfunction dependArray (value) {\n for (var e = (void 0), i = 0, l = value.length; i < l; i++) {\n e = value[i];\n e && e.__ob__ && e.__ob__.dep.depend();\n if (Array.isArray(e)) {\n dependArray(e);\n }\n }\n}\n\n/* */\n\n/**\n * Option overwriting strategies are functions that handle\n * how to merge a parent option value and a child option\n * value into the final value.\n */\nvar strats = config.optionMergeStrategies;\n\n/**\n * Options with restrictions\n */\nif (process.env.NODE_ENV !== 'production') {\n strats.el = strats.propsData = function (parent, child, vm, key) {\n if (!vm) {\n warn(\n \"option \\\"\" + key + \"\\\" can only be used during instance \" +\n 'creation with the `new` keyword.'\n );\n }\n return defaultStrat(parent, child)\n };\n}\n\n/**\n * Helper that recursively merges two data objects together.\n */\nfunction mergeData (to, from) {\n if (!from) { return to }\n var key, toVal, fromVal;\n\n var keys = hasSymbol\n ? Reflect.ownKeys(from)\n : Object.keys(from);\n\n for (var i = 0; i < keys.length; i++) {\n key = keys[i];\n // in case the object is already observed...\n if (key === '__ob__') { continue }\n toVal = to[key];\n fromVal = from[key];\n if (!hasOwn(to, key)) {\n set(to, key, fromVal);\n } else if (\n toVal !== fromVal &&\n isPlainObject(toVal) &&\n isPlainObject(fromVal)\n ) {\n mergeData(toVal, fromVal);\n }\n }\n return to\n}\n\n/**\n * Data\n */\nfunction mergeDataOrFn (\n parentVal,\n childVal,\n vm\n) {\n if (!vm) {\n // in a Vue.extend merge, both should be functions\n if (!childVal) {\n return parentVal\n }\n if (!parentVal) {\n return childVal\n }\n // when parentVal & childVal are both present,\n // we need to return a function that returns the\n // merged result of both functions... no need to\n // check if parentVal is a function here because\n // it has to be a function to pass previous merges.\n return function mergedDataFn () {\n return mergeData(\n typeof childVal === 'function' ? childVal.call(this, this) : childVal,\n typeof parentVal === 'function' ? parentVal.call(this, this) : parentVal\n )\n }\n } else {\n return function mergedInstanceDataFn () {\n // instance merge\n var instanceData = typeof childVal === 'function'\n ? childVal.call(vm, vm)\n : childVal;\n var defaultData = typeof parentVal === 'function'\n ? parentVal.call(vm, vm)\n : parentVal;\n if (instanceData) {\n return mergeData(instanceData, defaultData)\n } else {\n return defaultData\n }\n }\n }\n}\n\nstrats.data = function (\n parentVal,\n childVal,\n vm\n) {\n if (!vm) {\n if (childVal && typeof childVal !== 'function') {\n process.env.NODE_ENV !== 'production' && warn(\n 'The \"data\" option should be a function ' +\n 'that returns a per-instance value in component ' +\n 'definitions.',\n vm\n );\n\n return parentVal\n }\n return mergeDataOrFn(parentVal, childVal)\n }\n\n return mergeDataOrFn(parentVal, childVal, vm)\n};\n\n/**\n * Hooks and props are merged as arrays.\n */\nfunction mergeHook (\n parentVal,\n childVal\n) {\n var res = childVal\n ? parentVal\n ? parentVal.concat(childVal)\n : Array.isArray(childVal)\n ? childVal\n : [childVal]\n : parentVal;\n return res\n ? dedupeHooks(res)\n : res\n}\n\nfunction dedupeHooks (hooks) {\n var res = [];\n for (var i = 0; i < hooks.length; i++) {\n if (res.indexOf(hooks[i]) === -1) {\n res.push(hooks[i]);\n }\n }\n return res\n}\n\nLIFECYCLE_HOOKS.forEach(function (hook) {\n strats[hook] = mergeHook;\n});\n\n/**\n * Assets\n *\n * When a vm is present (instance creation), we need to do\n * a three-way merge between constructor options, instance\n * options and parent options.\n */\nfunction mergeAssets (\n parentVal,\n childVal,\n vm,\n key\n) {\n var res = Object.create(parentVal || null);\n if (childVal) {\n process.env.NODE_ENV !== 'production' && assertObjectType(key, childVal, vm);\n return extend(res, childVal)\n } else {\n return res\n }\n}\n\nASSET_TYPES.forEach(function (type) {\n strats[type + 's'] = mergeAssets;\n});\n\n/**\n * Watchers.\n *\n * Watchers hashes should not overwrite one\n * another, so we merge them as arrays.\n */\nstrats.watch = function (\n parentVal,\n childVal,\n vm,\n key\n) {\n // work around Firefox's Object.prototype.watch...\n if (parentVal === nativeWatch) { parentVal = undefined; }\n if (childVal === nativeWatch) { childVal = undefined; }\n /* istanbul ignore if */\n if (!childVal) { return Object.create(parentVal || null) }\n if (process.env.NODE_ENV !== 'production') {\n assertObjectType(key, childVal, vm);\n }\n if (!parentVal) { return childVal }\n var ret = {};\n extend(ret, parentVal);\n for (var key$1 in childVal) {\n var parent = ret[key$1];\n var child = childVal[key$1];\n if (parent && !Array.isArray(parent)) {\n parent = [parent];\n }\n ret[key$1] = parent\n ? parent.concat(child)\n : Array.isArray(child) ? child : [child];\n }\n return ret\n};\n\n/**\n * Other object hashes.\n */\nstrats.props =\nstrats.methods =\nstrats.inject =\nstrats.computed = function (\n parentVal,\n childVal,\n vm,\n key\n) {\n if (childVal && process.env.NODE_ENV !== 'production') {\n assertObjectType(key, childVal, vm);\n }\n if (!parentVal) { return childVal }\n var ret = Object.create(null);\n extend(ret, parentVal);\n if (childVal) { extend(ret, childVal); }\n return ret\n};\nstrats.provide = mergeDataOrFn;\n\n/**\n * Default strategy.\n */\nvar defaultStrat = function (parentVal, childVal) {\n return childVal === undefined\n ? parentVal\n : childVal\n};\n\n/**\n * Validate component names\n */\nfunction checkComponents (options) {\n for (var key in options.components) {\n validateComponentName(key);\n }\n}\n\nfunction validateComponentName (name) {\n if (!new RegExp((\"^[a-zA-Z][\\\\-\\\\.0-9_\" + (unicodeRegExp.source) + \"]*$\")).test(name)) {\n warn(\n 'Invalid component name: \"' + name + '\". Component names ' +\n 'should conform to valid custom element name in html5 specification.'\n );\n }\n if (isBuiltInTag(name) || config.isReservedTag(name)) {\n warn(\n 'Do not use built-in or reserved HTML elements as component ' +\n 'id: ' + name\n );\n }\n}\n\n/**\n * Ensure all props option syntax are normalized into the\n * Object-based format.\n */\nfunction normalizeProps (options, vm) {\n var props = options.props;\n if (!props) { return }\n var res = {};\n var i, val, name;\n if (Array.isArray(props)) {\n i = props.length;\n while (i--) {\n val = props[i];\n if (typeof val === 'string') {\n name = camelize(val);\n res[name] = { type: null };\n } else if (process.env.NODE_ENV !== 'production') {\n warn('props must be strings when using array syntax.');\n }\n }\n } else if (isPlainObject(props)) {\n for (var key in props) {\n val = props[key];\n name = camelize(key);\n res[name] = isPlainObject(val)\n ? val\n : { type: val };\n }\n } else if (process.env.NODE_ENV !== 'production') {\n warn(\n \"Invalid value for option \\\"props\\\": expected an Array or an Object, \" +\n \"but got \" + (toRawType(props)) + \".\",\n vm\n );\n }\n options.props = res;\n}\n\n/**\n * Normalize all injections into Object-based format\n */\nfunction normalizeInject (options, vm) {\n var inject = options.inject;\n if (!inject) { return }\n var normalized = options.inject = {};\n if (Array.isArray(inject)) {\n for (var i = 0; i < inject.length; i++) {\n normalized[inject[i]] = { from: inject[i] };\n }\n } else if (isPlainObject(inject)) {\n for (var key in inject) {\n var val = inject[key];\n normalized[key] = isPlainObject(val)\n ? extend({ from: key }, val)\n : { from: val };\n }\n } else if (process.env.NODE_ENV !== 'production') {\n warn(\n \"Invalid value for option \\\"inject\\\": expected an Array or an Object, \" +\n \"but got \" + (toRawType(inject)) + \".\",\n vm\n );\n }\n}\n\n/**\n * Normalize raw function directives into object format.\n */\nfunction normalizeDirectives (options) {\n var dirs = options.directives;\n if (dirs) {\n for (var key in dirs) {\n var def$$1 = dirs[key];\n if (typeof def$$1 === 'function') {\n dirs[key] = { bind: def$$1, update: def$$1 };\n }\n }\n }\n}\n\nfunction assertObjectType (name, value, vm) {\n if (!isPlainObject(value)) {\n warn(\n \"Invalid value for option \\\"\" + name + \"\\\": expected an Object, \" +\n \"but got \" + (toRawType(value)) + \".\",\n vm\n );\n }\n}\n\n/**\n * Merge two option objects into a new one.\n * Core utility used in both instantiation and inheritance.\n */\nfunction mergeOptions (\n parent,\n child,\n vm\n) {\n if (process.env.NODE_ENV !== 'production') {\n checkComponents(child);\n }\n\n if (typeof child === 'function') {\n child = child.options;\n }\n\n normalizeProps(child, vm);\n normalizeInject(child, vm);\n normalizeDirectives(child);\n\n // Apply extends and mixins on the child options,\n // but only if it is a raw options object that isn't\n // the result of another mergeOptions call.\n // Only merged options has the _base property.\n if (!child._base) {\n if (child.extends) {\n parent = mergeOptions(parent, child.extends, vm);\n }\n if (child.mixins) {\n for (var i = 0, l = child.mixins.length; i < l; i++) {\n parent = mergeOptions(parent, child.mixins[i], vm);\n }\n }\n }\n\n var options = {};\n var key;\n for (key in parent) {\n mergeField(key);\n }\n for (key in child) {\n if (!hasOwn(parent, key)) {\n mergeField(key);\n }\n }\n function mergeField (key) {\n var strat = strats[key] || defaultStrat;\n options[key] = strat(parent[key], child[key], vm, key);\n }\n return options\n}\n\n/**\n * Resolve an asset.\n * This function is used because child instances need access\n * to assets defined in its ancestor chain.\n */\nfunction resolveAsset (\n options,\n type,\n id,\n warnMissing\n) {\n /* istanbul ignore if */\n if (typeof id !== 'string') {\n return\n }\n var assets = options[type];\n // check local registration variations first\n if (hasOwn(assets, id)) { return assets[id] }\n var camelizedId = camelize(id);\n if (hasOwn(assets, camelizedId)) { return assets[camelizedId] }\n var PascalCaseId = capitalize(camelizedId);\n if (hasOwn(assets, PascalCaseId)) { return assets[PascalCaseId] }\n // fallback to prototype chain\n var res = assets[id] || assets[camelizedId] || assets[PascalCaseId];\n if (process.env.NODE_ENV !== 'production' && warnMissing && !res) {\n warn(\n 'Failed to resolve ' + type.slice(0, -1) + ': ' + id,\n options\n );\n }\n return res\n}\n\n/* */\n\n\n\nfunction validateProp (\n key,\n propOptions,\n propsData,\n vm\n) {\n var prop = propOptions[key];\n var absent = !hasOwn(propsData, key);\n var value = propsData[key];\n // boolean casting\n var booleanIndex = getTypeIndex(Boolean, prop.type);\n if (booleanIndex > -1) {\n if (absent && !hasOwn(prop, 'default')) {\n value = false;\n } else if (value === '' || value === hyphenate(key)) {\n // only cast empty string / same name to boolean if\n // boolean has higher priority\n var stringIndex = getTypeIndex(String, prop.type);\n if (stringIndex < 0 || booleanIndex < stringIndex) {\n value = true;\n }\n }\n }\n // check default value\n if (value === undefined) {\n value = getPropDefaultValue(vm, prop, key);\n // since the default value is a fresh copy,\n // make sure to observe it.\n var prevShouldObserve = shouldObserve;\n toggleObserving(true);\n observe(value);\n toggleObserving(prevShouldObserve);\n }\n if (\n process.env.NODE_ENV !== 'production' &&\n // skip validation for weex recycle-list child component props\n !(false)\n ) {\n assertProp(prop, key, value, vm, absent);\n }\n return value\n}\n\n/**\n * Get the default value of a prop.\n */\nfunction getPropDefaultValue (vm, prop, key) {\n // no default, return undefined\n if (!hasOwn(prop, 'default')) {\n return undefined\n }\n var def = prop.default;\n // warn against non-factory defaults for Object & Array\n if (process.env.NODE_ENV !== 'production' && isObject(def)) {\n warn(\n 'Invalid default value for prop \"' + key + '\": ' +\n 'Props with type Object/Array must use a factory function ' +\n 'to return the default value.',\n vm\n );\n }\n // the raw prop value was also undefined from previous render,\n // return previous default value to avoid unnecessary watcher trigger\n if (vm && vm.$options.propsData &&\n vm.$options.propsData[key] === undefined &&\n vm._props[key] !== undefined\n ) {\n return vm._props[key]\n }\n // call factory function for non-Function types\n // a value is Function if its prototype is function even across different execution context\n return typeof def === 'function' && getType(prop.type) !== 'Function'\n ? def.call(vm)\n : def\n}\n\n/**\n * Assert whether a prop is valid.\n */\nfunction assertProp (\n prop,\n name,\n value,\n vm,\n absent\n) {\n if (prop.required && absent) {\n warn(\n 'Missing required prop: \"' + name + '\"',\n vm\n );\n return\n }\n if (value == null && !prop.required) {\n return\n }\n var type = prop.type;\n var valid = !type || type === true;\n var expectedTypes = [];\n if (type) {\n if (!Array.isArray(type)) {\n type = [type];\n }\n for (var i = 0; i < type.length && !valid; i++) {\n var assertedType = assertType(value, type[i]);\n expectedTypes.push(assertedType.expectedType || '');\n valid = assertedType.valid;\n }\n }\n\n if (!valid) {\n warn(\n getInvalidTypeMessage(name, value, expectedTypes),\n vm\n );\n return\n }\n var validator = prop.validator;\n if (validator) {\n if (!validator(value)) {\n warn(\n 'Invalid prop: custom validator check failed for prop \"' + name + '\".',\n vm\n );\n }\n }\n}\n\nvar simpleCheckRE = /^(String|Number|Boolean|Function|Symbol)$/;\n\nfunction assertType (value, type) {\n var valid;\n var expectedType = getType(type);\n if (simpleCheckRE.test(expectedType)) {\n var t = typeof value;\n valid = t === expectedType.toLowerCase();\n // for primitive wrapper objects\n if (!valid && t === 'object') {\n valid = value instanceof type;\n }\n } else if (expectedType === 'Object') {\n valid = isPlainObject(value);\n } else if (expectedType === 'Array') {\n valid = Array.isArray(value);\n } else {\n valid = value instanceof type;\n }\n return {\n valid: valid,\n expectedType: expectedType\n }\n}\n\n/**\n * Use function string name to check built-in types,\n * because a simple equality check will fail when running\n * across different vms / iframes.\n */\nfunction getType (fn) {\n var match = fn && fn.toString().match(/^\\s*function (\\w+)/);\n return match ? match[1] : ''\n}\n\nfunction isSameType (a, b) {\n return getType(a) === getType(b)\n}\n\nfunction getTypeIndex (type, expectedTypes) {\n if (!Array.isArray(expectedTypes)) {\n return isSameType(expectedTypes, type) ? 0 : -1\n }\n for (var i = 0, len = expectedTypes.length; i < len; i++) {\n if (isSameType(expectedTypes[i], type)) {\n return i\n }\n }\n return -1\n}\n\nfunction getInvalidTypeMessage (name, value, expectedTypes) {\n var message = \"Invalid prop: type check failed for prop \\\"\" + name + \"\\\".\" +\n \" Expected \" + (expectedTypes.map(capitalize).join(', '));\n var expectedType = expectedTypes[0];\n var receivedType = toRawType(value);\n var expectedValue = styleValue(value, expectedType);\n var receivedValue = styleValue(value, receivedType);\n // check if we need to specify expected value\n if (expectedTypes.length === 1 &&\n isExplicable(expectedType) &&\n !isBoolean(expectedType, receivedType)) {\n message += \" with value \" + expectedValue;\n }\n message += \", got \" + receivedType + \" \";\n // check if we need to specify received value\n if (isExplicable(receivedType)) {\n message += \"with value \" + receivedValue + \".\";\n }\n return message\n}\n\nfunction styleValue (value, type) {\n if (type === 'String') {\n return (\"\\\"\" + value + \"\\\"\")\n } else if (type === 'Number') {\n return (\"\" + (Number(value)))\n } else {\n return (\"\" + value)\n }\n}\n\nfunction isExplicable (value) {\n var explicitTypes = ['string', 'number', 'boolean'];\n return explicitTypes.some(function (elem) { return value.toLowerCase() === elem; })\n}\n\nfunction isBoolean () {\n var args = [], len = arguments.length;\n while ( len-- ) args[ len ] = arguments[ len ];\n\n return args.some(function (elem) { return elem.toLowerCase() === 'boolean'; })\n}\n\n/* */\n\nfunction handleError (err, vm, info) {\n // Deactivate deps tracking while processing error handler to avoid possible infinite rendering.\n // See: https://github.com/vuejs/vuex/issues/1505\n pushTarget();\n try {\n if (vm) {\n var cur = vm;\n while ((cur = cur.$parent)) {\n var hooks = cur.$options.errorCaptured;\n if (hooks) {\n for (var i = 0; i < hooks.length; i++) {\n try {\n var capture = hooks[i].call(cur, err, vm, info) === false;\n if (capture) { return }\n } catch (e) {\n globalHandleError(e, cur, 'errorCaptured hook');\n }\n }\n }\n }\n }\n globalHandleError(err, vm, info);\n } finally {\n popTarget();\n }\n}\n\nfunction invokeWithErrorHandling (\n handler,\n context,\n args,\n vm,\n info\n) {\n var res;\n try {\n res = args ? handler.apply(context, args) : handler.call(context);\n if (res && !res._isVue && isPromise(res) && !res._handled) {\n res.catch(function (e) { return handleError(e, vm, info + \" (Promise/async)\"); });\n // issue #9511\n // avoid catch triggering multiple times when nested calls\n res._handled = true;\n }\n } catch (e) {\n handleError(e, vm, info);\n }\n return res\n}\n\nfunction globalHandleError (err, vm, info) {\n if (config.errorHandler) {\n try {\n return config.errorHandler.call(null, err, vm, info)\n } catch (e) {\n // if the user intentionally throws the original error in the handler,\n // do not log it twice\n if (e !== err) {\n logError(e, null, 'config.errorHandler');\n }\n }\n }\n logError(err, vm, info);\n}\n\nfunction logError (err, vm, info) {\n if (process.env.NODE_ENV !== 'production') {\n warn((\"Error in \" + info + \": \\\"\" + (err.toString()) + \"\\\"\"), vm);\n }\n /* istanbul ignore else */\n if ((inBrowser || inWeex) && typeof console !== 'undefined') {\n console.error(err);\n } else {\n throw err\n }\n}\n\n/* */\n\nvar isUsingMicroTask = false;\n\nvar callbacks = [];\nvar pending = false;\n\nfunction flushCallbacks () {\n pending = false;\n var copies = callbacks.slice(0);\n callbacks.length = 0;\n for (var i = 0; i < copies.length; i++) {\n copies[i]();\n }\n}\n\n// Here we have async deferring wrappers using microtasks.\n// In 2.5 we used (macro) tasks (in combination with microtasks).\n// However, it has subtle problems when state is changed right before repaint\n// (e.g. #6813, out-in transitions).\n// Also, using (macro) tasks in event handler would cause some weird behaviors\n// that cannot be circumvented (e.g. #7109, #7153, #7546, #7834, #8109).\n// So we now use microtasks everywhere, again.\n// A major drawback of this tradeoff is that there are some scenarios\n// where microtasks have too high a priority and fire in between supposedly\n// sequential events (e.g. #4521, #6690, which have workarounds)\n// or even between bubbling of the same event (#6566).\nvar timerFunc;\n\n// The nextTick behavior leverages the microtask queue, which can be accessed\n// via either native Promise.then or MutationObserver.\n// MutationObserver has wider support, however it is seriously bugged in\n// UIWebView in iOS >= 9.3.3 when triggered in touch event handlers. It\n// completely stops working after triggering a few times... so, if native\n// Promise is available, we will use it:\n/* istanbul ignore next, $flow-disable-line */\nif (typeof Promise !== 'undefined' && isNative(Promise)) {\n var p = Promise.resolve();\n timerFunc = function () {\n p.then(flushCallbacks);\n // In problematic UIWebViews, Promise.then doesn't completely break, but\n // it can get stuck in a weird state where callbacks are pushed into the\n // microtask queue but the queue isn't being flushed, until the browser\n // needs to do some other work, e.g. handle a timer. Therefore we can\n // \"force\" the microtask queue to be flushed by adding an empty timer.\n if (isIOS) { setTimeout(noop); }\n };\n isUsingMicroTask = true;\n} else if (!isIE && typeof MutationObserver !== 'undefined' && (\n isNative(MutationObserver) ||\n // PhantomJS and iOS 7.x\n MutationObserver.toString() === '[object MutationObserverConstructor]'\n)) {\n // Use MutationObserver where native Promise is not available,\n // e.g. PhantomJS, iOS7, Android 4.4\n // (#6466 MutationObserver is unreliable in IE11)\n var counter = 1;\n var observer = new MutationObserver(flushCallbacks);\n var textNode = document.createTextNode(String(counter));\n observer.observe(textNode, {\n characterData: true\n });\n timerFunc = function () {\n counter = (counter + 1) % 2;\n textNode.data = String(counter);\n };\n isUsingMicroTask = true;\n} else if (typeof setImmediate !== 'undefined' && isNative(setImmediate)) {\n // Fallback to setImmediate.\n // Technically it leverages the (macro) task queue,\n // but it is still a better choice than setTimeout.\n timerFunc = function () {\n setImmediate(flushCallbacks);\n };\n} else {\n // Fallback to setTimeout.\n timerFunc = function () {\n setTimeout(flushCallbacks, 0);\n };\n}\n\nfunction nextTick (cb, ctx) {\n var _resolve;\n callbacks.push(function () {\n if (cb) {\n try {\n cb.call(ctx);\n } catch (e) {\n handleError(e, ctx, 'nextTick');\n }\n } else if (_resolve) {\n _resolve(ctx);\n }\n });\n if (!pending) {\n pending = true;\n timerFunc();\n }\n // $flow-disable-line\n if (!cb && typeof Promise !== 'undefined') {\n return new Promise(function (resolve) {\n _resolve = resolve;\n })\n }\n}\n\n/* */\n\n/* not type checking this file because flow doesn't play well with Proxy */\n\nvar initProxy;\n\nif (process.env.NODE_ENV !== 'production') {\n var allowedGlobals = makeMap(\n 'Infinity,undefined,NaN,isFinite,isNaN,' +\n 'parseFloat,parseInt,decodeURI,decodeURIComponent,encodeURI,encodeURIComponent,' +\n 'Math,Number,Date,Array,Object,Boolean,String,RegExp,Map,Set,JSON,Intl,' +\n 'require' // for Webpack/Browserify\n );\n\n var warnNonPresent = function (target, key) {\n warn(\n \"Property or method \\\"\" + key + \"\\\" is not defined on the instance but \" +\n 'referenced during render. Make sure that this property is reactive, ' +\n 'either in the data option, or for class-based components, by ' +\n 'initializing the property. ' +\n 'See: https://vuejs.org/v2/guide/reactivity.html#Declaring-Reactive-Properties.',\n target\n );\n };\n\n var warnReservedPrefix = function (target, key) {\n warn(\n \"Property \\\"\" + key + \"\\\" must be accessed with \\\"$data.\" + key + \"\\\" because \" +\n 'properties starting with \"$\" or \"_\" are not proxied in the Vue instance to ' +\n 'prevent conflicts with Vue internals. ' +\n 'See: https://vuejs.org/v2/api/#data',\n target\n );\n };\n\n var hasProxy =\n typeof Proxy !== 'undefined' && isNative(Proxy);\n\n if (hasProxy) {\n var isBuiltInModifier = makeMap('stop,prevent,self,ctrl,shift,alt,meta,exact');\n config.keyCodes = new Proxy(config.keyCodes, {\n set: function set (target, key, value) {\n if (isBuiltInModifier(key)) {\n warn((\"Avoid overwriting built-in modifier in config.keyCodes: .\" + key));\n return false\n } else {\n target[key] = value;\n return true\n }\n }\n });\n }\n\n var hasHandler = {\n has: function has (target, key) {\n var has = key in target;\n var isAllowed = allowedGlobals(key) ||\n (typeof key === 'string' && key.charAt(0) === '_' && !(key in target.$data));\n if (!has && !isAllowed) {\n if (key in target.$data) { warnReservedPrefix(target, key); }\n else { warnNonPresent(target, key); }\n }\n return has || !isAllowed\n }\n };\n\n var getHandler = {\n get: function get (target, key) {\n if (typeof key === 'string' && !(key in target)) {\n if (key in target.$data) { warnReservedPrefix(target, key); }\n else { warnNonPresent(target, key); }\n }\n return target[key]\n }\n };\n\n initProxy = function initProxy (vm) {\n if (hasProxy) {\n // determine which proxy handler to use\n var options = vm.$options;\n var handlers = options.render && options.render._withStripped\n ? getHandler\n : hasHandler;\n vm._renderProxy = new Proxy(vm, handlers);\n } else {\n vm._renderProxy = vm;\n }\n };\n}\n\n/* */\n\nvar seenObjects = new _Set();\n\n/**\n * Recursively traverse an object to evoke all converted\n * getters, so that every nested property inside the object\n * is collected as a \"deep\" dependency.\n */\nfunction traverse (val) {\n _traverse(val, seenObjects);\n seenObjects.clear();\n}\n\nfunction _traverse (val, seen) {\n var i, keys;\n var isA = Array.isArray(val);\n if ((!isA && !isObject(val)) || Object.isFrozen(val) || val instanceof VNode) {\n return\n }\n if (val.__ob__) {\n var depId = val.__ob__.dep.id;\n if (seen.has(depId)) {\n return\n }\n seen.add(depId);\n }\n if (isA) {\n i = val.length;\n while (i--) { _traverse(val[i], seen); }\n } else {\n keys = Object.keys(val);\n i = keys.length;\n while (i--) { _traverse(val[keys[i]], seen); }\n }\n}\n\nvar mark;\nvar measure;\n\nif (process.env.NODE_ENV !== 'production') {\n var perf = inBrowser && window.performance;\n /* istanbul ignore if */\n if (\n perf &&\n perf.mark &&\n perf.measure &&\n perf.clearMarks &&\n perf.clearMeasures\n ) {\n mark = function (tag) { return perf.mark(tag); };\n measure = function (name, startTag, endTag) {\n perf.measure(name, startTag, endTag);\n perf.clearMarks(startTag);\n perf.clearMarks(endTag);\n // perf.clearMeasures(name)\n };\n }\n}\n\n/* */\n\nvar normalizeEvent = cached(function (name) {\n var passive = name.charAt(0) === '&';\n name = passive ? name.slice(1) : name;\n var once$$1 = name.charAt(0) === '~'; // Prefixed last, checked first\n name = once$$1 ? name.slice(1) : name;\n var capture = name.charAt(0) === '!';\n name = capture ? name.slice(1) : name;\n return {\n name: name,\n once: once$$1,\n capture: capture,\n passive: passive\n }\n});\n\nfunction createFnInvoker (fns, vm) {\n function invoker () {\n var arguments$1 = arguments;\n\n var fns = invoker.fns;\n if (Array.isArray(fns)) {\n var cloned = fns.slice();\n for (var i = 0; i < cloned.length; i++) {\n invokeWithErrorHandling(cloned[i], null, arguments$1, vm, \"v-on handler\");\n }\n } else {\n // return handler return value for single handlers\n return invokeWithErrorHandling(fns, null, arguments, vm, \"v-on handler\")\n }\n }\n invoker.fns = fns;\n return invoker\n}\n\nfunction updateListeners (\n on,\n oldOn,\n add,\n remove$$1,\n createOnceHandler,\n vm\n) {\n var name, def$$1, cur, old, event;\n for (name in on) {\n def$$1 = cur = on[name];\n old = oldOn[name];\n event = normalizeEvent(name);\n if (isUndef(cur)) {\n process.env.NODE_ENV !== 'production' && warn(\n \"Invalid handler for event \\\"\" + (event.name) + \"\\\": got \" + String(cur),\n vm\n );\n } else if (isUndef(old)) {\n if (isUndef(cur.fns)) {\n cur = on[name] = createFnInvoker(cur, vm);\n }\n if (isTrue(event.once)) {\n cur = on[name] = createOnceHandler(event.name, cur, event.capture);\n }\n add(event.name, cur, event.capture, event.passive, event.params);\n } else if (cur !== old) {\n old.fns = cur;\n on[name] = old;\n }\n }\n for (name in oldOn) {\n if (isUndef(on[name])) {\n event = normalizeEvent(name);\n remove$$1(event.name, oldOn[name], event.capture);\n }\n }\n}\n\n/* */\n\nfunction mergeVNodeHook (def, hookKey, hook) {\n if (def instanceof VNode) {\n def = def.data.hook || (def.data.hook = {});\n }\n var invoker;\n var oldHook = def[hookKey];\n\n function wrappedHook () {\n hook.apply(this, arguments);\n // important: remove merged hook to ensure it's called only once\n // and prevent memory leak\n remove(invoker.fns, wrappedHook);\n }\n\n if (isUndef(oldHook)) {\n // no existing hook\n invoker = createFnInvoker([wrappedHook]);\n } else {\n /* istanbul ignore if */\n if (isDef(oldHook.fns) && isTrue(oldHook.merged)) {\n // already a merged invoker\n invoker = oldHook;\n invoker.fns.push(wrappedHook);\n } else {\n // existing plain hook\n invoker = createFnInvoker([oldHook, wrappedHook]);\n }\n }\n\n invoker.merged = true;\n def[hookKey] = invoker;\n}\n\n/* */\n\nfunction extractPropsFromVNodeData (\n data,\n Ctor,\n tag\n) {\n // we are only extracting raw values here.\n // validation and default values are handled in the child\n // component itself.\n var propOptions = Ctor.options.props;\n if (isUndef(propOptions)) {\n return\n }\n var res = {};\n var attrs = data.attrs;\n var props = data.props;\n if (isDef(attrs) || isDef(props)) {\n for (var key in propOptions) {\n var altKey = hyphenate(key);\n if (process.env.NODE_ENV !== 'production') {\n var keyInLowerCase = key.toLowerCase();\n if (\n key !== keyInLowerCase &&\n attrs && hasOwn(attrs, keyInLowerCase)\n ) {\n tip(\n \"Prop \\\"\" + keyInLowerCase + \"\\\" is passed to component \" +\n (formatComponentName(tag || Ctor)) + \", but the declared prop name is\" +\n \" \\\"\" + key + \"\\\". \" +\n \"Note that HTML attributes are case-insensitive and camelCased \" +\n \"props need to use their kebab-case equivalents when using in-DOM \" +\n \"templates. You should probably use \\\"\" + altKey + \"\\\" instead of \\\"\" + key + \"\\\".\"\n );\n }\n }\n checkProp(res, props, key, altKey, true) ||\n checkProp(res, attrs, key, altKey, false);\n }\n }\n return res\n}\n\nfunction checkProp (\n res,\n hash,\n key,\n altKey,\n preserve\n) {\n if (isDef(hash)) {\n if (hasOwn(hash, key)) {\n res[key] = hash[key];\n if (!preserve) {\n delete hash[key];\n }\n return true\n } else if (hasOwn(hash, altKey)) {\n res[key] = hash[altKey];\n if (!preserve) {\n delete hash[altKey];\n }\n return true\n }\n }\n return false\n}\n\n/* */\n\n// The template compiler attempts to minimize the need for normalization by\n// statically analyzing the template at compile time.\n//\n// For plain HTML markup, normalization can be completely skipped because the\n// generated render function is guaranteed to return Array. There are\n// two cases where extra normalization is needed:\n\n// 1. When the children contains components - because a functional component\n// may return an Array instead of a single root. In this case, just a simple\n// normalization is needed - if any child is an Array, we flatten the whole\n// thing with Array.prototype.concat. It is guaranteed to be only 1-level deep\n// because functional components already normalize their own children.\nfunction simpleNormalizeChildren (children) {\n for (var i = 0; i < children.length; i++) {\n if (Array.isArray(children[i])) {\n return Array.prototype.concat.apply([], children)\n }\n }\n return children\n}\n\n// 2. When the children contains constructs that always generated nested Arrays,\n// e.g.