chore(i18n,learn): processed translations (#45432)

curriculum/challenges/chinese-traditional/02-javascript-algorithms-and-data-structures/debugging/use-typeof-to-check-the-type-of-a-variable.md

@@ -21,7 +21,7 @@ console.log(typeof {});
 
 控制檯將按順序顯示字符串 `string`、`number`、`object` 和 `object`。
 
-JavaScript 有六種原始（不可變）數據類型：`Boolean`、`Null`、`Undefined`、`Number`、`String` 和 `Symbol`（ES6 中新增的），和一種可變的數據類型：`Object`。 注意：在 JavaScript 中，數組在本質上是一種對象。
+JavaScript 有七種原始（不可變）數據類型： `Boolean`，`Null`，`Undefined`，`Number`，`String`，`Symbol` （new with ES6），`BigInt` （new with ES2020）和一種可變數據類型：`Object`。 注意：在 JavaScript 中，數組在本質上是一種對象。
 
 # --instructions--
 

curriculum/challenges/chinese-traditional/02-javascript-algorithms-and-data-structures/object-oriented-programming/verify-an-objects-constructor-with-instanceof.md

@@ -8,7 +8,7 @@ dashedName: verify-an-objects-constructor-with-instanceof
 
 # --description--
 
-凡是通過構造函數創建出的新對象，這個對象都叫做這個構造函數的 <dfn>instance</dfn>。 JavaScript 提供了一種很簡便的方法來驗證這個事實，那就是通過 `instanceof` 操作符。 `instanceof` 允許你將對象與構造函數之間進行比較，根據對象是否由這個構造函數創建的返回 `true` 或者 `false`。 以下是一個示例：
+凡是通過構造函數創建出的新對象，這個對象都叫做這個構造函數的 <dfn>實例</dfn>。 JavaScript 提供了一種很簡便的方法來驗證這個事實，那就是通過 `instanceof` 操作符。 `instanceof` 允許你將對象與構造函數之間進行比較，根據對象是否由這個構造函數創建的返回 `true` 或者 `false`。 以下是一個示例：
 
 ```js
 let Bird = function(name, color) {

curriculum/challenges/chinese-traditional/02-javascript-algorithms-and-data-structures/regular-expressions/match-beginning-string-patterns.md

@@ -10,7 +10,7 @@ dashedName: match-beginning-string-patterns
 
 回顧一下之前的挑戰，正則表達式可以用於查找多項匹配。 還可以查詢字符串中符合指定匹配模式的字符。
 
-在之前的挑戰中，使用字符集中前插入符號（`^`）來創建一個否定字符集，形如 `[^thingsThatWillNotBeMatched]`。 除了在字符集中使用之外，脫字符還用於匹配字符串的開始位置。
+在之前的挑戰中，使用字符集中前插入符號（`^`）來創建一個否定字符集，形如 `[^thingsThatWillNotBeMatched]`。 除了在字符集中使用之外，插入符號（^）用於匹配文本是否在字符串的開始位置
 
 ```js
 let firstString = "Ricky is first and can be found.";

curriculum/challenges/chinese/02-javascript-algorithms-and-data-structures/debugging/use-typeof-to-check-the-type-of-a-variable.md

@@ -21,7 +21,7 @@ console.log(typeof {});
 
 控制台将按顺序显示字符串 `string`、`number`、`object` 和 `object`。
 
-JavaScript 有六种原始（不可变）数据类型：`Boolean`、`Null`、`Undefined`、`Number`、`String` 和 `Symbol`（ES6 中新增的），和一种可变的数据类型：`Object`。 注意：在 JavaScript 中，数组在本质上是一种对象。
+JavaScript 有七种原始（不可变）数据类型： `Boolean`，`Null`，`Undefined`，`Number`，`String`，`Symbol` （new with ES6），`BigInt` （new with ES2020）和一种可变数据类型：`Object`。 注意：在 JavaScript 中，数组在本质上是一种对象。
 
 # --instructions--
 

curriculum/challenges/chinese/02-javascript-algorithms-and-data-structures/object-oriented-programming/verify-an-objects-constructor-with-instanceof.md

@@ -8,7 +8,7 @@ dashedName: verify-an-objects-constructor-with-instanceof
 
 # --description--
 
-凡是通过构造函数创建出的新对象，这个对象都叫做这个构造函数的 <dfn>instance</dfn>。 JavaScript 提供了一种很简便的方法来验证这个事实，那就是通过 `instanceof` 操作符。 `instanceof` 允许你将对象与构造函数之间进行比较，根据对象是否由这个构造函数创建的返回 `true` 或者 `false`。 以下是一个示例：
+凡是通过构造函数创建出的新对象，这个对象都叫做这个构造函数的 <dfn>实例</dfn>。 JavaScript 提供了一种很简便的方法来验证这个事实，那就是通过 `instanceof` 操作符。 `instanceof` 允许你将对象与构造函数之间进行比较，根据对象是否由这个构造函数创建的返回 `true` 或者 `false`。 以下是一个示例：
 
 ```js
 let Bird = function(name, color) {

curriculum/challenges/chinese/02-javascript-algorithms-and-data-structures/regular-expressions/match-beginning-string-patterns.md

@@ -10,7 +10,7 @@ dashedName: match-beginning-string-patterns
 
 回顾一下之前的挑战，正则表达式可以用于查找多项匹配。 还可以查询字符串中符合指定匹配模式的字符。
 
-在之前的挑战中，使用字符集中前插入符号（`^`）来创建一个否定字符集，形如 `[^thingsThatWillNotBeMatched]`。 除了在字符集中使用之外，脱字符还用于匹配字符串的开始位置。
+在之前的挑战中，使用字符集中前插入符号（`^`）来创建一个否定字符集，形如 `[^thingsThatWillNotBeMatched]`。 除了在字符集中使用之外，插入符号（^）用于匹配文本是否在字符串的开始位置
 
 ```js
 let firstString = "Ricky is first and can be found.";

curriculum/challenges/espanol/02-javascript-algorithms-and-data-structures/basic-javascript/decrement-a-number-with-javascript.md

@@ -38,9 +38,13 @@ assert(myVar === 10);
 `myVar = myVar - 1;` debe cambiarse.
 
 ```js
-assert(
-  /let\s*myVar\s*=\s*11;\s*\/*.*\s*([-]{2}\s*myVar|myVar\s*[-]{2});/.test(code)
-);
+assert(!code.match(/myVar\s*=\s*myVar\s*[-]\s*1.*?;?/));
+```
+
+No debes asignar `myVar` con `10`.
+
+```js
+assert(!code.match(/myVar\s*=\s*10.*?;?/));
 ```
 
 Debes usar el operador `--` en `myVar`.

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-duplicates.md

@@ -1,37 +1,41 @@
 ---
 id: 5e9a093a74c4063ca6f7c15f
-title: Data Cleaning Duplicates
+title: Limpieza de datos duplicados
 challengeType: 11
 videoId: kj7QqjXhH6A
+bilibiliIds:
+  aid: 675611672
+  bvid: BV1VU4y1A7tu
+  cid: 409019368
 dashedName: data-cleaning-duplicates
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-The Python method `.duplicated()` returns a boolean Series for your DataFrame. `True` is the return value for rows that:
+El método Python `.duplicated()` devuelve una serie booleana para su DataFrame. `True` es el valor de retorno de las filas que:
 
 ## --answers--
 
-contain a duplicate, where the value for the row contains the first occurrence of that value.
+contiene un duplicado, donde el valor de la fila contiene la primera coincidencia de ese valor.
 
 ---
 
-contain a duplicate, where the value for the row is at least the second occurrence of that value.
+contiene un duplicado, donde el valor de la fila es al menos la segunda coincidencia de ese valor.
 
 ---
 
-contain a duplicate, where the value for the row contains either the first or second occurrence.
+contiene un duplicado, donde el valor de la fila es ya sea la primera o segunda coincidencia.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-introduction.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c15d
-title: Data Cleaning Introduction
+title: Introducción a la limpieza de datos
 challengeType: 11
 videoId: ovYNhnltVxY
+bilibiliIds:
+  aid: 250574398
+  bvid: BV1Pv411A7GN
+  cid: 409018611
 dashedName: data-cleaning-introduction
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 import pandas as pd

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-with-dataframes.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c15e
-title: Data Cleaning with DataFrames
+title: Limpieza de datos con DataFrames
 challengeType: 11
 videoId: sTMN_pdI6S0
+bilibiliIds:
+  aid: 505597026
+  bvid: BV1Yg411c7bx
+  cid: 409018948
 dashedName: data-cleaning-with-dataframes
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 import pandas as pd

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/how-to-use-jupyter-notebooks-intro.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c14f
-title: How to use Jupyter Notebooks Intro
+title: Cómo usar la introducción de Notebooks de Jupyter
 challengeType: 11
 videoId: h8caJq2Bb9w
+bilibiliIds:
+  aid: 293035919
+  bvid: BV1Hf4y1n7qr
+  cid: 409002965
 dashedName: how-to-use-jupyter-notebooks-intro
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What is **not** allowed in a Jupyter Notebook's cell?
+¿Qué **no está** permitido en la celda de un Jupyter Notebook?
 
 ## --answers--
 
@@ -27,11 +31,11 @@ Markdown
 
 ---
 
-Python code
+Código Python
 
 ---
 
-An Excel sheet
+Una hoja de Excel
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/introduction-to-data-analysis.md

@@ -1,38 +1,42 @@
 ---
 id: 5e9a093a74c4063ca6f7c14c
-title: Introduction to Data Analysis
+title: Introducción al Análisis de Datos
 challengeType: 11
 videoId: VJrP2FUzKP0
+bilibiliIds:
+  aid: 378034466
+  bvid: BV19f4y1c7nu
+  cid: 409001487
 dashedName: introduction-to-data-analysis
 ---
 
 # --description--
 
-More resources:
+Más recursos:
 
-\- [Slides](https://docs.google.com/presentation/d/1cUIt8b2ySz-85_ykfeuuWsurccwTAuFPn782pZBzFsU/edit?usp=sharing)
+\- [Diapositivas](https://docs.google.com/presentation/d/1cUIt8b2ySz-85_ykfeuuWsurccwTAuFPn782pZBzFsU/edit?usp=sharing)
 
 # --question--
 
 ## --text--
 
-Which of the following is **not** part of Data Analysis?
+¿Cuál de las siguientes opciones no **es** parte del Análisis de Datos?
 
 ## --answers--
 
-Building statistical models and data visualizations.
+Construcción de modelos estadísticos y visualización de datos.
 
 ---
 
-Picking a desired conclusion for the analysis.
+Elegir una conclusión deseada para el análisis.
 
 ---
 
-Fixing incorrect values and removing invalid data.
+Corregir valores incorrectos y eliminar datos no válidos.
 
 ---
 
-Transforming data into an appropriate data structure.
+Transformación de datos en una estructura de datos apropiada.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/jupyter-notebooks-cells.md

@@ -1,37 +1,41 @@
 ---
 id: 5e9a093a74c4063ca6f7c150
-title: Jupyter Notebooks Cells
+title: Celdas de Notebooks de Jupyter
 challengeType: 11
 videoId: 5PPegAs9aLA
+bilibiliIds:
+  aid: 420510493
+  bvid: BV19341117Hq
+  cid: 409003280
 dashedName: jupyter-notebooks-cells
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What are the three main types of Jupyter Notebook Cell?
+¿Cuáles son los tres tipos principales de celdas de Jupyter Notebook?
 
 ## --answers--
 
-Code, Markdown, and Python
+Código, Markdown y Python
 
 ---
 
-Code, Markdown, and Raw
+Código, Markdown y Raw
 
 ---
 
-Markdown, Python, and Raw
+Markdown, Python y Raw
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/jupyter-notebooks-importing-and-exporting-data.md

@@ -1,45 +1,49 @@
 ---
 id: 5e9a093a74c4063ca6f7c151
-title: Jupyter Notebooks Importing and Exporting Data
+title: Importación y exportación de datos en Jupyter Notebooks
 challengeType: 11
 videoId: k1msxD3JIxE
+bilibiliIds:
+  aid: 975540688
+  bvid: BV1n44y1b7Gi
+  cid: 409006337
 dashedName: jupyter-notebooks-importing-and-exporting-data
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What kind of data can you import and work with in a Jupyter Notebook?
+¿Qué tipo de datos puede importar y trabajar en un Jupyter Notebook?
 
 ## --answers--
 
-Excel files.
+Archivos de Excel.
 
 ---
 
-CSV files.
+Archivos CSV.
 
 ---
 
-XML files.
+Archivos XML.
 
 ---
 
-Data from an API.
+Datos de una API.
 
 ---
 
-All of the above.
+Todo lo anterior.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-algebra-and-size.md

@@ -1,41 +1,45 @@
 ---
 id: 5e9a093a74c4063ca6f7c157
-title: Numpy Algebra and Size
+title: NumPy Álgebra y tamaño
 challengeType: 11
 videoId: XAT97YLOKD8
+bilibiliIds:
+  aid: 250621433
+  bvid: BV1hv41137uM
+  cid: 409013128
 dashedName: numpy-algebra-and-size
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What is the relationship between size of objects (such as lists and datatypes) in memory in Python's standard library and the NumPy library? Knowing this, what are the implications for performance?
+¿Cuál es la relación entre el tamaño de los objetos (como listas y tipos de datos) en la memoria de la biblioteca estándar de Python y la biblioteca NumPy? Sabiendo esto, ¿cuáles son las implicaciones para el rendimiento?
 
 ## --answers--
 
-Standard Python objects take up much more memory to store than NumPy objects; operations on comparable standard Python and NumPy objects complete in roughly the same time.
+Objetos estándar de Python ocupan mucha más memoria que los objetos de NumPy; operaciones comprables en Python estándar y objectos de NumPy se completan en casi el mismo tiempo.
 
 ---
 
-NumPy objects take up much more memory than standard Python objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.
+Los objectos de NumPy toman mucha más memoria que los de Python estándar; las operaciones con objectos de NumPy se terminan muy rápido comparadas con las de los objetos de Python estándar.
 
 ---
 
-NumPy objects take up much less memory than Standard Python objects; operations on Standard Python objects complete very quickly compared to comparable objects on NumPy Object.
+Los objetos de NumPy tomas mucha menos memoria que los de Python Estándar; las operaciones en Python Estándar se completan muy rápido comparando con objetos similares en NumPy.
 
 ---
 
-Standard Python objects take up more memory than NumPy objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.
+Los objetos de Python Estándar toman más memoria que los de NumPy; operaciones con objetos de Numpy se terminan rápidamente comparando con objetos de Python Estándar.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-arrays.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c154
-title: Numpy Arrays
+title: Arreglos de NumPy
 challengeType: 11
 videoId: VDYVFHBL1AM
+bilibiliIds:
+  aid: 890607366
+  bvid: BV1zP4y1h7FR
+  cid: 409011400
 dashedName: numpy-arrays
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 A = np.array([

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-boolean-arrays.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c156
-title: Numpy Boolean Arrays
+title: Arreglos Booleanos en Numpy
 challengeType: 11
 videoId: N1ttsMmcVMM
+bilibiliIds:
+  aid: 208091324
+  bvid: BV1Qh411p7V8
+  cid: 409012711
 dashedName: numpy-boolean-arrays
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 a = np.arange(5)

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-introduction-a.md

@@ -1,37 +1,41 @@
 ---
 id: 5e9a093a74c4063ca6f7c152
-title: Numpy Introduction A
+title: Numpy Introducción A
 challengeType: 11
 videoId: P-JjV6GBCmk
+bilibiliIds:
+  aid: 718079611
+  bvid: BV18Q4y1k7om
+  cid: 409007080
 dashedName: numpy-introduction-a
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-Why is Numpy an important, but unpopular Python library?
+¿Por qué es Numpy una biblioteca importante, pero poco popular en Python?
 
 ## --answers--
 
-Often you won't work directly with Numpy.
+A menudo no trabajarás directamente con Numpy.
 
 ---
 
-It is extremely slow.
+Es muy lento.
 
 ---
 
-Working with Numpy is difficult.
+Trabajar con Numpy es complicado.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-introduction-b.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c153
-title: Numpy Introduction B
+title: Introducción a Numpy B
 challengeType: 11
 videoId: YIqgrNLAZkA
+bilibiliIds:
+  aid: 250503382
+  bvid: BV1kv411w7vB
+  cid: 409010193
 dashedName: numpy-introduction-b
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-About how much memory does the integer `5` consume in plain Python?
+¿cuanta memoria consume el entero `5` en python plano?
 
 ## --answers--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-operations.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c155
-title: Numpy Operations
+title: Operaciones en NumPy
 challengeType: 11
 videoId: eqSVcJbaPdk
+bilibiliIds:
+  aid: 378057123
+  bvid: BV13f4y1w7od
+  cid: 409012507
 dashedName: numpy-operations
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What is the value of `a` after you run the following code?
+¿Cuál es el valor de `a` después de ejecutar el siguiente código?
 
 ```py
 a = np.arange(5)

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-conditional-selection-and-modifying-dataframes.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c15b
-title: Pandas Conditional Selection and Modifying DataFrames
+title: Selección condicional de Pandas y modificación de DataFrames
 challengeType: 11
 videoId: BFlH0fN5xRQ
+bilibiliIds:
+  aid: 505598518
+  bvid: BV1vg411c72y
+  cid: 409113534
 dashedName: pandas-conditional-selection-and-modifying-dataframes
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 import pandas as pd
@@ -40,17 +44,17 @@ print(certificates_earned)
 ## --answers--
 
 <pre>
-Tom      13
-Kris     11
-Ahmad     9
-Beau      7
-Name: Longest streak, dtype: int64
+Tom 13
+Kris 11
+Ahmad 9
+Beau 7
+Nombre: Racha más larga, tipo: int64
 </pre>
 
 ---
 
 <pre>
-      Certificates  Time (in months)  Longest streak
+      Certificados  Tiempo (en meses)  Racha más larga
 Tom               8                16              13
 Kris              2                 5              11
 Ahmad             5                 9               9
@@ -60,7 +64,7 @@ Beau              6                12               7
 ---
 
 <pre>
-      Certificates   Longest streak
+      Certificados   Racha más larga
 Tom               8               13
 Kris              2               11
 Ahmad             5                9

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-creating-columns.md

@@ -1,27 +1,31 @@
 ---
 id: 5e9a093a74c4063ca6f7c15c
-title: Pandas Creating Columns
+title: Creando columnas en Pandas
 challengeType: 11
 videoId: _sSo2XZoB3E
+bilibiliIds:
+  aid: 975568901
+  bvid: BV1b44y1b7Cg
+  cid: 409018052
 dashedName: pandas-creating-columns
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What code would add a "Certificates per month" column to the `certificates_earned` DataFrame like the one below?
+¿Qué código añadiría una columna "Certificados por mes" a la `certificates_earned` DataFrame como la de abajo?
 
-<pre>      Certificates  Time (in months)  Certificates per month
+<pre>      Certificados  Tiempo (en meses)    Certificados por mes
 Tom               8                16                    0.50
 Kris              2                 5                    0.40
 Ahmad             5                 9                    0.56

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-dataframes.md

@@ -3,23 +3,27 @@ id: 5e9a093a74c4063ca6f7c15a
 title: Pandas DataFrames
 challengeType: 11
 videoId: 7SgFBYXaiH0
+bilibiliIds:
+  aid: 890503235
+  bvid: BV1TP4y1h7qq
+  cid: 409014039
 dashedName: pandas-dataframes
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 import pandas as pd
@@ -41,23 +45,23 @@ Tom      16
 Kris      5
 Ahmad     9
 Beau     12
-Name: Time (in months), dtype: int64
+Nombre: Tiempo (en meses), dtype: int64
 </pre>
 
 ---
 
 <pre>
-Certificates         6
-Time (in months)    12
-Name: Beau, dtype: int64
+Certificados 6
+Tiempo (en meses) 12
+Nombre: Beau, dtype: int64
 </pre>
 
 ---
 
 <pre>
-Certificates        5
-Time (in months)    9
-Name: Ahmad, dtype: int64
+Certificados 5
+Tiempo (en meses) 9
+Nombre: Ahmad, dtype: int64
 </pre>
 
 ## --video-solution--

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-indexing-and-conditional-selection.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c159
-title: Pandas Indexing and Conditional Selection
+title: Indexación y Selección Condicional en Pandas
 challengeType: 11
 videoId: '-ZOrgV_aA9A'
+bilibiliIds:
+  aid: 720604139
+  bvid: BV1FQ4y1k7tC
+  cid: 409013650
 dashedName: pandas-indexing-and-conditional-selection
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 import pandas as pd
@@ -45,18 +49,18 @@ dtype: int64
 ---
 
 <pre>
-Tom      8
-Ahmad    5
-Beau     6
-dtype: int64
+Tom 8
+Ahmad 5
+Beau 6
+tipo: int64
 </pre>
 
 ---
 
 <pre>
-Tom      8
-Beau     6
-dtype: int64
+Tom 8
+Beau 6
+tipo: int64
 </pre>
 
 ## --video-solution--

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-introduction.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c158
-title: Pandas Introduction
+title: Introducción a Pandas
 challengeType: 11
 videoId: 0xACW-8cZU0
+bilibiliIds:
+  aid: 975510116
+  bvid: BV1u44y1b7fD
+  cid: 409013433
 dashedName: pandas-introduction
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What will the following code print out?
+¿Qué imprimirá el siguiente código?
 
 ```py
 import pandas as pd

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/parsing-html-and-saving-data.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c164
-title: Parsing HTML and Saving Data
+title: Analizando HTML y guardando datos
 challengeType: 11
 videoId: bJaqnTWQmb0
+bilibiliIds:
+  aid: 335522976
+  bvid: BV1RA411F7vi
+  cid: 409023170
 dashedName: parsing-html-and-saving-data
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-17-reading-html-tables/files)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What Python library has the `.read_html()` method we can we use for parsing HTML documents and extracting tables?
+¿Qué biblioteca de Python tiene el método `.read_html()` que podemos usar para analizar documentos HTML y extraer tablas?
 
 ## --answers--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/python-functions-and-collections.md

@@ -1,37 +1,41 @@
 ---
 id: 5e9a093a74c4063ca6f7c166
-title: Python Functions and Collections
+title: Funciones y colecciones de Python
 challengeType: 11
 videoId: NzpU17ZVlUw
+bilibiliIds:
+  aid: 675544435
+  bvid: BV1pU4y1N7JC
+  cid: 409023833
 dashedName: python-functions-and-collections
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What is the main difference between lists and tuples in Python?
+¿Cuál es la principal diferencia entre las listas y las tuplas en Python?
 
 ## --answers--
 
-Tuples are immutable.
+Los tuplas son inmutables.
 
 ---
 
-Lists are ordered.
+Las listas están ordenadas.
 
 ---
 
-Tuples are unordered.
+Los tuplas no están ordenadas.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/python-introduction.md

@@ -1,41 +1,45 @@
 ---
 id: 5e9a093a74c4063ca6f7c165
-title: Python Introduction
+title: Introducción a Python
 challengeType: 11
 videoId: PrQV9JkLhb4
+bilibiliIds:
+  aid: 805597530
+  bvid: BV1634y1S7gD
+  cid: 409023550
 dashedName: python-introduction
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-How do we define blocks of code in the body of functions in Python?
+¿Cómo definimos bloques de código en el cuerpo de funciones de Python?
 
 ## --answers--
 
-We use a set of curly braces, one on either side of each new block of our code.
+Utilizamos un conjunto de llaves, uno a cada lado de cada nuevo bloque de nuestro código.
 
 ---
 
-We use indentation, usually right-aligned 4 spaces.
+Utilizamos indentación, habitualmente 4 espacios alineados.
 
 ---
 
-We do not denote blocks of code.
+No denotamos bloques de código.
 
 ---
 
-We could use curly braces or indentation to denote blocks of code.
+Podríamos usar llaves o indentación para indicar bloques de código.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/python-iteration-and-modules.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c167
-title: Python Iteration and Modules
+title: Iteración y módulos en Python
 challengeType: 11
 videoId: XzosGWLafrY
+bilibiliIds:
+  aid: 633068913
+  bvid: BV1db4y127M4
+  cid: 409024056
 dashedName: python-iteration-and-modules
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-How would you iterate over and print the keys and values of a dictionary named `user`?
+¿Cómo iterarías e imprimirías las claves y valores de un diccionario llamado`user`?
 
 ## --answers--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/reading-data-csv-and-txt.md

@@ -1,25 +1,29 @@
 ---
 id: 5e9a093a74c4063ca6f7c162
-title: Reading Data CSV and TXT
+title: Leyendo datos CSV y TXT
 challengeType: 11
 videoId: ViGEv0zOzUk
+bilibiliIds:
+  aid: 505575354
+  bvid: BV1tg411c7GH
+  cid: 409020451
 dashedName: reading-data-csv-and-txt
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-1-reading-csv-and-txt-files/files)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-How would you import the CSV file `data.csv` and store it in a DataFrame using the Pandas module?
+¿Cómo importaría el archivo CSV `data.csv` y lo almacenaría en un DataFrame usando el módulo Pandas?
 
 ## --answers--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/reading-data-from-databases.md

@@ -1,37 +1,41 @@
 ---
 id: 5e9a093a74c4063ca6f7c163
-title: Reading Data from Databases
+title: Leyendo datos de bases de datos
 challengeType: 11
 videoId: MtgXS1MofRw
+bilibiliIds:
+  aid: 890546354
+  bvid: BV1JP4y1h7gk
+  cid: 409020851
 dashedName: reading-data-from-databases
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-11-reading-data-from-relational-databases/files)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-What method does a `Cursor` instance have and what does it allow?
+¿Qué método tiene una instancia de `Cursor` y qué permite?
 
 ## --answers--
 
-The `Cursor` instance has a `.run()` method which allows you to run SQL queries.
+La instancia `Cursor` tiene un método `.run()` que permite ejecutar consultas SQL.
 
 ---
 
-The `Cursor` instance has a `.select()` method which allows you to select records.
+La instancia `Cursor` tiene un método `.select()` que permite seleccionar registros.
 
 ---
 
-The `Cursor` instance has an `.execute()` method which will receive SQL parameters to run against the database.
+La instancia `Cursor` tiene un método `.execute()` que recibirá parámetros SQL para ejecutarse contra la base de datos.
 
 ## --video-solution--
 

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/reading-data-introduction.md

@@ -1,35 +1,39 @@
 ---
 id: 5e9a093a74c4063ca6f7c161
-title: Reading Data Introduction
+title: Introducción a Lectura de datos
 challengeType: 11
 videoId: cDnt02BcHng
+bilibiliIds:
+  aid: 548023524
+  bvid: BV1Nq4y1K7iV
+  cid: 409020187
 dashedName: reading-data-introduction
 ---
 
 # --description--
 
-*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
+*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
 
-More resources:
+Más recursos:
 
--   [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-1-reading-csv-and-txt-files/files)
--   [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
+-   [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
+-   [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
 
 # --question--
 
 ## --text--
 
-Given a file named `certificates.csv` with these contents:
+Dado un archivo llamado `certificates.csv` con estos contenidos:
 
 <pre>
-Name$Certificates$Time (in months)
+Nombre$Certificates$Tiempo (en meses)
 Tom$8$16
 Kris$2$5
 Ahmad$5$9
 Beau$6$12
 </pre>
 
-Fill in the blanks for the missing arguments below:
+Rellena los espacios en blanco de los argumentos que faltan a continuación:
 
 ```py
 import csv

curriculum/challenges/espanol/08-data-analysis-with-python/numpy/accessing-and-changing-elements,-rows,-columns.md

@@ -1,8 +1,12 @@
 ---
 id: 5e9a0a8e09c5df3cc3600ed4
-title: 'Accessing and Changing Elements, Rows, Columns'
+title: 'Acceder y cambiar elementos, filas, columnas'
 challengeType: 11
 videoId: v-7Y7koJ_N0
+bilibiliIds:
+  aid: 590517748
+  bvid: BV1Eq4y1f7Fa
+  cid: 409025392
 dashedName: accessing-and-changing-elements-rows-columns
 ---
 
@@ -10,7 +14,7 @@ dashedName: accessing-and-changing-elements-rows-columns
 
 ## --text--
 
-What code would change the values in the 3rd column of both of the following Numpy arrays to 20?
+¿Qué código cambiaría los valores en la tercera columna de los siguientes matrices Numpy a 20?
 
 ```py
 a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])

curriculum/challenges/espanol/08-data-analysis-with-python/numpy/basics-of-numpy.md

@@ -1,8 +1,12 @@
 ---
 id: 5e9a0a8e09c5df3cc3600ed3
-title: Basics of Numpy
+title: Fundamentos de Numpy
 challengeType: 11
 videoId: f9QrZrKQMLI
+bilibiliIds:
+  aid: 763014202
+  bvid: BV1K64y1a7bu
+  cid: 409025169
 dashedName: basics-of-numpy
 ---
 
@@ -10,7 +14,7 @@ dashedName: basics-of-numpy
 
 ## --text--
 
-What will the following code print?
+¿Qué hará el siguiente código?
 
 ```python
 b = np.array([[1.0,2.0,3.0],[3.0,4.0,5.0]])

curriculum/challenges/espanol/08-data-analysis-with-python/numpy/what-is-numpy.md

@@ -1,8 +1,12 @@
 ---
 id: 5e9a0a8e09c5df3cc3600ed2
-title: What is NumPy
+title: Qué es NumPy
 challengeType: 11
 videoId: 5Nwfs5Ej85Q
+bilibiliIds:
+  aid: 293086867
+  bvid: BV1Tf4y1E7QZ
+  cid: 409024791
 dashedName: what-is-numpy
 ---
 
@@ -10,23 +14,23 @@ dashedName: what-is-numpy
 
 ## --text--
 
-Why are Numpy arrays faster than regular Python lists?
+¿Por qué los arreglos Numpy son más rápidos que las listas de Python?
 
 ## --answers--
 
-Numpy does not perform type checking while iterating through objects.
+¿Por qué los arreglos Numpy son más rápidas que las listas normales de Python.
 
 ---
 
-Numpy uses fixed types.
+Numpy usa tipos fijos.
 
 ---
 
-Numpy uses contiguous memory.
+Numpy usa memoria contigua.
 
 ---
 
-All of the above.
+Todo lo anterior.
 
 ## --video-solution--
 

curriculum/challenges/espanol/09-information-security/information-security-with-helmetjs/install-and-require-helmet.md

@@ -1,6 +1,6 @@
 ---
 id: 587d8247367417b2b2512c36
-title: Install and Require Helmet
+title: Instalar y Requerir Helmet
 challengeType: 2
 forumTopicId: 301581
 dashedName: install-and-require-helmet
@@ -8,25 +8,25 @@ dashedName: install-and-require-helmet
 
 # --description--
 
-Working on these challenges will involve you writing your code using one of the following methods:
+Trabajar en estos desafíos implica escribir tu código usando uno de los siguientes métodos:
 
-- Clone [this GitHub repo](https://github.com/freeCodeCamp/boilerplate-infosec/) and complete these challenges locally.
-- Use [our Replit starter project](https://replit.com/github/freeCodeCamp/boilerplate-infosec) to complete these challenges.
-- Use a site builder of your choice to complete the project. Be sure to incorporate all the files from our GitHub repo.
+- Clona [este repositorio de Github](https://github.com/freeCodeCamp/boilerplate-infosec/) y completa estos desafíos localmente.
+- Use [nuestro proyecto inicial de Replit](https://replit.com/github/freeCodeCamp/boilerplate-infosec) para completar estos desafios.
+- Utilice un constructor de sitios de su elección para completar el proyecto. Asegúrese de incorporar todos los archivos de nuestro repositorio de GitHub.
 
-When you are done, make sure a working demo of your project is hosted somewhere public. Then submit the URL to it in the `Solution Link` field.
+Cuando haya terminado, asegúrese de que un demo funcional de su proyecto esté alojado en algún lugar público. A continuación, envíe la URL en el campo `Solution Link`.
 
-Helmet helps you secure your Express apps by setting various HTTP headers.
+Helmet te ayuda a proteger tus aplicaciones Express configurando varias cabeceras HTTP.
 
 # --instructions--
 
-All your code for these lessons goes in the `myApp.js` file between the lines of code we have started you off with. Do not change or delete the code we have added for you.
+Todo su código para estas lecciones va en el archivo `myApp.js` entre las líneas de código con las que hemos iniciado. No cambie o elimine el código que hemos añadido para usted.
 
-Install Helmet version `3.21.3`, then require it. You can install a specific version of a package with `npm install --save-exact package@version`, or by adding it to your `package.json` directly.
+Instale la versión `3.21.3` de Helmet, luego requiérala. Puede instalar una versión específica de un paquete con `npm install --save-exact package@version`, o agregándolo a su paquete `package.json` directamente.
 
 # --hints--
 
-`helmet` version `3.21.3` should be in `package.json`
+`helmet` version `3.21.3` debería estar en `package.json`
 
 ```js
 (getUserInput) =>

curriculum/challenges/espanol/09-information-security/python-for-penetration-testing/creating-a-tcp-client.md

@@ -1,8 +1,12 @@
 ---
 id: 5ea9997bbec2e9bc47e94db0
-title: Creating a TCP Client
+title: Creando un Cliente TCP
 challengeType: 11
 videoId: ugYfJNTawks
+bilibiliIds:
+  aid: 933058124
+  bvid: BV16M4y1g7zL
+  cid: 409034338
 dashedName: creating-a-tcp-client
 ---
 
@@ -10,7 +14,7 @@ dashedName: creating-a-tcp-client
 
 ## --text--
 
-Which socket object method lets you set the maximum amount of data your client accepts at once?
+¿Qué método del objeto Socket le permite definir la cantidad máxima de data que tu cliente acepta por vez?
 
 ## --answers--
 

curriculum/challenges/espanol/09-information-security/python-for-penetration-testing/developing-an-nmap-scanner-part-2.md

@@ -1,8 +1,12 @@
 ---
 id: 5ea9997bbec2e9bc47e94db2
-title: Developing an Nmap Scanner part 2
+title: Desarrollando un escáner de Nmap parte 2
 challengeType: 11
 videoId: a98PscnUsTg
+bilibiliIds:
+  aid: 505526943
+  bvid: BV1Hg411c7oE
+  cid: 409034761
 dashedName: developing-an-nmap-scanner-part-2
 ---
 
@@ -10,7 +14,7 @@ dashedName: developing-an-nmap-scanner-part-2
 
 ## --text--
 
-Which of the following allows you to scan for UDP ports between 21 to 443?
+¿Cuál de las siguientes opciones le permite buscar puertos UDP entre 21 a 443?
 
 ## --answers--
 

curriculum/challenges/espanol/10-coding-interview-prep/algorithms/find-the-symmetric-difference.md

@@ -1,6 +1,6 @@
 ---
 id: a3f503de51cf954ede28891d
-title: Find the Symmetric Difference
+title: Encuentra la diferencia simétrica
 challengeType: 5
 forumTopicId: 301611
 dashedName: find-the-symmetric-difference
@@ -8,77 +8,77 @@ dashedName: find-the-symmetric-difference
 
 # --description--
 
-The mathematical term <dfn>symmetric difference</dfn> (`△` or `⊕`) of two sets is the set of elements which are in either of the two sets but not in both. For example, for sets `A = {1, 2, 3}` and `B = {2, 3, 4}`, `A △ B = {1, 4}`.
+El término matemático <dfn>diferencia simétrica</dfn> (`△` or `⊕`) de dos conjuntos es el conjunto de elementos que están en cualquiera de los dos conjuntos, pero no en ambos. Por ejemplo, para los conjuntos `A = {1, 2, 3}` y `B = {2, 3, 4}`, `A △ B = {1, 4}`.
 
-Symmetric difference is a binary operation, which means it operates on only two elements. So to evaluate an expression involving symmetric differences among *three* elements (`A △ B △ C`), you must complete one operation at a time. Thus, for sets `A` and `B` above, and `C = {2, 3}`, `A △ B △ C = (A △ B) △ C = {1, 4} △ {2, 3} = {1, 2, 3, 4}`.
+Diferencia simétrica es una operación binaria, significa que opera en solo dos elementos. Entonces, para evaluar una expresión que involucra diferencias simétricas entre * tres * elementos (`A △ B △ C`), tienes que completar una operación a la vez. Asi, para los conjuntos `A` y `B` encima, y `C = {2, 3}`, `A △ B △ C = (A △ B) △ C = {1, 4} △ {2, 3} = {1, 2, 3, 4}`.
 
 # --instructions--
 
-Create a function that takes two or more arrays and returns an array of their symmetric difference. The returned array must contain only unique values (*no duplicates*).
+Cree una función que tome dos o más arrays y devuelva una array de sus diferencias. La array que se devuelve debe contener solo valores únicos (*no duplicados*).
 
 # --hints--
 
-`sym([1, 2, 3], [5, 2, 1, 4])` should return `[3, 4, 5]`.
+`sym([1, 2, 3], [5, 2, 1, 4])` debería retornar `[3, 4, 5]`.
 
 ```js
 assert.sameMembers(sym([1, 2, 3], [5, 2, 1, 4]), [3, 4, 5]);
 ```
 
-`sym([1, 2, 3], [5, 2, 1, 4])` should contain only three elements.
+`sym([1, 2, 3], [5, 2, 1, 4])`debería contener solo tres elementos.
 
 ```js
 assert.equal(sym([1, 2, 3], [5, 2, 1, 4]).length, 3);
 ```
 
-`sym([1, 2, 3, 3], [5, 2, 1, 4])` should return `[3, 4, 5]`.
+`sym([1, 2, 3, 3], [5, 2, 1, 4])` debería retornar `[3, 4, 5]`.
 
 ```js
 assert.sameMembers(sym([1, 2, 3, 3], [5, 2, 1, 4]), [3, 4, 5]);
 ```
 
-`sym([1, 2, 3, 3], [5, 2, 1, 4])` should contain only three elements.
+`sym([1, 2, 3, 3], [5, 2, 1, 4])` debería contener solo tres elementos.
 
 ```js
 assert.equal(sym([1, 2, 3, 3], [5, 2, 1, 4]).length, 3);
 ```
 
-`sym([1, 2, 3], [5, 2, 1, 4, 5])` should return `[3, 4, 5]`.
+`sym([1, 2, 3], [5, 2, 1, 4, 5])` debería retornar `[3, 4, 5]`.
 
 ```js
 assert.sameMembers(sym([1, 2, 3], [5, 2, 1, 4, 5]), [3, 4, 5]);
 ```
 
-`sym([1, 2, 3], [5, 2, 1, 4, 5])` should contain only three elements.
+`sym([1, 2, 3], [5, 2, 1, 4, 5])` debería contener solo tres elementos.
 
 ```js
 assert.equal(sym([1, 2, 3], [5, 2, 1, 4, 5]).length, 3);
 ```
 
-`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` should return `[1, 4, 5]`
+`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` debería retornar `[1, 4, 5]`
 
 ```js
 assert.sameMembers(sym([1, 2, 5], [2, 3, 5], [3, 4, 5]), [1, 4, 5]);
 ```
 
-`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` should contain only three elements.
+`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` debería contener solo tres elementos.
 
 ```js
 assert.equal(sym([1, 2, 5], [2, 3, 5], [3, 4, 5]).length, 3);
 ```
 
-`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` should return `[1, 4, 5]`.
+`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` debería retornar `[1, 4, 5]`.
 
 ```js
 assert.sameMembers(sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5]), [1, 4, 5]);
 ```
 
-`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` should contain only three elements.
+`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` debería contener solo tres elementos.
 
 ```js
 assert.equal(sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5]).length, 3);
 ```
 
-`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` should return `[2, 3, 4, 6, 7]`.
+`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` debería retornar `[2, 3, 4, 6, 7]`.
 
 ```js
 assert.sameMembers(
@@ -87,7 +87,7 @@ assert.sameMembers(
 );
 ```
 
-`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` should contain only five elements.
+`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` debería contener solo cinco elementos.
 
 ```js
 assert.equal(
@@ -96,7 +96,7 @@ assert.equal(
 );
 ```
 
-`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` should return `[1, 2, 4, 5, 6, 7, 8, 9]`.
+`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` debería retornar `[1, 2, 4, 5, 6, 7, 8, 9]`.
 
 ```js
 assert.sameMembers(
@@ -112,7 +112,7 @@ assert.sameMembers(
 );
 ```
 
-`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` should contain only eight elements.
+`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` debería contener solo ocho elementos.
 
 ```js
 assert.equal(

curriculum/challenges/espanol/10-coding-interview-prep/algorithms/implement-bubble-sort.md

@@ -1,6 +1,6 @@
 ---
 id: 8d5123c8c441eddfaeb5bdef
-title: Implement Bubble Sort
+title: Implementar ordenación de burbuja
 challengeType: 1
 forumTopicId: 301612
 dashedName: implement-bubble-sort
@@ -8,23 +8,23 @@ dashedName: implement-bubble-sort
 
 # --description--
 
-This is the first of several challenges on sorting algorithms. Given an array of unsorted items, we want to be able to return a sorted array. We will see several different methods to do this and learn some tradeoffs between these different approaches. While most modern languages have built-in sorting methods for operations like this, it is still important to understand some of the common basic approaches and learn how they can be implemented.
+Este es el primero de varios retos en algoritmos de ordenación. Dado un arreglo de elementos desordenados, queremos ser capaces de devolver un arreglo ordenado. Veremos diferentes métodos para hacerlo y aprenderemos algunos pros y contras entre estos diferentes enfoques. Mientras que la mayoría de los lenguajes modernos tienen métodos de ordenamiento incorporados para este tipo de operaciones, sigue siendo importante entender algunos de los enfoques básicos y aprender cómo pueden implementarse.
 
-Here we will see bubble sort. The bubble sort method starts at the beginning of an unsorted array and 'bubbles up' unsorted values towards the end, iterating through the array until it is completely sorted. It does this by comparing adjacent items and swapping them if they are out of order. The method continues looping through the array until no swaps occur at which point the array is sorted.
+Aquí veremos el algoritmo de burbuja. El método de ordenación de burbuja comienza al inicio de un arreglo desordenado y 'burbujea hacia arriba' los valores no ordenados, iterando a través del arreglo hasta que esté completamente ordenado. Esto se hace comparando elementos adyacentes e intercambiándolos si no están en orden. El método continúa iterando sobre el arreglo hasta que no hay mas intercambios, lo que significa que el arreglo esta ordenado.
 
-This method requires multiple iterations through the array and for average and worst cases has quadratic time complexity. While simple, it is usually impractical in most situations.
+Este método requiere múltiples iteraciones a través del arreglo y para el promedio y el peor de los casos tiene complejidad de tiempo cuadrática. Si bien es simple, suele ser poco práctico en la mayoría de las situaciones.
 
-**Instructions:** Write a function `bubbleSort` which takes an array of integers as input and returns an array of these integers in sorted order from least to greatest.
+**Instrucciones:** Escribe una función `bubbleSort` que tomará un arreglo de números enteros y retornará un arreglo con estos números, ordenados de menor a mayor.
 
 # --hints--
 
-`bubbleSort` should be a function.
+`bubbleSort` Debería ser una función.
 
 ```js
 assert(typeof bubbleSort == 'function');
 ```
 
-`bubbleSort` should return a sorted array (least to greatest).
+`bubbleSort` Debería retornar un arreglo ordenado (de menor a mayor).
 
 ```js
 assert(
@@ -52,7 +52,7 @@ assert(
 );
 ```
 
-`bubbleSort` should return an array that is unchanged except for order.
+`bubbleSort([1,4,2,8,345,123,43,32,5643,63,123,43,2,55,1,234,92])` Debería retornar el mismo arreglo cambiando solo el orden de los números.
 
 ```js
 assert.sameMembers(
@@ -79,7 +79,7 @@ assert.sameMembers(
 );
 ```
 
-`bubbleSort` should not use the built-in `.sort()` method.
+`bubbleSort` No debería usar el método incorporado `.sort()`.
 
 ```js
 assert(isBuiltInSortUsed());
@@ -113,8 +113,6 @@ function bubbleSort(array) {
   return array;
   // Only change code above this line
 }
-
-bubbleSort([1, 4, 2, 8, 345, 123, 43, 32, 5643, 63, 123, 43, 2, 55, 1, 234, 92]);
 ```
 
 # --solutions--

curriculum/challenges/espanol/10-coding-interview-prep/algorithms/implement-insertion-sort.md

@@ -1,6 +1,6 @@
 ---
 id: 587d8259367417b2b2512c86
-title: Implement Insertion Sort
+title: Implementar Orden de Inserción
 challengeType: 1
 forumTopicId: 301613
 dashedName: implement-insertion-sort
@@ -8,19 +8,19 @@ dashedName: implement-insertion-sort
 
 # --description--
 
-The next sorting method we'll look at is insertion sort. This method works by building up a sorted array at the beginning of the list. It begins the sorted array with the first element. Then it inspects the next element and swaps it backwards into the sorted array until it is in sorted position. It continues iterating through the list and swapping new items backwards into the sorted portion until it reaches the end. This algorithm has quadratic time complexity in the average and worst cases.
+El siguiente método de clasificación que veremos es el orden de las inserciones. Este método funciona construyendo un arreglo ordenado al principio de la lista. Comienza el arreglo ordenado con el primer elemento. Luego inspecciona el siguiente elemento, lo intercambia de atrás hacia adelante dentro de el arreglo clasificado hasta que esté en posición ordenada. Continúa iterando a través de la lista y cambiando nuevos elementos hacia atrás en la porción ordenada hasta llegar al final. Este algoritmo tiene una complejidad temporal cuadrática en el caso medio y en el peor.
 
-**Instructions:** Write a function `insertionSort` which takes an array of integers as input and returns an array of these integers in sorted order from least to greatest.
+**Instrucciones:** Escribe una función `insertionSort` que toma un "array" de enteros como entrada y devuelve un array de estos enteros ordenados de menor a mayor.
 
 # --hints--
 
-`insertionSort` should be a function.
+`insertionSort` debería ser una función.
 
 ```js
 assert(typeof insertionSort == 'function');
 ```
 
-`insertionSort` should return a sorted array (least to greatest).
+`insertionSort` debería devolver un arreglo ordenado (de menor al más grande).
 
 ```js
 assert(
@@ -48,7 +48,7 @@ assert(
 );
 ```
 
-`insertionSort` should return an array that is unchanged except for order.
+`insertionSort([1,4,2,8,345,123,43,32,5643,63,123,43,2,55,1,234,92])` debe devolver un arreglo sin cambios excepto por el orden.
 
 ```js
 assert.sameMembers(
@@ -75,7 +75,13 @@ assert.sameMembers(
 );
 ```
 
-`insertionSort` should not use the built-in `.sort()` method.
+`insertionSort([5, 4, 33, 2, 8])` debe devolver `[2, 4, 5, 8, 33]`.
+
+```js
+assert.deepEqual(insertionSort([5, 4, 33, 2, 8]), [2, 4, 5, 8, 33])
+```
+
+`insertionSort` no debe utilizar el método "buil-in" `.sort()`.
 
 ```js
 assert(isBuiltInSortUsed());
@@ -109,8 +115,6 @@ function insertionSort(array) {
   return array;
   // Only change code above this line
 }
-
-insertionSort([1, 4, 2, 8, 345, 123, 43, 32, 5643, 63, 123, 43, 2, 55, 1, 234, 92]);
 ```
 
 # --solutions--

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/deep-learning-demystified.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cc
-title: Deep Learning Demystified
+title: Deep Learning Desmitificado
 challengeType: 11
 videoId: bejQ-W9BGJg
 dashedName: deep-learning-demystified
@@ -10,23 +10,23 @@ dashedName: deep-learning-demystified
 
 ## --text--
 
-How should you assign weights to input neurons before training your network for the first time?
+¿Cómo deberías asignar pesos a las neuronas de entrada antes de entrenar tu red por primera vez?
 
 ## --answers--
 
-From smallest to largest.
+De más pequeño a más grande.
 
 ---
 
-Completely randomly.
+Completamente al azar.
 
 ---
 
-Alphabetically.
+Alfabéticamente.
 
 ---
 
-None of the above.
+Ninguna de las anteriores.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/how-convolutional-neural-networks-work.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cd
-title: How Convolutional Neural Networks work
+title: Cómo funcionan las Redes Neuronales Convolucionales
 challengeType: 11
 videoId: Y5M7KH4A4n4
 dashedName: how-convolutional-neural-networks-work
@@ -10,19 +10,19 @@ dashedName: how-convolutional-neural-networks-work
 
 ## --text--
 
-When are Convolutional Neural Networks not useful?
+¿Cuándo las Redes Neurales Convolucionales no son útiles?
 
 ## --answers--
 
-If your data can't be made to look like an image, or if you can rearrange elements of your data and it's still just as useful.
+Si tus datos no pueden ser hechos para parecer una imagen, o si puedes reorganizar elementos de tus datos y es igualmente útil.
 
 ---
 
-If your data is made up of different 2D or 3D images.
+Si sus datos se componen de diferentes imágenes 2D o 3D.
 
 ---
 
-If your data is text or sound based.
+Si sus datos son basados en texto o sonido.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/how-deep-neural-networks-work.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610ca
-title: How Deep Neural Networks Work
+title: Cómo Funcionan las Redes Neuronales Profundas
 challengeType: 11
 videoId: zvalnHWGtx4
 dashedName: how-deep-neural-networks-work
@@ -10,19 +10,19 @@ dashedName: how-deep-neural-networks-work
 
 ## --text--
 
-Why is it better to calculate the gradient (slope) directly rather than numerically?
+¿Por qué es mejor calcular la gradiente (pendiente) directamente que numéricamente?
 
 ## --answers--
 
-It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
+Es computacionalmente caro volver a través de toda la red neuronal y ajustar los pesos para cada capa de la red neuronal.
 
 ---
 
-It is more accurate.
+Es más preciso.
 
 ---
 
-There is no difference between the two methods.
+No hay diferencia entre ambos métodos.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/recurrent-neural-networks-rnn-and-long-short-term-memory-lstm.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cb
-title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
+title: Redes Neurales Recurrentes RNN y Memoria a Largo Plazo LSTM
 challengeType: 11
 videoId: UVimlsy9eW0
 dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
@@ -10,19 +10,19 @@ dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
 
 ## --text--
 
-What are the main neural network components that make up a Long Short Term Memory network?
+¿Cuáles son los principales componentes de la red neuronal que componen una red de memoria a largo plazo?
 
 ## --answers--
 
-New information and prediction.
+Nueva información y predicción.
 
 ---
 
-Prediction, collected possibilities, and selection.
+Predicción, posibilidades recolectadas y selección.
 
 ---
 
-Prediction, ignoring, forgetting, and selection.
+Predicción, ignoración, olvido, y selección.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-evaluating-the-model.md

@@ -1,8 +1,12 @@
 ---
 id: 5e8f2f13c4cdbe86b5c72d99
-title: 'Convolutional Neural Networks: Evaluating the Model'
+title: 'Redes Neurales Convolucionales: Evaluando el Modelo'
 challengeType: 11
 videoId: eCATNvwraXg
+bilibiliIds:
+  aid: 933030136
+  bvid: BV1hM4y1g7Bx
+  cid: 409132265
 dashedName: convolutional-neural-networks-evaluating-the-model
 ---
 
@@ -10,19 +14,19 @@ dashedName: convolutional-neural-networks-evaluating-the-model
 
 ## --text--
 
-What is **not** a good way to increase the accuracy of a convolutional neural network?
+¿Qué **no** es una buena manera de incrementar la precisión de una red neuronal convolucional?
 
 ## --answers--
 
-Augmenting the data you already have.
+Aumentando los datos que ya tiene.
 
 ---
 
-Using a pre-trained model.
+Usando un model pre-entrenado.
 
 ---
 
-Using your test data to retrain the model.
+Usando tus datos de prueba para re entrenar el modelo.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-picking-a-pretrained-model.md

@@ -1,8 +1,12 @@
 ---
 id: 5e8f2f13c4cdbe86b5c72d9a
-title: 'Convolutional Neural Networks: Picking a Pretrained Model'
+title: 'Redes Neuronales Convolucionales: Eligiendo un Modelo Pre-entrenado'
 challengeType: 11
 videoId: h1XUt1AgIOI
+bilibiliIds:
+  aid: 463063633
+  bvid: BV1qL411x73q
+  cid: 409132626
 dashedName: convolutional-neural-networks-picking-a-pretrained-model
 ---
 
@@ -10,7 +14,7 @@ dashedName: convolutional-neural-networks-picking-a-pretrained-model
 
 ## --text--
 
-Fill in the blanks below to use Google's pre-trained MobileNet V2 model as a base for a convolutional neural network:
+Completa los siguientes espacios en blanco para utilizar el modelo pre-entrenado MobileNet V2 de Google como base para una red neuronal convolucional:
 
 ```py
 base_model = tf.__A__.applications.__B__(input_shape=(160, 160, 3),

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning.md

@@ -1,8 +1,12 @@
 ---
 id: 5e8f2f13c4cdbe86b5c72da3
-title: Reinforcement Learning With Q-Learning
+title: Aprendizaje Reforzado con Q-Learning
 challengeType: 11
 videoId: Cf7DSU0gVb4
+bilibiliIds:
+  aid: 463025802
+  bvid: BV1iL411x7L6
+  cid: 409138811
 dashedName: reinforcement-learning-with-q-learning
 ---
 
@@ -10,19 +14,19 @@ dashedName: reinforcement-learning-with-q-learning
 
 ## --text--
 
-The key components of reinforcement learning are...
+Los componente clave del Aprendizaje Reforzado son...
 
 ## --answers--
 
-environment, representative, state, reaction, and reward.
+entorno, representatividad, estado, reacción, y recompensa.
 
 ---
 
-environment, agent, state, action, and reward.
+entorno, agente, estado, acción, y recompensa.
 
 ---
 
-habitat, agent, state, action, and punishment.
+habitat, agente, estado, acción, y castigo.
 
 ## --video-solution--
 

curriculum/challenges/italian/13-relational-databases/build-a-number-guessing-game-project/build-a-number-guessing-game.md

@@ -1,6 +1,6 @@
 ---
 id: 602da04c22201c65d2a019f4
-title: Build a Number Guessing Game
+title: Costruisci un gioco di indovinare il numero
 challengeType: 13
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-number-guessing-game
@@ -9,15 +9,15 @@ dashedName: build-a-number-guessing-game
 
 # --description--
 
-This is one of the required projects to earn your certification. For this project, you will use Bash scripting, PostgreSQL, and Git to create a number guessing game that runs in the terminal and saves user information.
+Questo è uno dei progetti richiesti per completare la certificazione. Per questo progetto, userai Bash scripting, PostgreSQL e Git per creare un gioco di indovinare il numero che esegue nel terminale e salva le informazioni dell'utente.
 
 # --instructions--
 
-**Important:** After you pass all the project tests, save a dump of your database into a `number_guessers.sql` file, as well as your whole `number_guessing_game` folder, so you can complete step 2. There will be instructions how to do that within the virtual machine.
+**Importante:** Dopo che passi tutti i test, salva un dump del tuo database nel file `number_guess.sql`, così come il tuo file `number_guess.sh`, così puoi completare lo step 2. Ci saranno istruzioni su come farlo nella macchina virtuale.
 
 # --notes--
 
-Required files: `number_guessers.sql`, and the whole `number_guessing_game` folder
+File richiesti: `number_guess.sql`, `number_guess.sh`
 
 # --hints--
 

curriculum/challenges/italian/13-relational-databases/build-a-periodic-table-database-project/build-a-periodic-table-database.md

@@ -1,6 +1,6 @@
 ---
 id: 602d9ff222201c65d2a019f2
-title: Build a Periodic Table Database
+title: Costruisci un database della tavola periodica
 challengeType: 13
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-periodic-table-database
@@ -9,15 +9,15 @@ dashedName: build-a-periodic-table-database
 
 # --description--
 
-This is one of the required projects to earn your certification. For this project, you will create Bash a script to get information about chemical elements from a periodic table database.
+Questo è uno dei progetti richiesti per completare la certificazione. Per questo progetto, creerai uno script Bash per ottenere informazioni sugli elementi chimici da un database della tavola periodica.
 
 # --instructions--
 
-**Important:** After you pass all the project tests, save a dump of your database into a `elements.sql` file, as well as your whole `periodic_table` folder, so you can complete step 2. There will be instructions how to do that within the virtual machine.
+**Importante:** Dopo che passi tutti i test, salva un dump del tuo database nel file `periodic_table.sql` così come il tuo file `element.sh` così puoi completare lo step 2. Ci saranno istruzioni su come farlo nella macchina virtuale.
 
 # --notes--
 
-Required files: `elements.sql`, and the whole `periodic_table` folder
+File richiesti: `periodic_table.sql`, `element.sh`
 
 # --hints--
 

curriculum/challenges/italian/13-relational-databases/learn-advanced-bash-by-building-a-kitty-ipsum-translator/build-a-kitty-ipsum-translator.md

@@ -1,6 +1,6 @@
 ---
 id: 602da0de22201c65d2a019f6
-title: Build a Kitty Ipsum Translator
+title: Costruisci un traduttore Kitty Ipsum
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-advanced-bash-by-building-a-kitty-ipsum-translator
@@ -9,7 +9,7 @@ dashedName: build-a-kitty-ipsum-translator
 
 # --description--
 
-In this 140 lesson course, you will learn some more complex commands, and the details of how commands work.
+In questo corso da 140 lezioni, imparerai comandi un po' più complessi e i dettagli di come i comandi lavorano.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-bash-and-sql-by-building-a-bike-rental-shop/build-a-bike-rental-shop.md

@@ -1,6 +1,6 @@
 ---
 id: 5f5b969a05380d2179fe6e18
-title: Build a Bike Rental Shop
+title: Costruisci un negozio di noleggio biciclette
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-bash-and-sql-by-building-a-bike-rental-shop
@@ -9,7 +9,7 @@ dashedName: build-a-bike-rental-shop
 
 # --description--
 
-In this 210 lesson course, you will build an interactive Bash program that stores rental information for your bike rental shop using PostgreSQL.
+In questo corso da 210 lezioni, costruirai un programma Bash interattivo che immagazzina informazioni per il tuo negozio di noleggio biciclette che usa PostgresSQL.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-bash-by-building-a-boilerplate/build-a-boilerplate.md

@@ -1,6 +1,6 @@
 ---
 id: 5ea8adfab628f68d805bfc5e
-title: Build a Boilerplate
+title: Crea un boilerplate
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-bash-by-building-a-boilerplate
@@ -9,7 +9,7 @@ dashedName: build-a-boilerplate
 
 # --description--
 
-In this 170 lesson course, you will learn basic commands by creating a website boilerplate using only the command line.
+In questo corso da 170 lezioni, imparerai i comandi del terminale creando un boilerplate di un sito web usando solo la riga di comando.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-bash-scripting-by-building-five-programs/build-five-programs.md

@@ -1,6 +1,6 @@
 ---
 id: 5f5904ac738bc2fa9efecf5a
-title: Build Five Programs
+title: Costruisci Cinque Programmi
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-bash-scripting-by-building-five-programs
@@ -9,7 +9,7 @@ dashedName: build-five-programs
 
 # --description--
 
-In this 220 lesson course, you will learn more terminal commands and how to use them within Bash scripts by creating five small programs.
+In questo corso con 220 lezioni, imparerai più comandi per il terminale e come usarli con script di Bash creando cinque piccoli programmi.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-git-by-building-an-sql-reference-object/build-an-sql-reference-object.md

@@ -1,6 +1,6 @@
 ---
 id: 5fa323cdaf6a73463d590659
-title: Build an SQL Reference Object
+title: Costruisci un oggetto di riferimento SQL
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-git-by-building-an-sql-reference-object
@@ -9,7 +9,7 @@ dashedName: build-an-sql-reference-object
 
 # --description--
 
-In this 240 lesson course, you will learn how Git keeps track of your code by creating an object containing commonly used SQL commands.
+In questo corso da 240 lezioni, imparareai come Git tiene traccia del tuo codice creando un oggetto che contiene comadni SQL comunemente usati.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-nano-by-building-a-castle/build-a-castle.md

@@ -1,6 +1,6 @@
 ---
 id: 5f32db63eb37f7e17323f459
-title: Build a Castle
+title: Costruisci un castello
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-nano-by-building-a-castle
@@ -9,7 +9,7 @@ dashedName: build-a-castle
 
 # --description--
 
-In this 40 lesson course, you will learn how to edit files in the terminal with Nano while building a castle.
+In questo corso di 40 lezioni, imparerai a modificare i file nel terminale con Nano mentre costruisci un castello.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-relational-databases-by-building-a-mario-database/build-a-mario-database.md

@@ -1,6 +1,6 @@
 ---
 id: 5f2c289f164c29556da632fd
-title: Build a Mario Database
+title: Costruisci un database di Mario
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-relational-databases-by-building-a-mario-database
@@ -9,7 +9,7 @@ dashedName: build-a-mario-database
 
 # --description--
 
-In this 165 lesson course, you will learn the basics of relational databases by creating a PostgreSQL database filled with video game characters.
+In questo corso da 165 lezioni, imparerai le basi di un database relazionale creando un database PostgresSQL pieno di personaggi di videogiochi.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-sql-by-building-a-student-database-part-1/build-a-student-database-part-1.md

@@ -1,6 +1,6 @@
 ---
 id: 602da0c222201c65d2a019f5
-title: "Build a Student Database: Part 1"
+title: "Costruisci un database degli studenti: parte 1"
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-sql-by-building-a-student-database-part-1
@@ -9,7 +9,7 @@ dashedName: build-a-student-database-part-1
 
 # --description--
 
-In this 140 lesson course, you will create a Bash script that uses SQL to enter information about your computer science students into PostgreSQL.
+In questo corso di 140 lezioni, creerai uno script Bash che utilizza SQL per inserire informazioni sui tuoi studenti di informatica in PostgreSQL.
 
 # --instructions--
 

curriculum/challenges/italian/13-relational-databases/learn-sql-by-building-a-student-database-part-2/build-a-student-database-part-2.md

@@ -1,6 +1,6 @@
 ---
 id: 618590adb0730ca724e37672
-title: "Build a Student Database: Part 2"
+title: "Costruisci un database degli studenti: parte 2"
 challengeType: 12
 helpCategory: Backend Development
 url: https://github.com/freeCodeCamp/learn-sql-by-building-a-student-database-part-2
@@ -9,7 +9,7 @@ dashedName: build-a-student-database-part-2
 
 # --description--
 
-In this 140 lesson course, you will complete your student database while diving deeper into SQL commands.
+In questo corso da 140 lezioni, completerai il tuo database degli studenti andando più a fondo nei comandi SQL.
 
 # --instructions--
 

curriculum/challenges/portuguese/14-responsive-web-design-22/learn-accessibility-by-building-a-quiz/613297a923965e0703b64796.md

@@ -0,0 +1,57 @@
+---
+id: 613297a923965e0703b64796
+title: Etapa 2
+challengeType: 0
+dashedName: step-2
+---
+
+# --description--
+
+Você já deve estar familiarizado com a tag `meta`. Ela é usada para especificar informações sobre a página como título, descrição, palavras-chave e autor.
+
+Dê à página uma tag `meta` com um valor apropriado para `charset`.
+
+O atributo `charset` especifica a codificação de caracteres da página e, hoje em dia, `UTF-8` é a única codificação suportada pela maioria dos navegadores.
+
+# --hints--
+
+Você deve criar um elemento `meta` ao redor do elemento `head`.
+
+```js
+// TODO: Once builder is fixed so head info is not in body
+assert.exists(document.querySelector('head > meta'));
+```
+
+Você deve dar à tag `meta` um `charset` de `UTF-8`.
+
+```js
+assert.equal(document.querySelector('head > meta')?.getAttribute('charset'), 'UTF-8');
+```
+
+# --seed--
+
+## --seed-contents--
+
+```html
+<!DOCTYPE html>
+<html lang="en">
+--fcc-editable-region--
+  <head>
+    <link rel="stylesheet" href="styles.css" />
+  </head>
+--fcc-editable-region--
+  <body>
+
+  </body>
+</html>
+
+```
+
+```css
+body {
+    background: #f5f6f7;
+    color: #1b1b32;
+    font-family: Helvetica;
+    margin: 0;
+}
+```

curriculum/challenges/portuguese/14-responsive-web-design-22/learn-accessibility-by-building-a-quiz/61329b210dac0b08047fd6ab.md

@@ -0,0 +1,62 @@
+---
+id: 61329b210dac0b08047fd6ab
+title: Etapa 3
+challengeType: 0
+dashedName: step-3
+---
+
+# --description--
+
+Continuando com as tags `meta`, uma definição de `viewport` informa ao navegador como renderizar a página. Sua inclusão melhora a acessibilidade visual no celular e a _SEO_ (otimização de mecanismos de busca).
+
+Adicione uma definição de `viewport` com um atributo `content` detalhando a `width` (largura) e a `initial-scale` (escala inicial) da página.
+
+# --hints--
+
+Você deve criar outro elemento `meta` na tag `head`.
+
+```js
+assert.equal(document.querySelectorAll('head > meta')?.length, 2);
+```
+
+Você deve dar à tag `meta` um atributo `name` do tipo `viewport`.
+
+```js
+assert.equal(document.querySelectorAll('head > meta[name="viewport"]')?.length, 1);
+```
+
+Você deve dar à tag `meta` um atributo `content` do tipo `width=device-width, initial-scale=1`.
+
+```js
+// TODO: Double-check this is the only correct answer
+assert.equal(document.querySelectorAll('head > meta[content="width=device-width, initial-scale=1.0"]')?.length || document.querySelectorAll('head > meta[content="width=device-width, initial-scale=1"]')?.length, 1);
+```
+
+# --seed--
+
+## --seed-contents--
+
+```html
+<!DOCTYPE html>
+<html lang="en">
+--fcc-editable-region--
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="stylesheet" href="styles.css" />
+  </head>
+--fcc-editable-region--
+  <body>
+
+  </body>
+</html>
+
+```
+
+```css
+body {
+    background: #f5f6f7;
+    color: #1b1b32;
+    font-family: Helvetica;
+    margin: 0;
+}
+```