updated quiz links

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-water-0103e7a0f.azurestaticapps.net/quiz/0)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/1)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1)
 
 ## Assignments
 

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-water-0103e7a0f.azurestaticapps.net/quiz/0)
+## [Cuestionario antes de la lección](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/1)
+## [Cuestionario después de la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1)
 
 ## Tareas
 

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-water-0103e7a0f.azurestaticapps.net/quiz/0)
+## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/1)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1)
 
 ## कार्य (Assignments)
 

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-water-0103e7a0f.azurestaticapps.net/quiz/0)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/1)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1)
 
 ## 과제
 

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-water-0103e7a0f.azurestaticapps.net/quiz/0)
+## [Starttoets data science](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/1)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1)
 
 ## Opdrachten
 

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-water-0103e7a0f.azurestaticapps.net/quiz/0)
+## [Quiz pré-aula](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/1)
+## [Quiz pós-aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1)
 
 ## Tarefas
 

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-water-0103e7a0f.azurestaticapps.net/quiz/0)
+## [Вступительный тест](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/1)
+## [Проверочный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1)
 
 ## Домашнее задание
 

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-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/3) 🎯
+## [Post-lecture quiz](https://red-bay-0a991ec0f.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.

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-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯
+## [पाठ से पहले की प्रश्नोत्तरी](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/3) 🎯
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.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।

1-Introduction/02-ethics/translations/README.ko.md

@@ -21,7 +21,7 @@
 
 
 
-## [강의 전 퀴즈](https://red-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/3) 🎯
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.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.

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-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯
+## [Pre-college quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/3) 🎯
+## [Quiz voor na het college](https://red-bay-0a991ec0f.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.

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-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯
+## [Quiz pré aula](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/3) 🎯
+## [Quiz pós aula](https://red-bay-0a991ec0f.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.

1-Introduction/02-ethics/translations/README.ru.md

@@ -21,7 +21,7 @@
 
 
 
-## [Вступительный тест](https://red-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯
+## [Вступительный тест](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/3) 🎯
+## [Проверочный тест](https://red-bay-0a991ec0f.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:

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-water-0103e7a0f.azurestaticapps.net/quiz/4)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/5)
+## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/4)
+## [Examen previo a la lección](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/5)
+## [Examen posterior a la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/4)
+## [पाठ के पूर्व की परीक्षा](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/5)
+## [पाठ के पश्चात परीक्षा](https://red-bay-0a991ec0f.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। 

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-water-0103e7a0f.azurestaticapps.net/quiz/4)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/5)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/4)
+## [Quiz Pré Aula](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/5)
+## [Quiz Pós Aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/4)
+## [Вступительный тест](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/5)
+## [Проверочный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5)
 
 
 ## Материалы для самостоятельного изучения

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-water-0103e7a0f.azurestaticapps.net/quiz/4)
+## [Ders Öncesi Kısa Sınavı](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/5)
+## [Ders Sonu Kısa Sınavı](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/5)
 
 ## İnceleme & Öz Çalışma
 

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-water-0103e7a0f.azurestaticapps.net/quiz/6)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/7)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/6)
+## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/7)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7)
 
 ## समीक्षा और आत्म अध्ययन
 

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-water-0103e7a0f.azurestaticapps.net/quiz/6)
+## [강의전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/7)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7)
 
 ## 복습 및 독학
 

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-water-0103e7a0f.azurestaticapps.net/quiz/6)
+## [Quiz Pré Aula](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/7)
+## [Quis Pós Aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7)
 
 ## Revisão e Autoestudo
 

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-water-0103e7a0f.azurestaticapps.net/quiz/6)
+## [Вступительный тест](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/7)
+## [Проверочный тест](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/7)
 
 ## Материалы для самостоятельного изучения
 

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-water-0103e7a0f.azurestaticapps.net/quiz/8)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/9)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/9)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/8)
+## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/9)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/9)
 
 ## समीक्षा और आत्म अध्ययन
 

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-water-0103e7a0f.azurestaticapps.net/quiz/8)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/9)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/9)
 
 ## 리뷰 & 복습
 

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-water-0103e7a0f.azurestaticapps.net/quiz/10)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/11)
+## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/11)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/10)
+## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/11)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/11)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/10)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/11)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/11)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/12)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/13)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/13)
 
 ## Review & Self Study
 

2-Working-With-Data/07-python/translations/README.ko.md

@@ -26,7 +26,7 @@
 
 
 
-## [강의 전 퀴즈](https://red-water-0103e7a0f.azurestaticapps.net/quiz/12)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/13)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/13)
 
 ## 리뷰 & 복습
 

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-water-0103e7a0f.azurestaticapps.net/quiz/14)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/15)
+## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/15)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/14)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/15)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/15)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/14)
+## [Ders Öncesi Kısa Sınavı](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/15)
+## [Ders Sonu Kısa Sınavı](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/15)
 
 
 

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-water-0103e7a0f.azurestaticapps.net/quiz/16)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/17)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/16)
+## [Cuestionario previo](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/17)
+## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17)
 
 ## Repaso y Autoestudio
 

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-water-0103e7a0f.azurestaticapps.net/quiz/16)
+## [प्री-रीडिंग क्विज](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/17)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17)
 
 ## समीक्षा और स्व अध्ययन
 

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-water-0103e7a0f.azurestaticapps.net/quiz/16)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/17)
+## [이전 강의 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17)
 
 ## 복습 & 자기주도학습
 

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-water-0103e7a0f.azurestaticapps.net/quiz/16)
+## [Quiz pré-aula](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/17)
+## [Quiz pós-aula](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17)
 
 ## Revisão e autoestudo
 

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-water-0103e7a0f.azurestaticapps.net/quiz/18)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/19)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/18)
+## [Cuestionario previo](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/19)
+## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19)
 
 ## Repaso y Autoestudio
 

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-water-0103e7a0f.azurestaticapps.net/quiz/18)
+## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/19)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19)
 
 ## समीक्षा और स्व अध्ययन
 

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-water-0103e7a0f.azurestaticapps.net/quiz/18)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/19)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19)
 
 ## 복습 & 자기주도학습
 

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-water-0103e7a0f.azurestaticapps.net/quiz/18)
+## [Quiz pré-aula](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/19)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19)
 
 ## Revisão e autoestudo
 

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-water-0103e7a0f.azurestaticapps.net/quiz/20)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/21)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/20)
+## [Cuestionario previo](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/21)
+## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21)
 
 ## Repaso y autoestudio
 

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-water-0103e7a0f.azurestaticapps.net/quiz/20)
+## [प्री-लेक्चर क्विज](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/21)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21)
 
 ## समीक्षा और आत्म अध्ययन
 

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-water-0103e7a0f.azurestaticapps.net/quiz/22)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/23)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/22)
+## [Cuestionario previo](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/23)
+## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23)
 
 ## Repaso y autoestudio
 

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-water-0103e7a0f.azurestaticapps.net/quiz/22)
+## [व्याख्यान पूर्व प्रश्नोत्तरी](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/23)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23)
 
 ## समीक्षा और आत्म अध्ययन
 

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-water-0103e7a0f.azurestaticapps.net/quiz/22)
+## [이전 강의 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/23)
+## [이전 강의 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23)
 
 ## 복습 & 자기 주도 학습
 

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-water-0103e7a0f.azurestaticapps.net/quiz/24)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/25)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/24)
+## [Cuestionario previo](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/25)
+## [Cuestionario posterior a la clase](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25)
 
 ## Revisión y Autoestudio
 

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-water-0103e7a0f.azurestaticapps.net/quiz/24)
+## [व्याख्यान पूर्व प्रश्नोत्तरी](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/25)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25)
 
 ## समीक्षा और आत्म अध्ययन
 

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-water-0103e7a0f.azurestaticapps.net/quiz/16)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/17)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/17)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/18)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/19)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/19)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/20)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/21)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/21)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/22)
+## [Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/23)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/23)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/24)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/25)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/25)
 
 ## Review & Self Study
 

4-Data-Science-Lifecycle/15-analyzing/README.md

@@ -6,7 +6,7 @@
 
 ## Pre-Lecture Quiz
 
-## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/28)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/27)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/27)
 
 ## Assignment
 

4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md

@@ -6,7 +6,7 @@
 
 ## 강의 전 퀴즈
 
-## [강의 전 퀴즈](https://red-water-0103e7a0f.azurestaticapps.net/quiz/28)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/27)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/27)
 
 ## 과제
 

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-water-0103e7a0f.azurestaticapps.net/quiz/30)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/31)
+## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/31)
 
 Review what you've just learned with the Post-Lecture Quiz above!
 

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-water-0103e7a0f.azurestaticapps.net/quiz/30)
+## [प्री-लेक्चर क्विज ](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/31)
+## [व्याख्यान के बाद प्रश्नोत्तरी](https://red-bay-0a991ec0f.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/)

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-water-0103e7a0f.azurestaticapps.net/quiz/30)
+## [강의 전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/31)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/31)
 
 ### 자습을 위한 추천 자료
 [스토리텔링의 5대 C - 분명한 설득](http://articulatepersuasion.com/the-five-cs-of-storytelling/)

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-water-0103e7a0f.azurestaticapps.net/quiz/32)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/32)
 
 
 ## What is the Cloud?
@@ -92,7 +92,7 @@ Sources:
 
 ## Post-Lecture Quiz
 
-[Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/33)
+[Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33)
 
 ## Assignment
 

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-water-0103e7a0f.azurestaticapps.net/quiz/32)
+## [प्री-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/32)
 
 
 ## क्लाउड क्या है?
@@ -93,7 +93,7 @@
 
 ## पोस्ट-लेक्चर क्विज़
 
-[पोस्ट-लेक्चर क्विज़](https://red-water-0103e7a0f.azurestaticapps.net/quiz/33)
+[पोस्ट-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33)
 
 ## असाइनमेंट
 

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-water-0103e7a0f.azurestaticapps.net/quiz/32)
+## [강의전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/33)
+[강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33)
 
 ## 과제
 

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-water-0103e7a0f.azurestaticapps.net/quiz/32)
+## [प्री-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/32)
 
 
 ## क्लाउड के हो?
@@ -94,7 +94,7 @@
 
 ## पोस्ट-लेक्चर क्विज़
 
-[पोस्ट-लेक्चर क्विज़](https://red-water-0103e7a0f.azurestaticapps.net/quiz/33)
+[पोस्ट-लेक्चर क्विज़](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/33)
 
 ## असाइनमेंट
 

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-water-0103e7a0f.azurestaticapps.net/quiz/34)
+## [Pre-Lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/35)
+## [Post-Lecture Quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/35)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/34)
+## [강의전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/35)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/35)
 
 ## 복습 및 독학
 

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-water-0103e7a0f.azurestaticapps.net/quiz/36)
+## [Pre-Lecture Quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/37)
+## [Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/37)
 
 ## Review & Self Study
 

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-water-0103e7a0f.azurestaticapps.net/quiz/36)
+## [강의전 퀴즈](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/37)
+## [강의 후 퀴즈](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/37)
 
 ## 복습 및 독학
 

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-water-0103e7a0f.azurestaticapps.net/quiz/38)
+[Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/39)
+[Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/39)
 ## Review & Self Study
 
 Want to explore more use cases? Here are a few relevant articles:

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-water-0103e7a0f.azurestaticapps.net/quiz/38)
+[Examen previo a la lección](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/39)
+[Examen posterior a la lección](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/39)
 ## Revisión y auto-estudio
 
 ¿Quieres explorar más casos de uso? Aquí hay algunos artículos relevantes:

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-water-0103e7a0f.azurestaticapps.net/quiz/38)
+[Pre-lecture quiz](https://red-bay-0a991ec0f.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-water-0103e7a0f.azurestaticapps.net/quiz/39)
+[Post-lecture quiz](https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/39)
 
 ## 리뷰 & 혼자 공부해보기
 

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-water-0103e7a0f.azurestaticapps.net/quiz/1 becomes https://red-water-0103e7a0f.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://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1 becomes https://red-bay-0a991ec0f.1.azurestaticapps.net/quiz/1?loc=id
 
 **THANK YOU**
 

quiz-app/dist/css/app.67375a05.css

@@ -0,0 +1 @@
+html{font-family:Avenir,Helvetica,Arial,sans-serif;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;color:#252d4a}nav{background-color:#252d4a;padding:1em;margin-bottom:20px}nav a{color:#fff;text-align:right}ul{list-style-type:none;margin:0;padding:0;overflow:hidden}li{float:left}.title{color:#fff;font-weight:700;font-size:x-large;float:right}.link{display:list-item}.message,h1,h2,h3{text-align:center}.error{color:red}.complete{color:green}.card{width:60%;border:solid #252d4a;border-radius:5px;margin:auto;padding:1em}.btn{min-width:50%;text-align:center;cursor:pointer;margin-bottom:5px;width:50%;font-size:16px;color:#fff;background-color:#252d4a;border-radius:5px;padding:5px;justify-content:flex-start;align-items:center}.ans-btn{justify-content:center;display:flex;margin:4px auto}

quiz-app/dist/index.html

@@ -0,0 +1 @@
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width,initial-scale=1"><link rel="icon" href="/favicon.ico"><title>quizzes</title><link href="/css/app.67375a05.css" rel="preload" as="style"><link href="/js/app.8ead00f7.js" rel="preload" as="script"><link href="/js/chunk-vendors.b492a747.js" rel="preload" as="script"><link href="/css/app.67375a05.css" rel="stylesheet"></head><body><noscript><strong>We're sorry but quizzes doesn't work properly without JavaScript enabled. Please enable it to continue.</strong></noscript><div id="app"></div><script src="/js/chunk-vendors.b492a747.js"></script><script src="/js/app.8ead00f7.js"></script></body></html>

quiz-app/dist/routes.json

@@ -0,0 +1,8 @@
+{
+	"routes": [
+		{
+			"route": "/*",
+			"serve": "/index.html"
+		}
+	]
+}