diff --git a/translations/en/1-Introduction/01-defining-data-science/README.md b/translations/en/1-Introduction/01-defining-data-science/README.md
index 4fa112ad..df2bc58c 100644
--- a/translations/en/1-Introduction/01-defining-data-science/README.md
+++ b/translations/en/1-Introduction/01-defining-data-science/README.md
@@ -1,29 +1,29 @@
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-Of course, we can go further. For example, we could analyze the test results to identify which specific questions are most often answered incorrectly. This could help us pinpoint areas where the material might need to be clarified or expanded. Additionally, we could track how students navigate through the course, such as which sections they revisit or skip, to better understand their learning patterns.
+Of course, depending on the specific data, some steps might be skipped (e.g., if the data is already stored in a database or if model training isn't necessary), or some steps might be repeated multiple times (such as data processing).
 
-By collecting and analyzing this data, we can make informed decisions to improve the course structure, content, and delivery. This is a simple example of how digitalization (collecting data about the course) and digital transformation (using that data to improve the course) can work together to enhance outcomes.
+## Digitalization and Digital Transformation
 
-## Summary
+Over the past decade, many businesses have come to realize the importance of data in making informed decisions. To apply data science principles effectively in a business context, the first step is to collect relevant data—essentially converting business processes into digital formats. This process is referred to as **digitalization**. Leveraging data science techniques on this digitized data to inform decisions can lead to significant productivity gains or even a complete business pivot, a process known as **digital transformation**.
 
-Data is everywhere, and its importance has grown significantly with the advent of computers and the Internet. Data science is the field that helps us extract knowledge and actionable insights from data, using scientific methods and computational tools. It operates on structured, semi-structured, and unstructured data, and spans a wide range of application domains.
+Let’s consider an example. Suppose we are delivering a data science course (like this one) online to students, and we want to use data science to improve it. How might we go about this?
 
-Understanding the types of data, where to find it, and how to use it effectively is key to leveraging data science. By applying these principles, businesses and individuals can make better decisions, optimize processes, and even transform the way they operate.
-You might argue that this approach isn't perfect, as modules can vary in length. It would probably be fairer to divide the time by the module's length (measured in the number of characters) and compare those values instead.
+We could start by asking, "What aspects can be digitized?" The simplest approach might involve measuring the time it takes each student to complete each module and assessing their knowledge through a multiple-choice test at the end of each module. By averaging the completion times across all students, we could identify which modules are the most challenging and focus on simplifying them.
+You might argue that this method isn't perfect, as modules can vary in length. It would likely be more equitable to divide the time by the module's length (measured in the number of characters) and compare those results instead.
 When analyzing the results of multiple-choice tests, we can identify concepts that students struggle to understand and use this information to improve the content. To achieve this, tests should be designed so that each question corresponds to a specific concept or piece of knowledge.
 
-For a more advanced approach, we can compare the time taken to complete each module with the age group of the students. This might reveal that certain age groups take an unusually long time to finish a module or that students drop out before completing it. Such insights can help us recommend appropriate age groups for the module and reduce dissatisfaction caused by mismatched expectations.
+For a more advanced approach, we can compare the time taken to complete each module with the age group of the students. This might reveal that certain age groups take an unusually long time to finish the module or that students drop out before completing it. Such insights can help us recommend appropriate age groups for the module and reduce dissatisfaction caused by mismatched expectations.
 
 ## 🚀 Challenge
 
-In this challenge, we will identify concepts related to the field of Data Science by analyzing texts. We'll use a Wikipedia article on Data Science, download and process the text, and then create a word cloud similar to this one:
+In this challenge, we will identify concepts related to the field of Data Science by analyzing texts. We'll use a Wikipedia article on Data Science, download and process the text, and then create a word cloud like this one:
 
 ![Word Cloud for Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.en.png)
 
@@ -31,7 +31,7 @@ Check out [`notebook.ipynb`](../../../../../../../../../1-Introduction/01-defini
 
 > If you're unfamiliar with running code in a Jupyter Notebook, refer to [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/1)
 
 ## Assignments
 
diff --git a/translations/en/1-Introduction/02-ethics/README.md b/translations/en/1-Introduction/02-ethics/README.md
index 0b2c3b1b..08793f79 100644
--- a/translations/en/1-Introduction/02-ethics/README.md
+++ b/translations/en/1-Introduction/02-ethics/README.md
@@ -1,8 +1,8 @@
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@@ -19,25 +19,25 @@ We are all data citizens living in a world shaped by data.
 
 Market trends suggest that by 2022, one in three large organizations will buy and sell their data through online [Marketplaces and Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/). As **App Developers**, it will become easier and more affordable to integrate data-driven insights and algorithm-based automation into everyday user experiences. However, as AI becomes more widespread, we must also understand the potential harms caused by the [weaponization](https://www.youtube.com/watch?v=TQHs8SA1qpk) of such algorithms on a large scale.
 
-Trends also show that by 2025, we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data. As **Data Scientists**, this provides unprecedented access to personal data, enabling us to build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) while potentially nudging users toward outcomes we prefer. This raises broader questions about data privacy and user protections.
+Trends also show that by 2025, we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data. As **Data Scientists**, this gives us unprecedented access to personal data, enabling us to build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) while potentially steering users toward outcomes we prefer. This raises broader questions about data privacy and user protections.
 
-Data ethics now serve as _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) highlights trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around the _democratization_ and _industrialization_ of AI.
+Data ethics now serve as _essential guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) highlights trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around the _democratization_ and _industrialization_ of AI.
 
 ![Gartner's Hype Cycle for AI - 2020](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
 
-In this lesson, we'll dive into the fascinating field of data ethics—covering core concepts and challenges, case studies, and applied AI concepts like governance—that help establish an ethics culture within teams and organizations working with data and AI.
+In this lesson, we'll dive into the fascinating field of data ethics—covering core concepts and challenges, case studies, and applied AI concepts like governance—to help establish an ethics culture within teams and organizations working with data and AI.
 
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/2) 🎯
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/2) 🎯
 
 ## Basic Definitions
 
 Let’s begin by understanding some basic terminology.
 
-The word "ethics" comes from the [Greek word "ethikos"](https://en.wikipedia.org/wiki/Ethics) (and its root "ethos"), meaning _character or moral nature_. 
+The word "ethics" originates from the [Greek word "ethikos"](https://en.wikipedia.org/wiki/Ethics) (and its root "ethos"), meaning _character or moral nature_. 
 
-**Ethics** refers to the shared values and moral principles that guide our behavior in society. Ethics are not based on laws but on widely accepted norms of what is "right vs. wrong." However, ethical considerations can influence corporate governance initiatives and government regulations, creating incentives for compliance.
+**Ethics** refers to the shared values and moral principles that guide our behavior in society. Ethics are not based on laws but on widely accepted norms of what is "right versus wrong." However, ethical considerations can influence corporate governance initiatives and government regulations, creating incentives for compliance.
 
-**Data Ethics** is a [new branch of ethics](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) that "studies and evaluates moral problems related to _data, algorithms, and corresponding practices_." Here, **"data"** focuses on actions like generation, recording, curation, processing, dissemination, sharing, and usage; **"algorithms"** focuses on AI, agents, machine learning, and robots; and **"practices"** focuses on topics like responsible innovation, programming, hacking, and ethics codes.
+**Data Ethics** is a [new branch of ethics](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) that "studies and evaluates moral problems related to _data, algorithms, and corresponding practices_." Here, **"data"** focuses on actions like generation, recording, curation, processing, dissemination, sharing, and usage; **"algorithms"** focuses on AI, agents, machine learning, and robots; and **"practices"** addresses topics like responsible innovation, programming, hacking, and ethical codes.
 
 **Applied Ethics** is the [practical application of moral considerations](https://en.wikipedia.org/wiki/Applied_ethics). It involves actively investigating ethical issues in the context of _real-world actions, products, and processes_ and taking corrective measures to ensure alignment with defined ethical values.
 
@@ -49,7 +49,7 @@ In this section, we’ll discuss concepts like **shared values** (principles) an
 
 ### 1. Ethics Principles
 
-Every data ethics strategy starts by defining _ethical principles_—the "shared values" that describe acceptable behaviors and guide compliant actions in data and AI projects. These can be defined at an individual or team level, but most large organizations outline them in an _ethical AI_ mission statement or framework that is defined at the corporate level and enforced consistently across all teams.
+Every data ethics strategy starts with defining _ethical principles_—the "shared values" that describe acceptable behaviors and guide compliant actions in data and AI projects. These principles can be defined at an individual or team level, but most large organizations outline them in an _ethical AI_ mission statement or framework that is enforced consistently across all teams.
 
 **Example:** Microsoft's [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai) mission statement reads: _"We are committed to the advancement of AI driven by ethical principles that put people first"_—identifying six ethical principles in the framework below:
 
@@ -62,15 +62,15 @@ Let’s briefly explore these principles. _Transparency_ and _accountability_ ar
 * [**Fairness**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) focuses on ensuring AI treats _all people_ fairly, addressing systemic or implicit socio-technical biases in data and systems.
 * [**Reliability & Safety**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) ensures that AI behaves _consistently_ with defined values, minimizing potential harms or unintended consequences.
 * [**Privacy & Security**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) involves understanding data lineage and providing _data privacy and related protections_ to users.
-* [**Inclusiveness**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) focuses on designing AI solutions with intention, adapting them to meet a _broad range of human needs_ and capabilities.
+* [**Inclusiveness**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) focuses on designing AI solutions intentionally, adapting them to meet a _broad range of human needs_ and capabilities.
 
 > 🚨 Think about what your data ethics mission statement could be. Explore ethical AI frameworks from other organizations—examples include [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles), and [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). What shared values do they have in common? How do these principles relate to the AI products or industries they operate in?
 
 ### 2. Ethics Challenges
 
-After defining ethical principles, the next step is to evaluate our data and AI actions to ensure they align with those shared values. Consider your actions in two categories: _data collection_ and _algorithm design_.
+Once ethical principles are defined, the next step is to evaluate our data and AI actions to ensure they align with those shared values. Consider your actions in two categories: _data collection_ and _algorithm design_.
 
-In data collection, actions often involve **personal data** or personally identifiable information (PII) for identifiable living individuals. This includes [various types of non-personal data](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) that, when combined, can identify an individual. Ethical challenges may relate to _data privacy_, _data ownership_, and topics like _informed consent_ and _intellectual property rights_ for users.
+In data collection, actions often involve **personal data** or personally identifiable information (PII) for identifiable living individuals. This includes [various types of non-personal data](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) that _collectively_ identify an individual. Ethical challenges may relate to _data privacy_, _data ownership_, and topics like _informed consent_ and _intellectual property rights_ for users.
 
 In algorithm design, actions involve collecting and curating **datasets**, then using them to train and deploy **data models** that predict outcomes or automate decisions in real-world contexts. Ethical challenges may arise from _dataset bias_, _data quality_ issues, _unfairness_, and _misrepresentation_ in algorithms—including systemic issues.
 
@@ -126,7 +126,7 @@ Questions to explore:
 
 #### 2.6 Dataset Bias
 
-Dataset or [Collection Bias](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) involves selecting a _non-representative_ subset of data for algorithm development, potentially creating unfairness in outcomes for diverse groups. Types of bias include selection or sampling bias, volunteer bias, and instrument bias.
+Dataset or [Collection Bias](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) refers to selecting a _non-representative_ subset of data for algorithm development, potentially creating unfairness in outcomes for diverse groups. Types of bias include selection or sampling bias, volunteer bias, and instrument bias.
 
 Questions to explore:
 * Did we recruit a representative set of data subjects?
@@ -144,7 +144,7 @@ Questions to explore:
 * Is information captured _accurately_ to reflect reality?
 
 #### 2.8 Algorithm Fairness
-[Algorithm Fairness](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) examines whether the design of an algorithm systematically discriminates against specific subgroups of individuals, potentially causing [harm](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) in areas such as _allocation_ (where resources are denied or withheld from certain groups) and _quality of service_ (where AI performs less accurately for some subgroups compared to others).
+[Algorithm Fairness](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) examines whether the design of an algorithm systematically discriminates against specific subgroups of individuals, potentially causing [harm](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) in areas like _allocation_ (where resources are denied or withheld from certain groups) and _quality of service_ (where AI performs less accurately for some subgroups compared to others).
 
 Questions to consider:
  * Have we assessed the model's accuracy across diverse subgroups and conditions?
@@ -155,16 +155,16 @@ Explore resources like [AI Fairness checklists](https://query.prod.cms.rt.micros
 
 #### 2.9 Misrepresentation
 
-[Data Misrepresentation](https://www.sciencedirect.com/topics/computer-science/misrepresentation) involves questioning whether insights derived from data are being communicated in a misleading way to support a specific narrative.
+[Data Misrepresentation](https://www.sciencedirect.com/topics/computer-science/misrepresentation) involves questioning whether insights derived from data are being presented in a misleading way to support a specific narrative.
 
 Questions to consider:
- * Are we presenting incomplete or inaccurate data?
+ * Are we reporting incomplete or inaccurate data?
  * Are we visualizing data in ways that lead to false conclusions?
- * Are we selectively using statistical techniques to manipulate outcomes?
+ * Are we using selective statistical methods to manipulate outcomes?
  * Are there alternative explanations that could lead to different conclusions?
 
 #### 2.10 Free Choice
-The [Illusion of Free Choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) arises when decision-making algorithms in "choice architectures" subtly push users toward a preferred outcome while giving the appearance of options and control. These [dark patterns](https://www.darkpatterns.org/) can result in social and economic harm to users. Since user decisions influence behavior profiles, these actions can shape future choices, amplifying the impact of these harms.
+The [Illusion of Free Choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) arises when decision-making algorithms in "choice architectures" subtly push users toward a preferred outcome while giving the appearance of options and control. These [dark patterns](https://www.darkpatterns.org/) can result in social and economic harm to users. Since user decisions influence behavioral profiles, these choices can amplify or perpetuate the impact of such harms over time.
 
 Questions to consider:
  * Did the user fully understand the consequences of their choice?
@@ -179,28 +179,28 @@ Here are some examples:
 
 | Ethics Challenge | Case Study  | 
 |--- |--- |
-| **Informed Consent** | 1972 - [Tuskegee Syphilis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - African American men were promised free medical care but were deceived by researchers who did not inform them of their diagnosis or available treatment. Many participants died, and their families were affected. The study lasted 40 years. | 
+| **Informed Consent** | 1972 - [Tuskegee Syphilis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - African American men were promised free medical care but were deceived by researchers who withheld their diagnosis and treatment options. Many participants died, and their families were affected. The study lasted 40 years. | 
 | **Data Privacy** | 2007 - The [Netflix data prize](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) provided researchers with _10M anonymized movie ratings from 50K customers_ to improve recommendation algorithms. However, researchers were able to link anonymized data to personally identifiable information in _external datasets_ (e.g., IMDb comments), effectively "de-anonymizing" some Netflix users. |
 | **Collection Bias** | 2013 - The City of Boston [developed Street Bump](https://www.boston.gov/transportation/street-bump), an app for citizens to report potholes, helping the city collect better roadway data. However, [lower-income groups had less access to cars and smartphones](https://hbr.org/2013/04/the-hidden-biases-in-big-data), making their roadway issues invisible in the app. Developers collaborated with academics to address _equitable access and digital divide_ issues for fairness. |
-| **Algorithmic Fairness** | 2018 - The MIT [Gender Shades Study](http://gendershades.org/overview.html) evaluated the accuracy of gender classification AI products, revealing disparities in accuracy for women and people of color. A [2019 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) appeared to offer less credit to women than men, highlighting algorithmic bias and its socio-economic consequences. |
+| **Algorithmic Fairness** | 2018 - The MIT [Gender Shades Study](http://gendershades.org/overview.html) revealed accuracy gaps in gender classification AI products for women and people of color. A [2019 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) reportedly offered less credit to women than men, highlighting algorithmic bias and its socio-economic impacts. |
 | **Data Misrepresentation** | 2020 - The [Georgia Department of Public Health released COVID-19 charts](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) that misled citizens about case trends by using non-chronological ordering on the x-axis. This demonstrates misrepresentation through visualization techniques. |
-| **Illusion of free choice** | 2020 - Learning app [ABCmouse paid $10M to settle an FTC complaint](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) where parents were trapped into paying for subscriptions they couldn't cancel. This illustrates dark patterns in choice architectures, nudging users toward harmful decisions. |
-| **Data Privacy & User Rights** | 2021 - Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) exposed data from 530M users, resulting in a $5B settlement to the FTC. However, Facebook refused to notify users of the breach, violating their rights to data transparency and access. |
+| **Illusion of Free Choice** | 2020 - Learning app [ABCmouse paid $10M to settle an FTC complaint](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) where parents were trapped into paying for subscriptions they couldn't cancel. This illustrates dark patterns in choice architectures, nudging users toward harmful decisions. |
+| **Data Privacy & User Rights** | 2021 - Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) exposed data from 530M users, resulting in a $5B settlement to the FTC. Facebook refused to notify users of the breach, violating their rights to data transparency and access. |
 
 Want to explore more case studies? Check out these resources:
 * [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - ethics dilemmas across various industries. 
 * [Data Science Ethics course](https://www.coursera.org/learn/data-science-ethics#syllabus) - landmark case studies explored.
 * [Where things have gone wrong](https://deon.drivendata.org/examples/) - deon checklist with examples.
 
-> 🚨 Reflect on the case studies you've encountered—have you experienced or been affected by a similar ethical challenge? Can you think of another case study that illustrates one of the ethical challenges discussed here?
+> 🚨 Reflect on the case studies you've reviewed. Have you encountered or been affected by a similar ethical challenge in your life? Can you think of another case study that illustrates one of the ethical challenges discussed here?
 
 ## Applied Ethics
 
-We've explored ethical concepts, challenges, and case studies in real-world contexts. But how can we start _applying_ ethical principles and practices in our projects? And how can we _operationalize_ these practices for better governance? Let’s examine some practical solutions:
+We've explored ethical concepts, challenges, and case studies in real-world contexts. But how can we start _applying_ ethical principles and practices in our projects? And how can we _operationalize_ these practices for better governance? Let’s look at some practical solutions:
 
 ### 1. Professional Codes
 
-Professional Codes provide a way for organizations to "encourage" members to align with their ethical principles and mission statement. These codes act as _moral guidelines_ for professional behavior, helping employees or members make decisions consistent with their organization's values. Their effectiveness depends on voluntary compliance, but many organizations offer rewards or penalties to motivate adherence.
+Professional Codes provide a way for organizations to "encourage" members to align with their ethical principles and mission. These codes act as _moral guidelines_ for professional behavior, helping employees or members make decisions consistent with the organization's values. Their effectiveness depends on voluntary compliance, but many organizations offer rewards or penalties to motivate adherence.
 
 Examples include:
  * [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/) Code of Ethics
@@ -211,19 +211,19 @@ Examples include:
 
 ### 2. Ethics Checklists
 
-While professional codes define expected _ethical behavior_, they [have limitations](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) in enforcement, especially in large-scale projects. Many data science experts [recommend checklists](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) to **translate principles into actionable practices**.
+While professional codes define expected _ethical behavior_, they [have limitations](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) in enforcement, especially for large-scale projects. Many data science experts [recommend checklists](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) to **translate principles into actionable practices**.
 
 Checklists turn questions into "yes/no" tasks that can be integrated into standard workflows, making them easier to track during product development.
 
 Examples include:
  * [Deon](https://deon.drivendata.org/) - a general-purpose data ethics checklist based on [industry recommendations](https://deon.drivendata.org/#checklist-citations), with a command-line tool for easy integration.
- * [Privacy Audit Checklist](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - offers general guidance for handling information from legal and social perspectives.
+ * [Privacy Audit Checklist](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - offers general guidance on handling information from legal and social perspectives.
  * [AI Fairness Checklist](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - created by AI practitioners to integrate fairness checks into AI development cycles.
  * [22 questions for ethics in data and AI](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - an open-ended framework for exploring ethical issues in design, implementation, and organizational contexts.
 
 ### 3. Ethics Regulations
 
-Ethics involves defining shared values and voluntarily doing the right thing. **Compliance** focuses on _following the law_ where applicable. **Governance** encompasses all organizational efforts to enforce ethical principles and comply with legal requirements.
+Ethics involves defining shared values and voluntarily doing the right thing. **Compliance**, on the other hand, is about _following the law_ where applicable. **Governance** encompasses all organizational efforts to enforce ethical principles and comply with legal requirements.
 
 Governance today has two main aspects. First, it involves defining **ethical AI** principles and implementing practices to ensure adoption across all AI-related projects. Second, it requires compliance with government-mandated **data protection regulations** in the regions where the organization operates.
 
@@ -235,16 +235,16 @@ Examples of data protection and privacy regulations:
  * `2018`, [California Consumer Privacy Act (CCPA)](https://www.oag.ca.gov/privacy/ccpa) - grants consumers more control over their personal data.
  * `2021`, China's [Personal Information Protection Law](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/) - one of the strongest online data privacy regulations globally.
 
-> 🚨 The European Union's GDPR (General Data Protection Regulation) is one of the most influential data privacy regulations today. Did you know it also defines [8 user rights](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) to protect citizens' digital privacy and personal data? Learn about these rights and why they matter.
+> 🚨 The European Union's GDPR (General Data Protection Regulation) is one of the most influential data privacy regulations today. Did you know it also defines [8 user rights](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) to protect citizens' digital privacy and personal data? Learn about these rights and why they are important.
 
 ### 4. Ethics Culture
 
-There is often a gap between _compliance_ (meeting legal requirements) and addressing [systemic issues](https://www.coursera.org/learn/data-science-ethics/home/week/4) (such as ossification, information asymmetry, and distributional unfairness) that can accelerate the misuse of AI.
+There is often a gap between _compliance_ (meeting legal requirements) and addressing [systemic issues](https://www.coursera.org/learn/data-science-ethics/home/week/4) (like ossification, information asymmetry, and distributional unfairness) that can accelerate the misuse of AI.
 
-Addressing these issues requires [collaborative efforts to build ethics cultures](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that foster emotional connections and shared values across organizations and industries. This involves creating [formalized data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) within organizations, empowering _anyone_ to [raise concerns early](https://en.wikipedia.org/wiki/Andon_(manufacturing)) and making ethical considerations (e.g., in hiring) a core criterion for forming AI project teams.
+Addressing these issues requires [collaborative efforts to build ethics cultures](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that foster emotional connections and shared values across organizations and industries. This calls for more [formalized data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) within organizations, enabling _anyone_ to [raise concerns early](https://en.wikipedia.org/wiki/Andon_(manufacturing)) and making ethical considerations (e.g., in hiring) a core part of team formation for AI projects.
 
 ---
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) 🎯
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/3) 🎯
 ## Review & Self Study 
 
 Courses and books help build a foundation in ethics concepts and challenges, while case studies and tools provide practical insights into applying ethics in real-world scenarios. Here are some resources to get started:
diff --git a/translations/en/1-Introduction/03-defining-data/README.md b/translations/en/1-Introduction/03-defining-data/README.md
index 24ec55e3..e616655d 100644
--- a/translations/en/1-Introduction/03-defining-data/README.md
+++ b/translations/en/1-Introduction/03-defining-data/README.md
@@ -1,8 +1,8 @@
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+  "original_hash": "12339119c0165da569a93ddba05f9339",
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@@ -17,41 +17,41 @@ Data consists of facts, information, observations, and measurements that are use
 
 This lesson focuses on identifying and classifying data based on its characteristics and sources.
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/4)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/4)
 
 ## How Data is Described
 
 ### Raw Data
-Raw data refers to data in its original state, directly from its source, without any analysis or organization. To make sense of a dataset, it needs to be organized into a format that can be understood by humans and the technology used for further analysis. The structure of a dataset describes how it is organized and can be classified as structured, unstructured, or semi-structured. These structures vary depending on the source but generally fall into one of these three categories.
+Raw data refers to data in its original state, as it comes from its source, without any analysis or organization. To make sense of a dataset, it needs to be organized into a format that is understandable to both humans and the technology used for further analysis. The structure of a dataset describes how it is organized and can be classified as structured, unstructured, or semi-structured. These structural types vary depending on the source but ultimately fall into one of these three categories.
 
 ### Quantitative Data
-Quantitative data consists of numerical observations within a dataset that can typically be analyzed, measured, and used mathematically. Examples of quantitative data include a country's population, a person's height, or a company's quarterly earnings. With further analysis, quantitative data can be used to identify seasonal trends in the Air Quality Index (AQI) or estimate the likelihood of rush hour traffic on a typical workday.
+Quantitative data consists of numerical observations within a dataset that can typically be analyzed, measured, and used mathematically. Examples of quantitative data include a country's population, a person's height, or a company's quarterly earnings. With additional analysis, quantitative data can be used to uncover seasonal trends in the Air Quality Index (AQI) or estimate the likelihood of rush hour traffic on a typical workday.
 
 ### Qualitative Data
-Qualitative data, also known as categorical data, cannot be measured objectively like quantitative data. It often consists of subjective information that captures the quality of something, such as a product or process. Sometimes, qualitative data is numerical but not typically used mathematically, such as phone numbers or timestamps. Examples of qualitative data include video comments, the make and model of a car, or your closest friends' favorite color. Qualitative data can be used to understand which products consumers prefer or identify popular keywords in job application resumes.
+Qualitative data, also known as categorical data, cannot be measured objectively like quantitative data. It generally consists of subjective information that captures the quality of something, such as a product or process. Sometimes, qualitative data may include numbers that are not typically used mathematically, such as phone numbers or timestamps. Examples of qualitative data include video comments, the make and model of a car, or your closest friends' favorite colors. Qualitative data can be used to determine which products consumers prefer or to identify popular keywords in job application resumes.
 
 ### Structured Data
-Structured data is organized into rows and columns, where each row has the same set of columns. Columns represent specific types of values and are identified by names describing what the values represent, while rows contain the actual data. Columns often have rules or restrictions to ensure the values accurately represent the column. For example, imagine a spreadsheet of customers where each row must include a phone number, and phone numbers cannot contain alphabetical characters. Rules might be applied to ensure the phone number column is never empty and only contains numbers.
+Structured data is organized into rows and columns, where each row contains the same set of columns. Columns represent specific types of values and are identified by names that describe what the values represent, while rows contain the actual data. Columns often have specific rules or restrictions to ensure the values accurately represent the column. For example, imagine a spreadsheet of customers where each row must include a phone number, and the phone numbers cannot contain alphabetical characters. Rules might be applied to the phone number column to ensure it is never empty and only contains numbers.
 
-One advantage of structured data is that it can be organized in a way that relates to other structured data. However, because structured data is designed to follow a specific organization, making changes to its structure can require significant effort. For instance, adding an email column to the customer spreadsheet that cannot be empty would require figuring out how to populate this column for existing rows.
+One advantage of structured data is that it can be organized in a way that allows it to relate to other structured data. However, because the data is designed to follow a specific structure, making changes to its overall organization can require significant effort. For instance, adding an email column to the customer spreadsheet that cannot be empty would require determining how to populate this column for existing rows in the dataset.
 
 Examples of structured data: spreadsheets, relational databases, phone numbers, bank statements.
 
 ### Unstructured Data
-Unstructured data cannot typically be organized into rows or columns and lacks a defined format or set of rules. Because unstructured data has fewer restrictions, it is easier to add new information compared to structured datasets. For example, if a sensor measuring barometric pressure every two minutes receives an update to also record temperature, it wouldn't require altering the existing data if it's unstructured. However, analyzing or investigating unstructured data can take longer. For instance, a scientist trying to calculate the average temperature for the previous month might find that the sensor recorded an "e" in some entries to indicate it was broken, resulting in incomplete data.
+Unstructured data cannot typically be categorized into rows or columns and does not follow a specific format or set of rules. Because unstructured data has fewer restrictions, it is easier to add new information compared to structured datasets. For example, if a sensor that records barometric pressure every two minutes is updated to also measure temperature, no changes are needed to the existing data if it is unstructured. However, analyzing or investigating unstructured data may take longer. For instance, a scientist trying to calculate the average temperature for the previous month might find that the sensor recorded an "e" in some entries to indicate it was broken, resulting in incomplete data.
 
 Examples of unstructured data: text files, text messages, video files.
 
 ### Semi-structured Data
-Semi-structured data combines features of both structured and unstructured data. It doesn't typically conform to rows and columns but is organized in a way that is considered structured and may follow a fixed format or set of rules. The structure can vary between sources, ranging from a well-defined hierarchy to something more flexible that allows easy integration of new information. Metadata helps determine how the data is organized and stored, with various names depending on the type of data. Common names for metadata include tags, elements, entities, and attributes. For example, a typical email message includes a subject, body, and recipients, and can be organized by sender or date.
+Semi-structured data combines features of both structured and unstructured data. While it does not typically conform to a row-and-column format, it is organized in a way that is considered structured and may follow a fixed format or set of rules. The structure can vary between sources, ranging from a well-defined hierarchy to a more flexible format that allows for easy integration of new information. Metadata serves as indicators that help determine how the data is organized and stored, and it may have various names depending on the type of data. Common terms for metadata include tags, elements, entities, and attributes. For example, a typical email message includes a subject, body, and a set of recipients, and it can be organized by sender or date sent.
 
 Examples of semi-structured data: HTML, CSV files, JavaScript Object Notation (JSON).
 
 ## Sources of Data
 
-A data source refers to the original location where the data was generated or resides, and it varies based on how and when it was collected. Data generated by its user(s) is known as primary data, while secondary data comes from a source that has collected data for general use. For example, scientists collecting observations in a rainforest would be considered primary data, and if they share it with others, it becomes secondary data for those users.
+A data source refers to the original location where the data was generated or "lives," and it varies based on how and when the data was collected. Data generated by its user(s) is known as primary data, while secondary data comes from a source that has collected data for general use. For example, a group of scientists collecting observations in a rainforest would be considered primary data, and if they share it with other scientists, it would be considered secondary data for those who use it.
 
-Databases are a common data source and rely on a database management system to host and maintain the data. Users explore the data using commands called queries. Files can also serve as data sources, including audio, image, and video files, as well as spreadsheets like Excel. The internet is another common location for hosting data, where both databases and files can be found. Application programming interfaces (APIs) allow programmers to share data with external users over the internet, while web scraping extracts data from web pages. The [lessons in Working with Data](../../../../../../../../../2-Working-With-Data) focus on how to use various data sources.
+Databases are a common data source and rely on a database management system to host and maintain the data. Users interact with the data using commands called queries. Files can also serve as data sources, including audio, image, and video files, as well as spreadsheets like Excel. The internet is another common location for hosting data, where both databases and files can be found. Application programming interfaces (APIs) allow programmers to create ways to share data with external users over the internet, while web scraping involves extracting data from web pages. The [lessons in Working with Data](../../../../../../../../../2-Working-With-Data) focus on how to use various data sources.
 
 ## Conclusion
 
@@ -69,7 +69,7 @@ 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://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/5)
 
 ## Review & Self Study
 
diff --git a/translations/en/1-Introduction/04-stats-and-probability/README.md b/translations/en/1-Introduction/04-stats-and-probability/README.md
index b9b417aa..7da698e3 100644
--- a/translations/en/1-Introduction/04-stats-and-probability/README.md
+++ b/translations/en/1-Introduction/04-stats-and-probability/README.md
@@ -1,8 +1,8 @@
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+  "original_hash": "1cf49f029ba1f25a54f0d5bc2fa575fc",
+  "translation_date": "2025-09-06T10:12:05+00:00",
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@@ -13,51 +13,51 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 | Statistics and Probability - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-Statistics and Probability Theory are two closely related branches of Mathematics that are highly relevant to Data Science. While you can work with data without a deep understanding of mathematics, it's still beneficial to grasp some fundamental concepts. This introduction will help you get started.
+Statistics and Probability Theory are two closely related branches of Mathematics that are highly relevant to Data Science. While you can work with data without a deep understanding of mathematics, it's still beneficial to grasp some basic concepts. Here, we'll provide a brief introduction to help you get started.
 
-[![Intro Video](../../../../1-Introduction/04-stats-and-probability/images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw)
+[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.en.png)](https://youtu.be/Z5Zy85g4Yjw)
 
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/6)
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/6)
 
 ## Probability and Random Variables
 
-**Probability** is a value between 0 and 1 that represents how likely an **event** is to occur. It is calculated as the number of favorable outcomes (leading to the event) divided by the total number of possible outcomes, assuming all outcomes are equally likely. For instance, when rolling a die, the probability of getting an even number is 3/6 = 0.5.
+**Probability** is a number between 0 and 1 that represents how likely an **event** is to occur. It is calculated as the number of favorable outcomes (leading to the event) divided by the total number of possible outcomes, assuming all outcomes are equally likely. For example, when rolling a die, the probability of getting an even number is 3/6 = 0.5.
 
-When discussing events, we use **random variables**. For example, the random variable representing the number rolled on a die can take values from 1 to 6. This set of numbers (1 to 6) is called the **sample space**. We can calculate the probability of a random variable taking a specific value, such as P(X=3)=1/6.
+When discussing events, we use **random variables**. For instance, the random variable representing the number rolled on a die can take values from 1 to 6. The set of numbers from 1 to 6 is called the **sample space**. We can talk about the probability of a random variable taking a specific value, such as P(X=3)=1/6.
 
-The random variable in this example is called **discrete** because its sample space consists of countable values that can be listed. In other cases, the sample space might be a range of real numbers or the entire set of real numbers. Such variables are called **continuous**. A good example is the time a bus arrives.
+The random variable in the example above is called **discrete** because its sample space consists of countable values that can be listed. In other cases, the sample space might be a range of real numbers or the entire set of real numbers. Such variables are called **continuous**. A good example is the time a bus arrives.
 
 ## Probability Distribution
 
-For discrete random variables, it's straightforward to describe the probability of each event using a function P(X). For every value *s* in the sample space *S*, the function assigns a number between 0 and 1, ensuring that the sum of all P(X=s) values equals 1.
+For discrete random variables, it's straightforward to describe the probability of each event using a function P(X). For each value *s* in the sample space *S*, the function assigns a number between 0 and 1, ensuring that the sum of all P(X=s) values equals 1.
 
-The most common discrete distribution is the **uniform distribution**, where the sample space contains N elements, each with an equal probability of 1/N.
+The most well-known discrete distribution is the **uniform distribution**, where the sample space consists of N elements, each with an equal probability of 1/N.
 
-Describing the probability distribution of a continuous variable, such as values within an interval [a,b] or the entire set of real numbers ℝ, is more complex. Consider the bus arrival time example. The probability of the bus arriving at an exact time *t* is actually 0!
+Describing the probability distribution of a continuous variable, with values drawn from an interval [a,b] or the entire set of real numbers ℝ, is more complex. Consider the case of bus arrival times. For any exact arrival time *t*, the probability of the bus arriving at that precise moment is actually 0!
 
-> Now you know that events with 0 probability can still happen—and quite often! For example, every time the bus arrives!
+> Now you know that events with 0 probability can happen—and they happen quite often! For example, every time the bus arrives!
 
-Instead, we discuss the probability of a variable falling within a range of values, such as P(t<sub>1</sub>≤X<t<sub>2</sub>). In this case, the probability distribution is described by a **probability density function** p(x), such that:
+Instead, we talk about the probability of a variable falling within a range of values, such as P(t<sub>1</sub>≤X<t<sub>2</sub>). In this case, the probability distribution is described by a **probability density function** p(x), such that:
 
-![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../1-Introduction/04-stats-and-probability/images/probability-density.png)
+![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/probability-density.a8aad29f17a14afb519b407c7b6edeb9f3f9aa5f69c9e6d9445f604e5f8a2bf7.en.png)
 
-A continuous version of the uniform distribution is called **continuous uniform distribution**, defined over a finite interval. The probability of X falling within an interval of length l is proportional to l and reaches up to 1.
+A continuous analog of the uniform distribution is called **continuous uniform distribution**, which is defined over a finite interval. The probability that the value X falls within an interval of length l is proportional to l and increases up to 1.
 
-Another key distribution is the **normal distribution**, which we'll explore in more detail later.
+Another important distribution is the **normal distribution**, which we'll discuss in more detail later.
 
 ## Mean, Variance, and Standard Deviation
 
-Suppose we take a sequence of n samples from a random variable X: x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>n</sub>. The **mean** (or **arithmetic average**) of the sequence is calculated as (x<sub>1</sub>+x<sub>2</sub>+...+x<sub>n</sub>)/n. As the sample size increases (n→∞), the mean approaches the **expectation** of the distribution, denoted as **E**(x).
+Suppose we draw a sequence of n samples from a random variable X: x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>n</sub>. The **mean** (or **arithmetic average**) of the sequence is calculated as (x<sub>1</sub>+x<sub>2</sub>+...+x<sub>n</sub>)/n. As the sample size grows (i.e., as n approaches infinity), the mean converges to the **expectation** of the distribution, denoted as **E**(x).
 
 > For any discrete distribution with values {x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>N</sub>} and corresponding probabilities p<sub>1</sub>, p<sub>2</sub>, ..., p<sub>N</sub>, the expectation is given by E(X)=x<sub>1</sub>p<sub>1</sub>+x<sub>2</sub>p<sub>2</sub>+...+x<sub>N</sub>p<sub>N</sub>.
 
-To measure how spread out the values are, we calculate the variance σ<sup>2</sup> = ∑(x<sub>i</sub> - μ)<sup>2</sup>/n, where μ is the mean of the sequence. The square root of the variance, σ, is called the **standard deviation**, while σ<sup>2</sup> is the **variance**.
+To measure how spread out the values are, we calculate the variance σ<sup>2</sup> = ∑(x<sub>i</sub> - μ)<sup>2</sup>/n, where μ is the mean of the sequence. The square root of the variance, σ, is called the **standard deviation**, and σ<sup>2</sup> is the **variance**.
 
 ## Mode, Median, and Quartiles
 
-Sometimes, the mean doesn't accurately represent the "typical" value of the data, especially when there are extreme values that skew the average. A better indicator might be the **median**, the value that divides the data into two equal halves—one half below and the other above.
+Sometimes, the mean doesn't adequately represent the "typical" value of the data. For example, extreme values can skew the mean. A better indicator in such cases is the **median**, which is the value that divides the data into two halves—one half below it and the other half above it.
 
-To further understand data distribution, we use **quartiles**:
+To better understand the distribution of data, we use **quartiles**:
 
 * The first quartile (Q1) is the value below which 25% of the data falls.
 * The third quartile (Q3) is the value below which 75% of the data falls.
@@ -66,72 +66,77 @@ The relationship between the median and quartiles can be visualized using a **bo
 
 <img src="images/boxplot_explanation.png" width="50%"/>
 
-We also calculate the **inter-quartile range** (IQR=Q3-Q1) and identify **outliers**—values outside the range [Q1-1.5*IQR, Q3+1.5*IQR].
+We also calculate the **interquartile range** (IQR=Q3-Q1) and identify **outliers**—values that fall outside the range [Q1-1.5*IQR, Q3+1.5*IQR].
 
-For small, finite distributions, the most frequent value is a good "typical" value, called the **mode**. Mode is often used for categorical data, such as colors. For instance, if two groups of people strongly prefer red and blue, the mean color might fall somewhere in the orange-green spectrum, which doesn't reflect either group's preference. However, the mode would correctly identify the most popular colors. If two colors are equally popular, the sample is called **multimodal**.
+For distributions with a small number of possible values, the most frequent value is a good "typical" value, called the **mode**. Mode is often used for categorical data, such as colors. For example, if two groups of people strongly prefer red and blue, the mean of their preferences might fall somewhere in the orange-green spectrum, which doesn't represent either group's preference. However, the mode would correctly identify the most popular colors. If two colors are equally popular, the sample is called **multimodal**.
 
 ## Real-world Data
 
-When analyzing real-world data, it often doesn't behave like random variables in the strict sense, as we aren't conducting experiments with unknown outcomes. For example, consider a baseball team and their physical attributes like height, weight, and age. These numbers aren't truly random, but we can still apply mathematical concepts. For instance, a sequence of players' weights can be treated as values drawn from a random variable. Below is a sequence of weights from actual baseball players in [Major League Baseball](http://mlb.mlb.com/index.jsp), sourced from [this dataset](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) (only the first 20 values are shown):
+When analyzing real-world data, the values often aren't random variables in the strict sense, as they don't result from experiments with unknown outcomes. For example, consider a baseball team and their physical attributes like height, weight, and age. These numbers aren't truly random, but we can still apply mathematical concepts. For instance, the sequence of players' weights can be treated as values drawn from a random variable. Below is the sequence of weights for actual baseball players from [Major League Baseball](http://mlb.mlb.com/index.jsp), taken from [this dataset](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) (only the first 20 values are shown for convenience):
 
 ```
 [180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]
 ```
 
-> **Note**: To see an example of working with this dataset, check out the [accompanying notebook](../../../../1-Introduction/04-stats-and-probability/notebook.ipynb). There are also challenges throughout this lesson that you can complete by adding code to the notebook. If you're unsure how to work with data, don't worry—we'll revisit data manipulation using Python later. If you don't know how to run code in Jupyter Notebook, refer to [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
+> **Note**: To see an example of working with this dataset, check out the [accompanying notebook](notebook.ipynb). There are also challenges throughout this lesson that you can complete by adding code to the notebook. If you're unsure how to work with data, don't worry—we'll revisit data manipulation using Python later. If you don't know how to run code in Jupyter Notebook, check out [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
 
-Here is a box plot showing the mean, median, and quartiles for the data:
+Here's a box plot showing the mean, median, and quartiles for the data:
 
-![Weight Box Plot](../../../../1-Introduction/04-stats-and-probability/images/weight-boxplot.png)
+![Weight Box Plot](../../../../translated_images/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.en.png)
 
-Since the dataset includes information about different player **roles**, we can create box plots by role to see how parameters vary across roles. This time, we'll consider height:
+Since the data includes information about different player **roles**, we can create box plots by role to see how the values differ across roles. This time, we'll consider height:
 
-![Box plot by role](../../../../1-Introduction/04-stats-and-probability/images/boxplot_byrole.png)
+![Box plot by role](../../../../translated_images/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.en.png)
 
 This diagram suggests that, on average, first basemen are taller than second basemen. Later in this lesson, we'll learn how to formally test this hypothesis and demonstrate statistical significance.
 
 > When working with real-world data, we assume that all data points are samples drawn from a probability distribution. This assumption allows us to apply machine learning techniques and build predictive models.
 
-To visualize the distribution of the data, we can create a **histogram**. The X-axis represents weight intervals (or **bins**), while the Y-axis shows the frequency of samples within each interval.
+To visualize the distribution of the data, we can create a **histogram**. The x-axis represents weight intervals (or **bins**), and the y-axis shows the frequency of values within each interval.
 
-![Histogram of real-world data](../../../../1-Introduction/04-stats-and-probability/images/weight-histogram.png)
+![Histogram of real world data](../../../../translated_images/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.en.png)
 
-From this histogram, you can see that most values cluster around a certain mean weight, with fewer values appearing as we move further from the mean. This indicates that extreme weights are less likely. The variance shows how much weights deviate from the mean.
+From this histogram, you can see that most values cluster around a certain mean weight, and the further you move from the mean, the less frequent those values become. This indicates that extreme weights are less likely. The variance shows how much the weights deviate from the mean.
 
-> If we analyze weights of people outside the baseball league, the distribution might differ. However, the general shape of the distribution would remain similar, with changes in mean and variance. Training a model on baseball players might yield inaccurate results when applied to university students, as the underlying distribution differs.
+> If we analyzed weights of people outside the baseball league, the distribution might differ. However, the general shape of the distribution would remain the same, with changes to the mean and variance. Training a model on baseball players might yield inaccurate results when applied to university students, as the underlying distribution differs.
 
 ## Normal Distribution
 
-The weight distribution we observed above is very common, and many real-world measurements follow a similar pattern, albeit with different means and variances. This pattern is called the **normal distribution**, which plays a crucial role in statistics.
+The weight distribution we observed above is very common, and many real-world measurements follow the same type of distribution, albeit with different means and variances. This distribution is called the **normal distribution**, and it plays a crucial role in statistics.
 
 Using a normal distribution is a valid way to generate random weights for potential baseball players. Once we know the mean weight `mean` and standard deviation `std`, we can generate 1000 weight samples as follows:
+
 ```python
 samples = np.random.normal(mean,std,1000)
 ``` 
 
-If we plot a histogram of the generated samples, it will resemble the earlier example. Increasing the number of samples and bins will produce a graph closer to the ideal normal distribution:
+If we plot a histogram of the generated samples, it will resemble the one shown earlier. Increasing the number of samples and bins will produce a graph closer to the ideal normal distribution:
 
-![Normal Distribution with mean=0 and std.dev=1](../../../../1-Introduction/04-stats-and-probability/images/normal-histogram.png)
+![Normal Distribution with mean=0 and std.dev=1](../../../../translated_images/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.en.png)
 
 *Normal Distribution with mean=0 and std.dev=1*
 
 ## Confidence Intervals
 
-When discussing baseball players' weights, we assume there is a **random variable W** representing the ideal probability distribution of weights for all players (the **population**). Our sequence of weights represents a subset of players, called a **sample**. A key question is whether we can determine the parameters of W's distribution, such as the population mean and variance.
+When discussing baseball players' weights, we assume there is a **random variable W** representing the ideal probability distribution of weights for all baseball players (the **population**). Our sequence of weights corresponds to a subset of players, called a **sample**. A key question is whether we can determine the parameters of W's distribution, such as the population mean and variance.
 
 The simplest approach is to calculate the sample's mean and variance. However, the sample might not perfectly represent the population. This is where **confidence intervals** come into play.
-> **Confidence interval** is the estimation of the true mean of the population based on our sample, with a certain probability (or **level of confidence**) of being accurate.
-Suppose we have a sample X<sub>1</sub>, ..., X<sub>n</sub> from our distribution. Each time we draw a sample from our distribution, we would end up with a different mean value μ. Thus, μ can be considered a random variable. A **confidence interval** with confidence p is a pair of values (L<sub>p</sub>, R<sub>p</sub>), such that **P**(L<sub>p</sub> ≤ μ ≤ R<sub>p</sub>) = p, meaning the probability of the measured mean value falling within the interval equals p.
 
-It goes beyond the scope of this short introduction to discuss in detail how these confidence intervals are calculated. More details can be found [on Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). In short, we define the distribution of the computed sample mean relative to the true mean of the population, which is called the **Student distribution**.
+> **Confidence interval** refers to the range within which the true population mean is likely to fall, with a certain probability (or **level of confidence**).
+
+Suppose we have a sample X...
+<sub>
+1<sub>1</sub>, ..., X<sub>n</sub> from our distribution. Each time we draw a sample from our distribution, we would end up with a different mean value μ. Thus, μ can be considered a random variable. A **confidence interval** with confidence p is a pair of values (L<sub>p</sub>, R<sub>p</sub>), such that **P**(L<sub>p</sub> ≤ μ ≤ R<sub>p</sub>) = p, meaning the probability of the measured mean value falling within the interval equals p.
 
-> **Interesting fact**: The Student distribution is named after mathematician William Sealy Gosset, who published his paper under the pseudonym "Student." He worked at the Guinness brewery, and, according to one version, his employer did not want the general public to know they were using statistical tests to determine the quality of raw materials.
+It goes beyond the scope of this brief introduction to discuss in detail how these confidence intervals are calculated. More information can be found [on Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). In short, we define the distribution of the computed sample mean relative to the true mean of the population, which is called the **Student distribution**.
+
+> **Interesting fact**: The Student distribution is named after mathematician William Sealy Gosset, who published his paper under the pseudonym "Student." He worked at the Guinness brewery, and, according to one version, his employer did not want the public to know they were using statistical tests to assess the quality of raw materials.
 
 If we want to estimate the mean μ of our population with confidence p, we need to take the *(1-p)/2-th percentile* of a Student distribution A, which can either be taken from tables or computed using built-in functions in statistical software (e.g., Python, R, etc.). Then the interval for μ would be given by X ± A * D / √n, where X is the obtained mean of the sample, and D is the standard deviation.
 
-> **Note**: We also omit the discussion of an important concept called [degrees of freedom](https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)), which is relevant to the Student distribution. You can refer to more comprehensive books on statistics to understand this concept in depth.
+> **Note**: We also omit the discussion of an important concept called [degrees of freedom](https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)), which is relevant to the Student distribution. You can refer to more comprehensive books on statistics to explore this concept further.
 
-An example of calculating confidence intervals for weights and heights is provided in the [accompanying notebooks](../../../../1-Introduction/04-stats-and-probability/notebook.ipynb).
+An example of calculating confidence intervals for weights and heights is provided in the [accompanying notebooks](notebook.ipynb).
 
 | p    | Weight mean   |
 |------|---------------|
@@ -143,7 +148,7 @@ Notice that the higher the confidence probability, the wider the confidence inte
 
 ## Hypothesis Testing
 
-In our baseball players dataset, there are different player roles, summarized below (refer to the [accompanying notebook](../../../../1-Introduction/04-stats-and-probability/notebook.ipynb) to see how this table is calculated):
+In our baseball players dataset, there are different player roles, summarized below (refer to the [accompanying notebook](notebook.ipynb) to see how this table is calculated):
 
 | Role              | Height     | Weight     | Count |
 |-------------------|------------|------------|-------|
@@ -157,13 +162,13 @@ In our baseball players dataset, there are different player roles, summarized be
 | Starting_Pitcher  | 74.719457  | 205.163636 | 221   |
 | Third_Baseman     | 73.044444  | 200.955556 | 45    |
 
-We can observe that the mean height of first basemen is higher than that of second basemen. Thus, we might be tempted to conclude that **first basemen are taller than second basemen**.
+We can observe that the mean height of first basemen is greater than that of second basemen. This might lead us to conclude that **first basemen are taller than second basemen**.
 
-> This statement is called **a hypothesis**, because we do not know whether the fact is actually true or not.
+> This statement is called **a hypothesis**, as we do not know whether the fact is actually true or not.
 
-However, it is not always clear whether we can make this conclusion. From the discussion above, we know that each mean has an associated confidence interval, and this difference could simply be a statistical error. We need a more formal way to test our hypothesis.
+However, it is not always clear whether we can make this conclusion. From the discussion above, we know that each mean has an associated confidence interval, and this difference could simply be a statistical error. We need a more formal method to test our hypothesis.
 
-Let's compute confidence intervals separately for the heights of first and second basemen:
+Let’s compute confidence intervals separately for the heights of first and second basemen:
 
 | Confidence | First Basemen   | Second Basemen  |
 |------------|-----------------|-----------------|
@@ -175,11 +180,11 @@ We can see that under no confidence level do the intervals overlap. This support
 
 More formally, the problem we are solving is to determine whether **two probability distributions are the same**, or at least have the same parameters. Depending on the distribution, different tests are required. If we know our distributions are normal, we can apply the **[Student t-test](https://en.wikipedia.org/wiki/Student%27s_t-test)**.
 
-In the Student t-test, we compute the so-called **t-value**, which indicates the difference between means while accounting for variance. It has been demonstrated that the t-value follows the **Student distribution**, which allows us to find the threshold value for a given confidence level **p** (this can be computed or looked up in numerical tables). We then compare the t-value to this threshold to accept or reject the hypothesis.
+In the Student t-test, we compute the **t-value**, which measures the difference between means while accounting for variance. It has been shown that the t-value follows the **Student distribution**, allowing us to determine the threshold value for a given confidence level **p** (this can be computed or looked up in numerical tables). We then compare the t-value to this threshold to accept or reject the hypothesis.
 
-In Python, we can use the **SciPy** package, which includes the `ttest_ind` function (along with many other useful statistical functions!). This function computes the t-value for us and also performs the reverse lookup of the confidence p-value, allowing us to simply examine the confidence level to draw conclusions.
+In Python, we can use the **SciPy** package, which includes the `ttest_ind` function (along with many other useful statistical functions!). This function computes the t-value for us and performs the reverse lookup of the confidence p-value, enabling us to draw conclusions directly from the confidence level.
 
-For example, our comparison between the heights of first and second basemen yields the following results:
+For example, comparing the heights of first and second basemen yields the following results:
 ```python
 from scipy.stats import ttest_ind
 
@@ -192,28 +197,28 @@ P-value: 9.137321189738925e-12
 ```
 In this case, the p-value is very low, indicating strong evidence that first basemen are taller.
 
-There are also other types of hypotheses we might want to test, such as:
-* Proving that a given sample follows a specific distribution. In our case, we assumed that heights are normally distributed, but this requires formal statistical verification.
-* Proving that the mean value of a sample corresponds to a predefined value.
-* Comparing the means of multiple samples (e.g., differences in happiness levels across different age groups).
+There are other types of hypotheses we might want to test, such as:
+* Verifying that a given sample follows a specific distribution. For instance, we assumed that heights are normally distributed, but this requires formal statistical validation.
+* Testing whether the mean value of a sample corresponds to a predefined value.
+* Comparing the means of multiple samples (e.g., differences in happiness levels across age groups).
 
 ## Law of Large Numbers and Central Limit Theorem
 
-One reason why the normal distribution is so important is the **central limit theorem**. Suppose we have a large sample of independent N values X<sub>1</sub>, ..., X<sub>N</sub>, drawn from any distribution with mean μ and variance σ<sup>2</sup>. Then, for sufficiently large N (in other words, as N→∞), the mean Σ<sub>i</sub>X<sub>i</sub> will be normally distributed, with mean μ and variance σ<sup>2</sup>/N.
+One reason the normal distribution is so important is the **central limit theorem**. Suppose we have a large sample of independent N values X<sub>1</sub>, ..., X<sub>N</sub>, drawn from any distribution with mean μ and variance σ<sup>2</sup>. Then, for sufficiently large N (i.e., as N→∞), the mean Σ<sub>i</sub>X<sub>i</sub> will be normally distributed, with mean μ and variance σ<sup>2</sup>/N.
 
-> Another way to interpret the central limit theorem is to say that regardless of the original distribution, when you compute the mean of a sum of random variable values, you end up with a normal distribution.
+> Another interpretation of the central limit theorem is that regardless of the original distribution, the mean of a sum of random variable values will follow a normal distribution.
 
-From the central limit theorem, it also follows that as N→∞, the probability of the sample mean equaling μ approaches 1. This is known as **the law of large numbers**.
+The central limit theorem also implies that as N→∞, the probability of the sample mean equaling μ approaches 1. This is known as the **law of large numbers**.
 
 ## Covariance and Correlation
 
-One of the tasks in Data Science is identifying relationships between data. We say two sequences **correlate** when they exhibit similar behavior at the same time, i.e., they either rise/fall simultaneously, or one rises while the other falls, and vice versa. In other words, there appears to be some relationship between the two sequences.
+One of the goals of data science is to identify relationships between data. Two sequences are said to **correlate** when they exhibit similar behavior simultaneously—either rising/falling together or one rising while the other falls, and vice versa. In other words, there appears to be some connection between the two sequences.
 
-> Correlation does not necessarily imply a causal relationship between two sequences; sometimes both variables depend on an external cause, or the correlation may occur purely by chance. However, strong mathematical correlation is a good indication that two variables are somehow connected.
+> Correlation does not necessarily imply a causal relationship between two sequences. Sometimes both variables depend on an external factor, or the correlation may occur purely by chance. However, strong mathematical correlation is a good indicator that two variables are somehow related.
 
-Mathematically, the main concept that shows the relationship between two random variables is **covariance**, computed as: Cov(X, Y) = **E**\[(X - **E**(X))(Y - **E**(Y))\]. We calculate the deviation of both variables from their mean values and then take the product of those deviations. If both variables deviate together, the product will always be positive, resulting in positive covariance. If the variables deviate out of sync (i.e., one falls below average while the other rises above average), the product will always be negative, resulting in negative covariance. If the deviations are independent, they will sum to roughly zero.
+Mathematically, the concept that quantifies the relationship between two random variables is **covariance**, calculated as Cov(X, Y) = **E**\[(X - **E**(X))(Y - **E**(Y))\]. This involves computing the deviation of both variables from their mean values and then multiplying these deviations. If both variables deviate together, the product will always be positive, resulting in positive covariance. If the variables deviate out of sync (e.g., one falls below average while the other rises above average), the product will be negative, leading to negative covariance. If the deviations are independent, the covariance will be approximately zero.
 
-The absolute value of covariance does not provide much insight into the strength of the correlation, as it depends on the magnitude of the values. To normalize it, we divide covariance by the standard deviation of both variables to obtain **correlation**. The advantage of correlation is that it always falls within the range [-1, 1], where 1 indicates strong positive correlation, -1 indicates strong negative correlation, and 0 indicates no correlation (variables are independent).
+The absolute value of covariance does not indicate the strength of the correlation, as it depends on the magnitude of the values. To normalize it, we divide the covariance by the standard deviation of both variables, yielding **correlation**. Correlation always falls within the range [-1, 1], where 1 indicates strong positive correlation, -1 indicates strong negative correlation, and 0 indicates no correlation (independence).
 
 **Example**: We can compute the correlation between the weights and heights of baseball players from the dataset mentioned earlier:
 ```python
@@ -225,40 +230,39 @@ array([[1.        , 0.52959196],
        [0.52959196, 1.        ]])
 ```
 
-> A correlation matrix C can be computed for any number of input sequences S<sub>1</sub>, ..., S<sub>n</sub>. The value of C<sub>ij</sub> represents the correlation between S<sub>i</sub> and S<sub>j</sub>, and the diagonal elements are always 1 (self-correlation of S<sub>i</sub>).
+> A correlation matrix C can be computed for any number of input sequences S<sub>1</sub>, ..., S<sub>n</sub>. The value of C<sub>ij</sub> represents the correlation between S<sub>i</sub> and S<sub>j</sub>, and diagonal elements are always 1 (self-correlation of S<sub>i</sub>).
 
-In our case, the value 0.53 indicates some correlation between a person's weight and height. We can also create a scatter plot of one variable against the other to visualize the relationship:
+In this case, the value 0.53 indicates some correlation between a person’s weight and height. We can also create a scatter plot to visualize the relationship:
 
-![Relationship between weight and height](../../../../1-Introduction/04-stats-and-probability/images/weight-height-relationship.png)
+![Relationship between weight and height](../../../../translated_images/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.en.png)
 
-> More examples of correlation and covariance can be found in the [accompanying notebook](../../../../1-Introduction/04-stats-and-probability/notebook.ipynb).
+> More examples of correlation and covariance can be found in the [accompanying notebook](notebook.ipynb).
 
 ## Conclusion
 
 In this section, we have learned:
-
 * Basic statistical properties of data, such as mean, variance, mode, and quartiles.
 * Different distributions of random variables, including the normal distribution.
-* How to find correlations between different properties.
-* How to use mathematical and statistical tools to test hypotheses.
+* How to identify correlations between different properties.
+* How to use mathematical and statistical tools to validate hypotheses.
 * How to compute confidence intervals for random variables based on data samples.
 
-While this is not an exhaustive list of topics within probability and statistics, it should provide a solid foundation for this course.
+While this is not an exhaustive list of topics in probability and statistics, it provides a solid foundation for this course.
 
 ## 🚀 Challenge
 
-Use the sample code in the notebook to test other hypotheses:
+Use the sample code in the notebook to test the following hypotheses:
 1. First basemen are older than second basemen.
 2. First basemen are taller than third basemen.
 3. Shortstops are taller than second basemen.
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/7)
 
 ## Review & Self Study
 
-Probability and statistics is a broad topic that deserves its own course. If you want to explore the theory further, consider reading the following books:
+Probability and statistics is a vast field that warrants its own course. If you wish to delve deeper into the theory, consider exploring the following resources:
 
-1. [Carlos Fernandez-Granda](https://cims.nyu.edu/~cfgranda/) from New York University has excellent lecture notes: [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf) (available online).
+1. [Carlos Fernandez-Granda](https://cims.nyu.edu/~cfgranda/) from New York University offers excellent lecture notes: [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf) (available online).
 2. [Peter and Andrew Bruce. Practical Statistics for Data Scientists.](https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/) [[Sample code in R](https://github.com/andrewgbruce/statistics-for-data-scientists)].
 3. [James D. Miller. Statistics for Data Science](https://www.packtpub.com/product/statistics-for-data-science/9781788290678) [[Sample code in R](https://github.com/PacktPublishing/Statistics-for-Data-Science)].
 
@@ -268,9 +272,9 @@ Probability and statistics is a broad topic that deserves its own course. If you
 
 ## Credits
 
-This lesson was authored with ♥️ by [Dmitry Soshnikov](http://soshnikov.com).
+This lesson was created with ♥️ by [Dmitry Soshnikov](http://soshnikov.com).
 
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/2-Working-With-Data/05-relational-databases/README.md b/translations/en/2-Working-With-Data/05-relational-databases/README.md
index c2f47961..3803c848 100644
--- a/translations/en/2-Working-With-Data/05-relational-databases/README.md
+++ b/translations/en/2-Working-With-Data/05-relational-databases/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "11b166fbcb7eaf82308cdc24b562f687",
-  "translation_date": "2025-09-05T07:39:59+00:00",
+  "original_hash": "9399d7b4767e75068f95ce5c660b285c",
+  "translation_date": "2025-09-06T10:06:30+00:00",
   "source_file": "2-Working-With-Data/05-relational-databases/README.md",
   "language_code": "en"
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@@ -15,13 +15,13 @@ CO_OP_TRANSLATOR_METADATA:
 
 Chances are you’ve used a spreadsheet before to store information. You had rows and columns, where the rows contained the data, and the columns described the data (sometimes called metadata). A relational database is based on this same principle of rows and columns in tables, but it allows you to spread information across multiple tables. This makes it possible to work with more complex data, avoid duplication, and have more flexibility in exploring the data. Let’s dive into the concepts of relational databases.
 
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/8)
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/8)
 
 ## It all starts with tables
 
-At the heart of a relational database are tables. Similar to a spreadsheet, a table is a collection of rows and columns. The rows contain the data you want to work with, such as the name of a city or the amount of rainfall, while the columns describe the type of data stored.
+At the heart of a relational database are tables. Just like a spreadsheet, a table is a collection of rows and columns. The rows contain the data you want to work with, such as the name of a city or the amount of rainfall. The columns describe the type of data stored.
 
-Let’s start by creating a table to store information about cities. We might begin with their name and country. You could organize this in a table like this:
+Let’s start by creating a table to store information about cities. We might begin with their name and country. You could store this in a table like this:
 
 | City     | Country       |
 | -------- | ------------- |
@@ -29,11 +29,11 @@ Let’s start by creating a table to store information about cities. We might be
 | Atlanta  | United States |
 | Auckland | New Zealand   |
 
-Notice how the column names **city**, **country**, and **population** describe the data being stored, and each row contains information about one city.
+Notice that the column names **city**, **country**, and **population** describe the data being stored, and each row contains information about one city.
 
 ## The shortcomings of a single table approach
 
-The table above might look familiar to you. Now let’s add more data to our growing database—annual rainfall (in millimeters). We’ll focus on the years 2018, 2019, and 2020. If we were to add this data for Tokyo, it might look like this:
+The table above probably looks familiar to you. Now let’s add more data to our growing database—annual rainfall (in millimeters). We’ll focus on the years 2018, 2019, and 2020. If we were to add this for Tokyo, it might look like this:
 
 | City  | Country | Year | Amount |
 | ----- | ------- | ---- | ------ |
@@ -43,7 +43,7 @@ The table above might look familiar to you. Now let’s add more data to our gro
 
 What do you notice about this table? You might see that we’re repeating the name and country of the city multiple times. This could take up a lot of storage and is unnecessary since Tokyo only has one name and country.
 
-Let’s try another approach. We’ll add new columns for each year:
+Let’s try another approach. Let’s add new columns for each year:
 
 | City     | Country       | 2018 | 2019 | 2020 |
 | -------- | ------------- | ---- | ---- | ---- |
@@ -51,13 +51,13 @@ Let’s try another approach. We’ll add new columns for each year:
 | Atlanta  | United States | 1779 | 1111 | 1683 |
 | Auckland | New Zealand   | 1386 | 942  | 1176 |
 
-While this avoids repeating rows, it introduces other challenges. We’d need to change the table structure every time a new year is added. Additionally, as the data grows, having years as columns would make it harder to retrieve and calculate values.
+While this avoids repeating rows, it introduces other challenges. We’d need to change the structure of the table every time a new year is added. Additionally, as the data grows, having years as columns would make it harder to retrieve and calculate values.
 
 This is why we need multiple tables and relationships. By splitting the data into separate tables, we can avoid duplication and gain more flexibility in working with the data.
 
 ## The concepts of relationships
 
-Let’s revisit our data and decide how to divide it. We know we want to store the name and country of each city, so this will work best in one table.
+Let’s revisit our data and decide how to split it up. We know we want to store the name and country of each city, so this will work best in one table.
 
 | City     | Country       |
 | -------- | ------------- |
@@ -65,7 +65,7 @@ Let’s revisit our data and decide how to divide it. We know we want to store t
 | Atlanta  | United States |
 | Auckland | New Zealand   |
 
-Before creating the next table, we need a way to reference each city. We need an identifier, ID, or (in database terms) a primary key. A primary key is a unique value used to identify a specific row in a table. While it could be based on an existing value (like the city name), it’s better to use a number or other identifier that won’t change. If the ID changes, it would break the relationship. In most cases, the primary key or ID is an auto-generated number.
+Before creating the next table, we need a way to reference each city. We need an identifier, ID, or (in database terms) a primary key. A primary key is a value used to uniquely identify a specific row in a table. While this could be based on an existing value (like the city name), it’s better to use a number or other identifier that won’t change. If the ID changes, it would break the relationship. In most cases, the primary key or ID is an auto-generated number.
 
 > ✅ Primary key is often abbreviated as PK
 
@@ -79,7 +79,7 @@ Before creating the next table, we need a way to reference each city. We need an
 
 > ✅ You’ll notice we use the terms "id" and "primary key" interchangeably in this lesson. These concepts also apply to DataFrames, which you’ll explore later. While DataFrames don’t use the term "primary key," they behave similarly.
 
-With our cities table created, let’s store the rainfall data. Instead of repeating the full city information, we can use the ID. The new table should also have an *id* column, as all tables should have a primary key.
+With our cities table created, let’s store the rainfall data. Instead of repeating the full city information, we can use the ID. The new table should also have an *id* column, as all tables should have an ID or primary key.
 
 ### rainfall
 
@@ -103,7 +103,7 @@ Notice the **city_id** column in the **rainfall** table. This column contains va
 
 With our data split into two tables, you might wonder how to retrieve it. If you’re using a relational database like MySQL, SQL Server, or Oracle, you can use a language called Structured Query Language (SQL). SQL (sometimes pronounced "sequel") is a standard language for retrieving and modifying data in relational databases.
 
-To retrieve data, you use the `SELECT` command. Essentially, you **select** the columns you want to see **from** the table they’re in. For example, to display just the names of the cities, you could use:
+To retrieve data, you use the `SELECT` command. At its core, you **select** the columns you want to see **from** the table they’re in. For example, to display just the names of the cities, you could use:
 
 ```sql
 SELECT city
@@ -115,11 +115,11 @@ FROM cities;
 -- Auckland
 ```
 
-`SELECT` specifies the columns, and `FROM` specifies the table.
+`SELECT` lists the columns, and `FROM` specifies the table.
 
-> [NOTE] SQL syntax is case-insensitive, meaning `select` and `SELECT` are the same. However, depending on the database, column and table names might be case-sensitive. It’s a best practice to treat everything in programming as case-sensitive. In SQL, it’s common to write keywords in uppercase.
+> [NOTE] SQL syntax is case-insensitive, meaning `select` and `SELECT` are the same. However, depending on the database, column and table names might be case-sensitive. It’s a best practice to treat everything in programming as case-sensitive. In SQL, it’s common to write keywords in all uppercase.
 
-The query above will display all cities. If you only want to display cities in New Zealand, you can use a filter. The SQL keyword for this is `WHERE`, which specifies a condition.
+The query above will display all cities. If you only want to display cities in New Zealand, you need a filter. The SQL keyword for this is `WHERE`, which specifies a condition.
 
 ```sql
 SELECT city
@@ -132,9 +132,9 @@ WHERE country = 'New Zealand';
 
 ## Joining data
 
-So far, we’ve retrieved data from a single table. Now let’s combine data from both **cities** and **rainfall**. This is done by *joining* the tables. You create a connection between the two tables by matching values in a column from each table.
+So far, we’ve retrieved data from a single table. Now we want to combine data from both **cities** and **rainfall**. This is done by *joining* the tables. You create a connection between the two tables by matching values in a column from each table.
 
-In our example, we’ll match the **city_id** column in **rainfall** with the **city_id** column in **cities**. This will link the rainfall data to its corresponding city. The type of join we’ll use is called an *inner* join, which means rows without matches in the other table won’t be displayed. In our case, every city has rainfall data, so all rows will be displayed.
+In our example, we’ll match the **city_id** column in **rainfall** with the **city_id** column in **cities**. This will link the rainfall data to its respective city. The type of join we’ll use is called an *inner* join, which means rows that don’t match won’t be displayed. In our case, every city has rainfall data, so all rows will be displayed.
 
 Let’s retrieve the rainfall data for 2019 for all cities.
 
@@ -147,7 +147,7 @@ FROM cities
     INNER JOIN rainfall ON cities.city_id = rainfall.city_id
 ```
 
-We’ve highlighted the columns we want and specified that we’re joining the tables by **city_id**. Now we can add a `WHERE` statement to filter for the year 2019.
+We’ve highlighted the columns we want and specified that we’re joining the tables by **city_id**. Now we can add the `WHERE` clause to filter for the year 2019.
 
 ```sql
 SELECT cities.city
@@ -167,7 +167,7 @@ WHERE rainfall.year = 2019
 
 ## Summary
 
-Relational databases are designed to divide information across multiple tables, which can then be combined for display and analysis. This approach provides flexibility for calculations and data manipulation. You’ve learned the core concepts of relational databases and how to join two tables.
+Relational databases are built around dividing data into multiple tables, which can then be combined for display and analysis. This approach provides flexibility for calculations and data manipulation. You’ve learned the core concepts of relational databases and how to join two tables.
 
 ## 🚀 Challenge
 
@@ -175,7 +175,7 @@ There are many relational databases available online. Use the skills you’ve le
 
 ## Post-Lecture Quiz
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/9)
 
 ## Review & Self Study
 
@@ -192,4 +192,4 @@ Microsoft Learn offers several resources to deepen your understanding of SQL and
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/2-Working-With-Data/06-non-relational/README.md b/translations/en/2-Working-With-Data/06-non-relational/README.md
index 5469fa01..d9745607 100644
--- a/translations/en/2-Working-With-Data/06-non-relational/README.md
+++ b/translations/en/2-Working-With-Data/06-non-relational/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
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-  "translation_date": "2025-09-05T07:38:44+00:00",
+  "original_hash": "c182e87f9f80be7e7cdffc7b40bbfccf",
+  "translation_date": "2025-09-06T10:05:27+00:00",
   "source_file": "2-Working-With-Data/06-non-relational/README.md",
   "language_code": "en"
 }
@@ -13,49 +13,49 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 |Working with NoSQL Data - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/10)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/10)
 
 Data isn't limited to relational databases. This lesson focuses on non-relational data and introduces the basics of spreadsheets and NoSQL.
 
 ## Spreadsheets
 
-Spreadsheets are a widely used method for storing and analyzing data because they are easy to set up and use. In this lesson, you'll learn the fundamental components of a spreadsheet, along with formulas and functions. Examples will be demonstrated using Microsoft Excel, but most spreadsheet software will have similar features and terminology.
+Spreadsheets are a widely used tool for storing and analyzing data because they are easy to set up and use. In this lesson, you'll learn the fundamental components of a spreadsheet, along with formulas and functions. The examples will use Microsoft Excel, but most spreadsheet software has similar features and terminology.
 
-![An empty Microsoft Excel workbook with two worksheets](../../../../2-Working-With-Data/06-non-relational/images/parts-of-spreadsheet.png)
+![An empty Microsoft Excel workbook with two worksheets](../../../../translated_images/parts-of-spreadsheet.120711c82aa18a45c3e62a491a15bba0a31ab0e9db407ec022702fed8ffd89bf.en.png)
 
 A spreadsheet is a file that can be accessed on a computer, device, or cloud-based file system. The software itself might be browser-based or require installation as an application or app. In Excel, these files are referred to as **workbooks**, and this term will be used throughout the lesson.
 
-A workbook contains one or more **worksheets**, each labeled with tabs. Within a worksheet are rectangular areas called **cells**, which hold the actual data. A cell is the intersection of a row and column, with columns labeled alphabetically and rows labeled numerically. Some spreadsheets include headers in the first few rows to describe the data in the cells.
+A workbook contains one or more **worksheets**, which are labeled with tabs. Each worksheet consists of rectangles called **cells**, where the actual data is stored. A cell is the intersection of a row and column, with columns labeled alphabetically and rows labeled numerically. Some spreadsheets include headers in the first few rows to describe the data in the cells.
 
-Using these basic elements of an Excel workbook, we'll explore an example from [Microsoft Templates](https://templates.office.com/) focused on inventory management to demonstrate additional features of a spreadsheet.
+Using these basic elements of an Excel workbook, we'll explore an example from [Microsoft Templates](https://templates.office.com/) focused on inventory management to demonstrate additional spreadsheet features.
 
 ### Managing an Inventory 
 
-The spreadsheet file named "InventoryExample" is a formatted inventory spreadsheet containing three worksheets, with tabs labeled "Inventory List," "Inventory Pick List," and "Bin Lookup." Row 4 of the Inventory List worksheet serves as the header, describing the value of each cell in the corresponding column.
+The spreadsheet file named "InventoryExample" is a formatted inventory sheet containing three worksheets, with tabs labeled "Inventory List," "Inventory Pick List," and "Bin Lookup." Row 4 of the Inventory List worksheet serves as the header, describing the value of each cell in the corresponding column.
 
-![A highlighted formula from an example inventory list in Microsoft Excel](../../../../2-Working-With-Data/06-non-relational/images/formula-excel.png)
+![A highlighted formula from an example inventory list in Microsoft Excel](../../../../translated_images/formula-excel.ad1068c220892f5ead570d12f2394897961d31a5043a1dd4e6fc5d7690c7a14e.en.png)
 
-Sometimes, a cell's value depends on the values of other cells. For example, the Inventory List spreadsheet tracks the cost of each item in the inventory, but what if we need to calculate the total value of the inventory? [**Formulas**](https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263) perform operations on cell data, and in this case, a formula is used in the Inventory Value column to calculate the value of each item by multiplying the quantity (under the QTY header) by the cost (under the COST header). Double-clicking or highlighting a cell reveals the formula. Formulas always start with an equals sign, followed by the calculation or operation.
+Sometimes, a cell's value depends on other cells to calculate its value. For example, the Inventory List spreadsheet tracks the cost of each item, but what if we need to calculate the total value of the inventory? [**Formulas**](https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263) perform operations on cell data, and in this case, a formula is used in the Inventory Value column to calculate the value of each item by multiplying the quantity (under the QTY header) by the cost (under the COST header). Double-clicking or highlighting a cell reveals the formula. Formulas always start with an equals sign, followed by the calculation or operation.
 
-![A highlighted function from an example inventory list in Microsoft Excel](../../../../2-Working-With-Data/06-non-relational/images/function-excel.png)
+![A highlighted function from an example inventory list in Microsoft Excel](../../../../translated_images/function-excel.be2ae4feddc10ca089f3d4363040d93b7fd046c8d4f83ba975ec46483ee99895.en.png)
 
-To find the total inventory value, we can use another formula to sum up all the values in the Inventory Value column. While adding each cell manually is possible, it can be tedious. Excel provides [**functions**](https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89), which are predefined formulas for performing calculations on cell values. Functions require arguments, which are the values needed for the calculation. If a function requires multiple arguments, they must be listed in a specific order to ensure accurate results. In this example, the SUM function is used to add up the values in the Inventory Value column, with the total displayed in row 3, column B (B3).
+To find the total inventory value, we can use another formula to sum up all the values in the Inventory Value column. While adding each cell manually is possible, it would be tedious. Excel provides [**functions**](https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89), which are predefined formulas for performing calculations. Functions require arguments, which are the values needed for the calculation. If a function requires multiple arguments, they must be listed in the correct order to ensure accurate results. In this example, the SUM function is used to add up the values in the Inventory Value column, with the total displayed in row 3, column B (B3).
 
 ## NoSQL
 
-NoSQL is a broad term encompassing various methods for storing non-relational data. It can be interpreted as "non-SQL," "non-relational," or "not only SQL." These database systems are generally categorized into four types.
+NoSQL is a broad term for various methods of storing non-relational data. It can mean "non-SQL," "non-relational," or "not only SQL." These database systems are generally categorized into four types.
 
-![Graphical representation of a key-value data store showing 4 unique numerical keys that are associated with 4 various values](../../../../2-Working-With-Data/06-non-relational/images/kv-db.png)
+![Graphical representation of a key-value data store showing 4 unique numerical keys that are associated with 4 various values](../../../../translated_images/kv-db.e8f2b75686bbdfcba0c827b9272c10ae0821611ea0fe98429b9d13194383afa6.en.png)
 > Source from [Michał Białecki Blog](https://www.michalbialecki.com/2018/03/18/azure-cosmos-db-key-value-database-cloud/)
 
-[Key-value](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores) databases pair unique keys (identifiers) with associated values. These pairs are stored using a [hash table](https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/) and an appropriate hashing function.
+[Key-value](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores) databases store data as pairs of unique keys and their associated values. These pairs are organized using a [hash table](https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/) with a suitable hashing function.
 
-![Graphical representation of a graph data store showing the relationships between people, their interests and locations](../../../../2-Working-With-Data/06-non-relational/images/graph-db.png)
+![Graphical representation of a graph data store showing the relationships between people, their interests and locations](../../../../translated_images/graph-db.d13629152f79a9dac895b20fa7d841d4d4d6f6008b1382227c3bbd200fd4cfa1.en.png)
 > Source from [Microsoft](https://docs.microsoft.com/en-us/azure/cosmos-db/graph/graph-introduction#graph-database-by-example)
 
-[Graph](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores) databases represent relationships between data as collections of nodes and edges. Nodes represent entities, such as a student or bank statement, while edges represent relationships between entities. Both nodes and edges have properties that provide additional information.
+[Graph](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores) databases represent relationships between data as nodes and edges. A node represents an entity, such as a student or a bank statement, while edges represent the relationships between entities. Both nodes and edges have properties that provide additional information.
 
-![Graphical representation of a columnar data store showing a customer database with two column families named Identity and Contact Info](../../../../2-Working-With-Data/06-non-relational/images/columnar-db.png)
+![Graphical representation of a columnar data store showing a customer database with two column families named Identity and Contact Info](../../../../translated_images/columnar-db.ffcfe73c3e9063a8c8f93f8ace85e1200863584b1e324eb5159d8ca10f62ec04.en.png)
 
 [Columnar](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) data stores organize data into rows and columns, similar to relational databases. However, columns are grouped into column families, where all data within a column family is related and can be retrieved or modified as a single unit.
 
@@ -63,7 +63,7 @@ NoSQL is a broad term encompassing various methods for storing non-relational da
 
 [Document](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#document-data-stores) data stores expand on the concept of key-value stores by using fields and objects. This section explores document databases using the Cosmos DB emulator.
 
-Cosmos DB fits the "Not Only SQL" definition, as its document database uses SQL for querying data. The [previous lesson](../05-relational-databases/README.md) on SQL covers the basics of the language, and we'll apply some of those concepts to a document database here. The Cosmos DB Emulator allows you to create and explore a document database locally on your computer. Learn more about the Emulator [here](https://docs.microsoft.com/en-us/azure/cosmos-db/local-emulator?tabs=ssl-netstd21).
+Cosmos DB fits the "Not Only SQL" definition, as its document database uses SQL for querying data. The [previous lesson](../05-relational-databases/README.md) on SQL covers the basics of the language, and we'll apply similar queries to a document database here. The Cosmos DB Emulator allows you to create and explore a document database locally. Learn more about the Emulator [here](https://docs.microsoft.com/en-us/azure/cosmos-db/local-emulator?tabs=ssl-netstd21).
 
 A document consists of fields and object values, where fields describe the object values. Below is an example of a document.
 
@@ -88,11 +88,11 @@ You can download and install the emulator [for Windows here](https://aka.ms/cosm
 
 The Emulator opens in a browser window, where the Explorer view lets you navigate documents.
 
-![The Explorer view of the Cosmos DB Emulator](../../../../2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-explorer.png)
+![The Explorer view of the Cosmos DB Emulator](../../../../translated_images/cosmosdb-emulator-explorer.a1c80b1347206fe2f30f88fc123821636587d04fc5a56a9eb350c7da6b31f361.en.png)
 
-If you're following along, click "Start with Sample" to generate a sample database called SampleDB. Expanding SampleDB reveals a container called `Persons`. A container holds a collection of items, which are the documents within it. You can explore the four individual documents under `Items`.
+If you're following along, click "Start with Sample" to generate a sample database called SampleDB. Expanding SampleDB reveals a container named `Persons`. A container holds a collection of items, which are the documents within it. You can explore the four individual documents under `Items`.
 
-![Exploring sample data in the Cosmos DB Emulator](../../../../2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons.png)
+![Exploring sample data in the Cosmos DB Emulator](../../../../translated_images/cosmosdb-emulator-persons.bf640586a7077c8985dfd3071946465c8e074c722c7c202d6d714de99a93b90a.en.png)
 
 #### Querying Document Data with the Cosmos DB Emulator
 
@@ -102,15 +102,15 @@ You can query the sample data by clicking the "New SQL Query" button (second but
 
 `SELECT * FROM c where c.age < 40`
 
-![Running a SELECT query on sample data in the Cosmos DB Emulator to find documents that have an age field value that is less than 40](../../../../2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons-query.png)
+ ![Running a SELECT query on sample data in the Cosmos DB Emulator to find documents that have an age field value that is less than 40](../../../../translated_images/cosmosdb-emulator-persons-query.6905ebb497e3cd047cd96e55a0a03f69ce1b91b2b3d8c147e617b746b22b7e33.en.png)
 
 The query returns two documents, both with age values less than 40.
 
 #### JSON and Documents
 
-If you're familiar with JavaScript Object Notation (JSON), you'll notice that documents resemble JSON. A `PersonsData.json` file in this directory contains additional data that can be uploaded to the Persons container in the Emulator using the `Upload Item` button.
+If you're familiar with JavaScript Object Notation (JSON), you'll notice that documents resemble JSON. A `PersonsData.json` file in this directory contains additional data that you can upload to the Persons container in the Emulator using the `Upload Item` button.
 
-APIs that return JSON data can often be directly stored in document databases. Below is another document, representing tweets from the Microsoft Twitter account. This data was retrieved using the Twitter API and inserted into Cosmos DB.
+In many cases, APIs that return JSON data can directly store this data in document databases. Below is another document, representing tweets from the Microsoft Twitter account. This data was retrieved using the Twitter API and inserted into Cosmos DB.
 
 ```json
 {
@@ -131,22 +131,22 @@ Key fields in this document include `created_at`, `id`, and `text`.
 A `TwitterData.json` file is available for upload to the SampleDB database. It's recommended to add it to a separate container. To do this:
 
 1. Click the "New Container" button in the top right.
-2. Select the existing database (SampleDB), create a container ID for the container.
+2. Select the existing database (SampleDB), create a container ID for the new container.
 3. Set the partition key to `/id`.
 4. Click OK (you can ignore the other settings since this is a small dataset running locally).
 5. Open your new container and upload the Twitter Data file using the `Upload Item` button.
 
 Try running a few SELECT queries to find documents containing "Microsoft" in the text field. Hint: 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://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/11)
 
 ## Review & Self Study
 
-- This lesson doesn't cover all the formatting and features available in spreadsheets. Microsoft offers a [comprehensive library of documentation and videos](https://support.microsoft.com/excel) for Excel if you'd like to learn more.
+- This lesson doesn't cover all the formatting and features available in spreadsheets. Microsoft offers a [comprehensive library of documentation and videos](https://support.microsoft.com/excel) for those interested in learning more about Excel.
 
 - This architectural documentation explains the characteristics of different types of non-relational data: [Non-relational Data and NoSQL](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data).
 
-- Cosmos DB is a cloud-based non-relational database that supports the NoSQL types discussed in this lesson. Learn more through this [Cosmos DB Microsoft Learn Module](https://docs.microsoft.com/en-us/learn/paths/work-with-nosql-data-in-azure-cosmos-db/).
+- Cosmos DB is a cloud-based non-relational database that supports the NoSQL types discussed in this lesson. Learn more in this [Cosmos DB Microsoft Learn Module](https://docs.microsoft.com/en-us/learn/paths/work-with-nosql-data-in-azure-cosmos-db/).
 
 ## Assignment
 
@@ -155,4 +155,4 @@ Try running a few SELECT queries to find documents containing "Microsoft" in the
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/2-Working-With-Data/07-python/README.md b/translations/en/2-Working-With-Data/07-python/README.md
index 40cfa106..4d742a2f 100644
--- a/translations/en/2-Working-With-Data/07-python/README.md
+++ b/translations/en/2-Working-With-Data/07-python/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
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-  "translation_date": "2025-09-05T07:39:13+00:00",
+  "original_hash": "577a611517482c3ceaf76d3d8142cba9",
+  "translation_date": "2025-09-06T10:05:49+00:00",
   "source_file": "2-Working-With-Data/07-python/README.md",
   "language_code": "en"
 }
@@ -13,38 +13,40 @@ CO_OP_TRANSLATOR_METADATA:
 | :-------------------------------------------------------------------------------------------------------: |
 |                 Working With Python - _Sketchnote by [@nitya](https://twitter.com/nitya)_                 |
 
-[![Intro Video](../../../../2-Working-With-Data/07-python/images/video-ds-python.png)](https://youtu.be/dZjWOGbsN4Y)
+[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.en.png)](https://youtu.be/dZjWOGbsN4Y)
 
 Databases provide efficient ways to store and query data using query languages, but the most flexible method for processing data is writing your own program to manipulate it. Often, database queries are more effective, but in cases where complex data processing is required, SQL may not be sufficient. 
 
 Data processing can be done in any programming language, but some languages are better suited for working with data. Data scientists typically use one of the following:
 
-* **[Python](https://www.python.org/)**: A general-purpose programming language often considered ideal for beginners due to its simplicity. Python has numerous libraries that can help solve practical problems, such as extracting data from ZIP archives or converting images to grayscale. Beyond data science, Python is widely used for web development.
-* **[R](https://www.r-project.org/)**: A traditional tool designed for statistical data processing. It has a large library repository (CRAN), making it a strong choice for data analysis. However, R is not a general-purpose language and is rarely used outside the data science domain.
-* **[Julia](https://julialang.org/)**: A language specifically developed for data science, offering better performance than Python, making it ideal for scientific experimentation.
+* **[Python](https://www.python.org/)**: A general-purpose programming language known for its simplicity, making it ideal for beginners. Python has numerous libraries that can help solve practical problems, such as extracting data from ZIP archives or converting images to grayscale. Beyond data science, Python is widely used for web development.
+* **[R](https://www.r-project.org/)**: A specialized tool designed for statistical data processing. It has a vast library repository (CRAN), making it a strong choice for data analysis. However, R is not a general-purpose language and is rarely used outside the data science domain.
+* **[Julia](https://julialang.org/)**: A language specifically developed for data science, offering better performance than Python. It is particularly useful for scientific experiments.
 
-In this lesson, we will focus on using Python for basic data processing. We assume you have a basic understanding of the language. For a deeper dive into Python, consider the following resources:
+In this lesson, we will focus on Python for basic data processing, assuming you have a foundational understanding of the language. For a deeper dive into Python, consider these resources:
 
 * [Learn Python in a Fun Way with Turtle Graphics and Fractals](https://github.com/shwars/pycourse) - A quick GitHub-based introduction to Python programming.
 * [Take your First Steps with Python](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) - A learning path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum).
 
-Data can exist in various forms. In this lesson, we will focus on three types: **tabular data**, **text**, and **images**.
+Data can exist in various formats. In this lesson, we will focus on three types: **tabular data**, **text**, and **images**.
 
-Rather than covering all related libraries, we will focus on a few examples of data processing. This approach will give you a clear understanding of what's possible and help you know where to find solutions when needed.
+Rather than covering all related libraries, we will explore a few examples of data processing to give you a sense of what's possible. This approach will help you understand where to find solutions when needed.
 
 > **Best advice**: If you're unsure how to perform a specific data operation, search online. [Stackoverflow](https://stackoverflow.com/) often has useful Python code samples for common tasks.
 
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/12)
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/12)
 
 ## Tabular Data and Dataframes
 
-You’ve already encountered tabular data when discussing relational databases. When dealing with large datasets stored in multiple linked tables, SQL is often the best tool. However, there are situations where you have a single table of data and need to derive **insights** or **understanding**—such as analyzing distributions or correlations. In data science, transforming data and visualizing it are common tasks, and Python makes these steps straightforward.
+You’ve already encountered tabular data when learning about relational databases. When dealing with large datasets stored in multiple linked tables, SQL is the best tool for the job. However, there are situations where you have a single table of data and need to derive **insights** or **understanding**—such as analyzing distributions or correlations. In data science, transforming and visualizing data are common tasks, and Python makes these steps straightforward.
 
 Two key Python libraries for working with tabular data are:
+
 * **[Pandas](https://pandas.pydata.org/)**: Enables manipulation of **DataFrames**, which are similar to relational tables. You can work with named columns and perform operations on rows, columns, or entire DataFrames.
-* **[Numpy](https://numpy.org/)**: A library for working with **tensors**, or multi-dimensional **arrays**. Arrays contain values of the same type and are simpler than DataFrames, offering more mathematical operations with less overhead.
+* **[Numpy](https://numpy.org/)**: A library for handling **tensors**, or multi-dimensional **arrays**. Arrays contain values of the same type and are simpler than DataFrames, offering more mathematical operations with less overhead.
 
 Other useful libraries include:
+
 * **[Matplotlib](https://matplotlib.org/)**: Used for data visualization and graph plotting.
 * **[SciPy](https://www.scipy.org/)**: Provides additional scientific functions. You may recall this library from discussions on probability and statistics.
 
@@ -62,7 +64,7 @@ Pandas revolves around a few core concepts.
 
 A **Series** is a sequence of values, similar to a list or numpy array. The key difference is that a Series has an **index**, which is considered during operations (e.g., addition). The index can be as simple as an integer row number (default when creating a Series from a list or array) or more complex, like a date range.
 
-> **Note**: Introductory Pandas code is available in the accompanying notebook [`notebook.ipynb`](../../../../2-Working-With-Data/07-python/notebook.ipynb). We’ll outline some examples here, but feel free to explore the full notebook.
+> **Note**: Introductory Pandas code is available in the accompanying notebook [`notebook.ipynb`](notebook.ipynb). While we outline some examples here, feel free to explore the full notebook.
 
 Example: Let’s analyze sales at an ice cream shop. We’ll generate a Series of daily sales numbers over a period of time:
 
@@ -74,9 +76,9 @@ print(f"Length of index is {len(idx)}")
 items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)
 items_sold.plot()
 ```
-![Time Series Plot](../../../../2-Working-With-Data/07-python/images/timeseries-1.png)
+![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.en.png)
 
-Suppose we host a weekly party and take an additional 10 packs of ice cream for the event. We can create another Series, indexed by week, to represent this:
+Suppose we host weekly parties and take an additional 10 packs of ice cream for each event. We can create another Series, indexed by week, to represent this:
 ```python
 additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W"))
 ```
@@ -85,7 +87,7 @@ Adding the two Series gives us the total number:
 total_items = items_sold.add(additional_items,fill_value=0)
 total_items.plot()
 ```
-![Time Series Plot](../../../../2-Working-With-Data/07-python/images/timeseries-2.png)
+![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.en.png)
 
 > **Note**: We don’t use the simple syntax `total_items + additional_items`. Doing so would result in many `NaN` (*Not a Number*) values in the resulting Series because missing values in the `additional_items` index lead to `NaN` when added. To avoid this, we specify the `fill_value` parameter during addition.
 
@@ -94,11 +96,11 @@ With time series, we can also **resample** data at different intervals. For inst
 monthly = total_items.resample("1M").mean()
 ax = monthly.plot(kind='bar')
 ```
-![Monthly Time Series Averages](../../../../2-Working-With-Data/07-python/images/timeseries-3.png)
+![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.en.png)
 
 ### DataFrame
 
-A DataFrame is essentially a collection of Series with the same index. You can combine multiple Series into a DataFrame:
+A DataFrame is essentially a collection of Series with the same index. Multiple Series can be combined into a DataFrame:
 ```python
 a = pd.Series(range(1,10))
 b = pd.Series(["I","like","to","play","games","and","will","not","change"],index=range(0,9))
@@ -110,11 +112,11 @@ This creates a horizontal table like this:
 | 0   | 1   | 2    | 3   | 4   | 5      | 6   | 7      | 8    | 9    |
 | 1   | I   | like | to  | use | Python | and | Pandas | very | much |
 
-You can also use Series as columns and specify column names using a dictionary:
+Alternatively, Series can be used as columns, with column names specified via a dictionary:
 ```python
 df = pd.DataFrame({ 'A' : a, 'B' : b })
 ```
-This results in a table like this:
+This produces the following table:
 
 |     | A   | B      |
 | --- | --- | ------ |
@@ -128,7 +130,7 @@ This results in a table like this:
 | 7   | 8   | very   |
 | 8   | 9   | much   |
 
-**Note**: You can achieve this layout by transposing the previous table using:
+**Note**: This table layout can also be achieved by transposing the previous table using:
 ```python
 df = pd.DataFrame([a,b]).T..rename(columns={ 0 : 'A', 1 : 'B' })
 ```
@@ -138,15 +140,15 @@ Key operations on DataFrames include:
 
 **Column selection**: Select individual columns with `df['A']` (returns a Series) or a subset of columns with `df[['B','A']]` (returns another DataFrame).
 
-**Filtering rows**: Filter rows based on criteria, e.g., `df[df['A']>5]` keeps rows where column `A` is greater than 5.
+**Filtering rows**: Filter rows based on criteria, e.g., `df[df['A']>5]` selects rows where column `A` is greater than 5.
 
-> **Note**: Filtering works by creating a boolean Series (`df['A']<5`) that indicates whether the condition is `True` or `False` for each element. Using this boolean Series as an index returns the filtered rows. Avoid using regular Python boolean expressions like `df[df['A']>5 and df['A']<7]`. Instead, use `&` for boolean Series: `df[(df['A']>5) & (df['A']<7)]` (*brackets are required*).
+> **Note**: Filtering works by creating a boolean Series (e.g., `df['A']<5`), which is then used as an index to return matching rows. Avoid using standard Python boolean expressions like `df[df['A']>5 and df['A']<7]`. Instead, use `&` for boolean Series: `df[(df['A']>5) & (df['A']<7)]` (*brackets are required*).
 
 **Creating new columns**: Add new columns with expressions like:
 ```python
 df['DivA'] = df['A']-df['A'].mean() 
 ``` 
-This calculates the divergence of `A` from its mean value. The operation computes a Series and assigns it to the new column. Avoid incompatible operations, such as:
+This calculates the divergence of `A` from its mean value. However, avoid operations incompatible with Series, such as:
 ```python
 # Wrong code -> df['ADescr'] = "Low" if df['A'] < 5 else "Hi"
 df['LenB'] = len(df['B']) # <- Wrong result
@@ -160,7 +162,7 @@ df['LenB'] = df['B'].apply(lambda x : len(x))
 df['LenB'] = df['B'].apply(len)
 ```
 
-After these operations, the DataFrame looks like this:
+After these operations, the resulting DataFrame looks like this:
 
 |     | A   | B      | DivA | LenB |
 | --- | --- | ------ | ---- | ---- |
@@ -179,11 +181,11 @@ After these operations, the DataFrame looks like this:
 df.iloc[:5]
 ```
 
-**Grouping**: Grouping is useful for results similar to *pivot tables* in Excel. For example, to calculate the mean of column `A` for each unique `LenB` value:
+**Grouping**: Similar to Excel pivot tables, grouping allows calculations like the mean of column `A` for each unique `LenB` value:
 ```python
 df.groupby(by='LenB').mean()
 ```
-For more complex aggregations, use the `aggregate` function:
+For more complex aggregations (e.g., mean and count), use the `aggregate` function:
 ```python
 df.groupby(by='LenB') \
  .aggregate({ 'DivA' : len, 'A' : lambda x: x.mean() }) \
@@ -216,21 +218,21 @@ This overview covers the key concepts of Pandas, but the library is incredibly r
 
 ## 🚀 Challenge 1: Analyzing COVID Spread
 
-The first problem we’ll tackle is modeling the spread of the COVID-19 epidemic. To do this, we’ll use data on the number of infected individuals in various countries, provided by the [Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) at [Johns Hopkins University](https://jhu.edu/). The dataset is available in [this GitHub Repository](https://github.com/CSSEGISandData/COVID-19).
+The first problem we’ll tackle is modeling the spread of the COVID-19 epidemic. To do this, we’ll use data on the number of infected individuals across different countries, provided by the [Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) at [Johns Hopkins University](https://jhu.edu/). The dataset is available in [this GitHub Repository](https://github.com/CSSEGISandData/COVID-19).
 
-To demonstrate how to work with data, we encourage you to open [`notebook-covidspread.ipynb`](../../../../2-Working-With-Data/07-python/notebook-covidspread.ipynb) and read through it from start to finish. You can also execute the cells and try out some challenges we’ve included at the end.
+To demonstrate how to work with data, we encourage you to open [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) and go through it from start to finish. You can also execute the cells and try out some challenges we’ve included at the end.
 
-![COVID Spread](../../../../2-Working-With-Data/07-python/images/covidspread.png)
+![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.en.png)
 
 > If you’re unfamiliar with running code in Jupyter Notebook, check out [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
 
 ## Working with Unstructured Data
 
-While data often comes in tabular form, there are cases where we need to work with less structured data, such as text or images. In these situations, to apply the data processing techniques we’ve discussed, we need to **extract** structured data. Here are a few examples:
+While data often comes in tabular form, there are cases where we need to handle less structured data, such as text or images. In these situations, to apply the data processing techniques we’ve discussed, we need to **extract** structured data. Here are a few examples:
 
 * Extracting keywords from text and analyzing their frequency
 * Using neural networks to identify objects in images
-* Detecting emotions in people from video camera feeds
+* Detecting emotions in video camera feeds
 
 ## 🚀 Challenge 2: Analyzing COVID Papers
 
@@ -240,29 +242,29 @@ A complete example of analyzing this dataset using the [Text Analytics for Healt
 
 > **NOTE**: This repository does not include a copy of the dataset. You may need to download the [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) file from [this Kaggle dataset](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge). Registration with Kaggle may be required. Alternatively, you can download the dataset without registration [from here](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html), which includes all full texts in addition to the metadata file.
 
-Open [`notebook-papers.ipynb`](../../../../2-Working-With-Data/07-python/notebook-papers.ipynb) and read through it from start to finish. You can also execute the cells and try out some challenges we’ve included at the end.
+Open [`notebook-papers.ipynb`](notebook-papers.ipynb) and go through it from start to finish. You can also execute the cells and try out some challenges we’ve included at the end.
 
-![Covid Medical Treatment](../../../../2-Working-With-Data/07-python/images/covidtreat.png)
+![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.en.png)
 
 ## Processing Image Data
 
 Recently, powerful AI models have been developed to analyze images. Many tasks can be accomplished using pre-trained neural networks or cloud services. Examples include:
 
 * **Image Classification**, which categorizes images into predefined classes. You can train your own classifiers using services like [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum).
-* **Object Detection**, which identifies various objects in an image. Services like [Computer Vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) can detect common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) models to detect specific objects of interest.
+* **Object Detection**, which identifies various objects in an image. Services like [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) can detect common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) models to detect specific objects of interest.
 * **Face Detection**, including age, gender, and emotion recognition. This can be achieved using [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum).
 
-All these cloud services can be accessed via [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum), making it easy to integrate them into your data exploration workflow.
+These cloud services can be accessed via [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum), making it easy to integrate them into your data exploration workflow.
 
 Here are some examples of working with image data sources:
-* In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/), we analyze Instagram photos to understand what makes people like a photo more. We extract information from images using [Computer Vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) and use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) to build an interpretable model.
+* In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/), we analyze Instagram photos to understand what makes people like a photo more. We extract information from images using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) and use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) to build an interpretable model.
 * In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies), we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) to analyze emotions in event photographs to understand what makes people happy.
 
 ## Conclusion
 
 Whether you’re working with structured or unstructured data, Python allows you to perform all steps related to data processing and analysis. It’s one of the most flexible tools for data processing, which is why most data scientists use Python as their primary tool. If you’re serious about pursuing data science, learning Python in depth is highly recommended!
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/13)
 
 ## Review & Self Study
 
diff --git a/translations/en/2-Working-With-Data/08-data-preparation/README.md b/translations/en/2-Working-With-Data/08-data-preparation/README.md
index 54ab413a..a7a0f730 100644
--- a/translations/en/2-Working-With-Data/08-data-preparation/README.md
+++ b/translations/en/2-Working-With-Data/08-data-preparation/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "90a815d332aea41a222f4c6372e7186e",
-  "translation_date": "2025-09-05T07:40:23+00:00",
+  "original_hash": "1b560955ff39a2bcf2a049fce474a951",
+  "translation_date": "2025-09-06T10:06:52+00:00",
   "source_file": "2-Working-With-Data/08-data-preparation/README.md",
   "language_code": "en"
 }
@@ -13,34 +13,34 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 |Data Preparation - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/14)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/14)
 
-Raw data, depending on its source, may have inconsistencies that make analysis and modeling difficult. This type of data is often referred to as "dirty" and requires cleaning. This lesson focuses on techniques for cleaning and transforming data to address issues like missing, inaccurate, or incomplete data. The topics covered will use Python and the Pandas library and will be [demonstrated in the notebook](../../../../2-Working-With-Data/08-data-preparation/notebook.ipynb) in this directory.
+Raw data, depending on its source, may contain inconsistencies that make analysis and modeling difficult. This type of data is often referred to as "dirty" and requires cleaning. This lesson focuses on techniques for cleaning and transforming data to address issues like missing, inaccurate, or incomplete data. The topics covered will use Python and the Pandas library and will be [demonstrated in the notebook](notebook.ipynb) in this directory.
 
 ## The importance of cleaning data
 
 - **Ease of use and reuse**: Properly organized and normalized data is easier to search, use, and share with others.
 
-- **Consistency**: Data science often involves working with multiple datasets, which may need to be combined. Ensuring that each dataset follows common standards helps maintain its usefulness when merged.
+- **Consistency**: Data science often involves working with multiple datasets, which may need to be combined. Ensuring that each dataset follows common standards makes the merged dataset more useful.
 
-- **Model accuracy**: Clean data improves the accuracy of models that depend on it.
+- **Model accuracy**: Clean data improves the accuracy of models that rely on it.
 
 ## Common cleaning goals and strategies
 
-- **Exploring a dataset**: Data exploration, covered in a [later lesson](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/4-Data-Science-Lifecycle/15-analyzing), helps identify data that needs cleaning. Observing values visually can set expectations or highlight issues to resolve. Exploration may involve querying, visualizations, and sampling.
+- **Exploring a dataset**: Data exploration, covered in a [later lesson](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/4-Data-Science-Lifecycle/15-analyzing), helps identify data that needs cleaning. Observing values visually can set expectations or highlight problems to address. Exploration may involve querying, visualizations, and sampling.
 
-- **Formatting**: Data from different sources may have inconsistencies in presentation, causing issues in searches or visualizations. Common formatting problems include whitespace, dates, and data types. Resolving these issues often depends on the user's needs, as standards for dates and numbers vary by region.
+- **Formatting**: Data from different sources may have inconsistencies in presentation, which can affect searches and visualizations. Common formatting issues include whitespace, dates, and data types. Resolving these issues often depends on the user's needs, as standards for dates and numbers can vary by country.
 
-- **Duplications**: Duplicate data can lead to inaccurate results and often needs removal. This is common when merging datasets. However, some duplicates may contain additional information and should be preserved.
+- **Duplications**: Duplicate data can lead to inaccurate results and often needs to be removed. However, in some cases, duplicates may contain additional useful information and should be preserved.
 
 - **Missing Data**: Missing data can lead to inaccuracies or biased results. Solutions include reloading the data, filling in missing values programmatically, or removing the affected data. The approach depends on the reasons behind the missing data.
 
 ## Exploring DataFrame information
 > **Learning goal:** By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.
 
-Once data is loaded into pandas, it is typically stored in a DataFrame (refer to the previous [lesson](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/07-python#dataframe) for details). If your DataFrame contains 60,000 rows and 400 columns, how do you start understanding it? Fortunately, [pandas](https://pandas.pydata.org/) offers tools to quickly view overall information about a DataFrame, as well as its first and last few rows.
+Once data is loaded into pandas, it is typically stored in a DataFrame (refer to the previous [lesson](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/07-python#dataframe) for an overview). If your DataFrame contains 60,000 rows and 400 columns, how do you start understanding it? Fortunately, [pandas](https://pandas.pydata.org/) offers tools to quickly summarize the DataFrame and view its first and last few rows.
 
-To explore this functionality, we will use the Python scikit-learn library and the well-known **Iris dataset**.
+To explore this functionality, we will import the Python scikit-learn library and use the well-known **Iris dataset**.
 
 ```python
 import pandas as pd
@@ -57,7 +57,7 @@ iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])
 |3                                       |4.6              |3.1             |1.5              |0.2             |
 |4                                       |5.0              |3.6             |1.4              |0.2             |
 
-- **DataFrame.info**: The `info()` method provides a summary of the content in a `DataFrame`. Let’s examine this dataset:
+- **DataFrame.info**: The `info()` method provides a summary of the DataFrame's content. Let's examine the dataset:
 ```python
 iris_df.info()
 ```
@@ -75,7 +75,7 @@ memory usage: 4.8 KB
 ```
 This tells us the *Iris* dataset has 150 entries across four columns, with no null values. All data is stored as 64-bit floating-point numbers.
 
-- **DataFrame.head()**: To view the first few rows of the `DataFrame`, use the `head()` method:
+- **DataFrame.head()**: To view the first few rows of the DataFrame, use the `head()` method:
 ```python
 iris_df.head()
 ```
@@ -99,18 +99,18 @@ iris_df.tail()
 148                6.2               3.4                5.4               2.3
 149                5.9               3.0                5.1               1.8
 ```
-> **Takeaway:** By examining metadata or the first and last few rows of a DataFrame, you can quickly understand its size, structure, and content.
+> **Takeaway:** By examining metadata and the first/last few rows of a DataFrame, you can quickly understand its size, structure, and content.
 
 ## Dealing with Missing Data
 > **Learning goal:** By the end of this subsection, you should know how to replace or remove null values from DataFrames.
 
-Datasets often contain missing values. How you handle missing data can impact your analysis and real-world outcomes.
+Most datasets you work with will have missing values. How you handle missing data can impact your analysis and real-world outcomes.
 
-Pandas uses two methods to represent missing values: `NaN` (Not a Number) for floating-point values and `None` for other types. While this dual approach may seem confusing, it provides flexibility for most use cases. However, both `None` and `NaN` have limitations you should be aware of.
+Pandas uses two methods to represent missing values: `NaN` (Not a Number) for missing floating-point values and `None` for other types. While having two representations might seem confusing, this design choice balances flexibility and performance. Both `None` and `NaN` have limitations you should be aware of.
 
 Learn more about `NaN` and `None` in the [notebook](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb)!
 
-- **Detecting null values**: Use the `isnull()` and `notnull()` methods in pandas to detect null data. Both return Boolean masks over your data. We’ll use `numpy` for `NaN` values:
+- **Detecting null values**: Use the `isnull()` and `notnull()` methods to detect null data. Both return Boolean masks over your data. We'll use `numpy` for `NaN` values:
 ```python
 import numpy as np
 
@@ -128,9 +128,9 @@ Notice that `0` is treated as a valid integer, not null. Similarly, `''` (an emp
 
 You can use Boolean masks directly as a `Series` or `DataFrame` index to isolate missing or present values.
 
-> **Takeaway**: The `isnull()` and `notnull()` methods provide results along with their indices, making it easier to work with your data.
+> **Takeaway**: The `isnull()` and `notnull()` methods help identify null values and their indices, making it easier to work with your data.
 
-- **Dropping null values**: Pandas allows you to remove null values from `Series` and `DataFrame`s. For large datasets, removing missing values is often more practical than other approaches. Let’s revisit `example1`:
+- **Dropping null values**: Pandas provides the `dropna()` method to remove null values from `Series` and `DataFrame`s. For large datasets, removing null values is often more practical than filling them. Let's revisit `example1`:
 ```python
 example1 = example1.dropna()
 example1
@@ -140,7 +140,7 @@ example1
 2     
 dtype: object
 ```
-This output matches `example3[example3.notnull()]`, but `dropna` removes missing values directly from the `Series`.
+This output matches `example3[example3.notnull()]`, but `dropna()` removes missing values entirely.
 
 For `DataFrame`s, you can drop rows or columns containing null values:
 ```python
@@ -155,7 +155,7 @@ example2
 |1     |2.0|5.0|8  |
 |2     |NaN|6.0|9  |
 
-You can drop rows (default behavior) or columns using `axis=1`:
+You can drop rows (default behavior) or columns (`axis=1`) containing null values:
 ```python
 example2.dropna()
 ```
@@ -190,7 +190,7 @@ example2.dropna(axis='rows', thresh=3)
 1	2.0	5.0	8	NaN
 ```
 
-- **Filling null values**: Instead of dropping null values, you can replace them with valid ones using `fillna`. Let’s create another example `Series`:
+- **Filling null values**: Instead of dropping null values, you can fill them with valid ones using `fillna()`. This method replaces missing values with a specified value. Let's create another example `Series`:
 ```python
 example3 = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))
 example3
@@ -203,7 +203,7 @@ d    NaN
 e    3.0
 dtype: float64
 ```
-Replace all null entries with a single value, like `0`:
+Fill all null entries with a single value, like `0`:
 ```python
 example3.fillna(0)
 ```
@@ -239,7 +239,7 @@ d    3.0
 e    3.0
 dtype: float64
 ```
-For `DataFrame`s, you can specify an `axis` to fill null values:
+For `DataFrame`s, you can specify an `axis` to fill null values along rows or columns:
 ```python
 example2.fillna(method='ffill', axis=1)
 ```
@@ -249,16 +249,15 @@ example2.fillna(method='ffill', axis=1)
 1	2.0	5.0	8.0	8.0
 2	NaN	6.0	9.0	9.0
 ```
-If no previous value exists for forward-filling, the null value remains.
-> **Takeaway:** There are several ways to handle missing values in your datasets. The approach you choose—whether it's removing them, replacing them, or deciding how to replace them—should depend on the specifics of your data. The more you work with datasets, the better you'll understand how to manage missing values effectively.
-
+When no previous value is available for forward-filling, the null value remains.
+> **Takeaway:** There are several ways to handle missing values in your datasets. The approach you choose (whether to remove them, replace them, or decide how to replace them) should depend on the specific characteristics of the data. The more you work with and analyze datasets, the better you'll become at managing missing values.
 ## Removing duplicate data
 
-> **Learning goal:** By the end of this subsection, you should feel confident identifying and removing duplicate values from DataFrames.
+> **Learning goal:** By the end of this subsection, you should be comfortable identifying and removing duplicate values from DataFrames.
 
-In addition to missing data, duplicated data is another common issue in real-world datasets. Luckily, `pandas` makes it simple to detect and remove duplicate entries.
+In addition to missing data, you will often encounter duplicated data in real-world datasets. Fortunately, `pandas` provides an easy way to detect and remove duplicate entries.
 
-- **Identifying duplicates: `duplicated`**: You can easily identify duplicate values using the `duplicated` method in pandas. This method returns a Boolean mask that indicates whether an entry in a `DataFrame` is a duplicate of a previous one. Let's create another example `DataFrame` to see how this works.
+- **Identifying duplicates: `duplicated`**: You can easily identify duplicate values using the `duplicated` method in pandas, which returns a Boolean mask indicating whether an entry in a `DataFrame` is a duplicate of an earlier one. Let's create another example `DataFrame` to see this in action.
 ```python
 example4 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'],
                          'numbers': [1, 2, 1, 3, 3]})
@@ -283,7 +282,7 @@ example4.duplicated()
 4     True
 dtype: bool
 ```
-- **Dropping duplicates: `drop_duplicates`:** This method returns a copy of the data where all `duplicated` values are marked as `False`:
+- **Dropping duplicates: `drop_duplicates`:** This method simply returns a copy of the data where all `duplicated` values are `False`:
 ```python
 example4.drop_duplicates()
 ```
@@ -293,7 +292,7 @@ example4.drop_duplicates()
 1	B	2
 3	B	3
 ```
-Both `duplicated` and `drop_duplicates` default to considering all columns, but you can specify that they only examine a subset of columns in your `DataFrame`:
+Both `duplicated` and `drop_duplicates` default to consider all columns, but you can specify that they examine only a subset of columns in your `DataFrame`:
 ```python
 example4.drop_duplicates(['letters'])
 ```
@@ -303,22 +302,26 @@ letters	numbers
 1	B	2
 ```
 
-> **Takeaway:** Removing duplicate data is a crucial step in nearly every data science project. Duplicate data can skew your analyses and lead to inaccurate results!
+> **Takeaway:** Removing duplicate data is an essential part of almost every data-science project. Duplicate data can skew the results of your analyses and lead to inaccurate conclusions!
+
 
 ## 🚀 Challenge
 
-All the materials covered in this lesson are available 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 at the end of each section—give them a try!
+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 at the end of each section—give them a try!
+
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/15)
+
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
 
 ## Review & Self Study
 
-There are many ways to explore and approach preparing your data for analysis and modeling. Cleaning your data is a critical step that requires hands-on experience. Try these Kaggle challenges to learn techniques not covered in this lesson:
+There are many ways to explore and approach preparing your data for analysis and modeling, and cleaning the data is an important step that requires "hands-on" practice. Try these challenges from Kaggle to explore techniques that were not covered in this lesson.
 
 - [Data Cleaning Challenge: Parsing Dates](https://www.kaggle.com/rtatman/data-cleaning-challenge-parsing-dates/)
 
 - [Data Cleaning Challenge: Scale and Normalize Data](https://www.kaggle.com/rtatman/data-cleaning-challenge-scale-and-normalize-data)
 
+
 ## Assignment
 
 [Evaluating Data from a Form](assignment.md)
diff --git a/translations/en/3-Data-Visualization/09-visualization-quantities/README.md b/translations/en/3-Data-Visualization/09-visualization-quantities/README.md
index 19c44594..69ef23e0 100644
--- a/translations/en/3-Data-Visualization/09-visualization-quantities/README.md
+++ b/translations/en/3-Data-Visualization/09-visualization-quantities/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "69b32b6789a91f796ebc7a02f5575e03",
-  "translation_date": "2025-09-05T07:43:38+00:00",
+  "original_hash": "a49d78e32e280c410f04e5f2a2068e77",
+  "translation_date": "2025-09-06T10:09:46+00:00",
   "source_file": "3-Data-Visualization/09-visualization-quantities/README.md",
   "language_code": "en"
 }
@@ -13,13 +13,13 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 | Visualizing Quantities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-In this lesson, you'll learn how to use one of the many Python libraries available to create engaging visualizations focused on the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you'll uncover fascinating insights about local wildlife.
+In this lesson, you'll learn how to use one of the many Python libraries available to create engaging visualizations focused on the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can uncover fascinating insights about local wildlife.
 
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/16)
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/16)
 
 ## Observe wingspan with Matplotlib
 
-[Matplotlib](https://matplotlib.org/stable/index.html) is an excellent library for creating both simple and complex plots and charts of various types. Generally, the process of plotting data with these libraries involves identifying the parts of your dataframe to target, performing any necessary transformations, assigning x and y axis values, choosing the type of plot, and then displaying it. Matplotlib offers a wide range of visualizations, but for this lesson, we'll focus on those best suited for visualizing quantities: line charts, scatterplots, and bar plots.
+A great library for creating both simple and complex plots and charts is [Matplotlib](https://matplotlib.org/stable/index.html). Generally, the process of plotting data with these libraries involves identifying the parts of your dataframe to target, performing any necessary transformations, assigning x and y axis values, choosing the type of plot, and then displaying it. Matplotlib offers a wide range of visualizations, but for this lesson, we'll focus on those best suited for visualizing quantities: line charts, scatterplots, and bar plots.
 
 > ✅ Choose the chart type that best fits your data structure and the story you want to tell.  
 > - To analyze trends over time: line  
@@ -29,13 +29,13 @@ In this lesson, you'll learn how to use one of the many Python libraries availab
 > - To show trends: line, column  
 > - To show relationships between values: line, scatterplot, bubble  
 
-If you have a dataset and need to determine how much of a particular item is included, one of your first tasks will be to inspect its values.
+If you have a dataset and need to determine how much of a specific item is included, one of your first tasks will be to inspect its values.
 
 ✅ There are excellent 'cheat sheets' for Matplotlib available [here](https://matplotlib.org/cheatsheets/cheatsheets.pdf).
 
 ## Build a line plot about bird wingspan values
 
-Open the `notebook.ipynb` file located at the root of this lesson folder and add a cell.
+Open the `notebook.ipynb` file located in the root of this lesson folder and add a cell.
 
 > Note: The data is stored in the root of this repository in the `/data` folder.
 
@@ -61,13 +61,13 @@ Let's start by plotting some of the numeric data using a basic line plot. Suppos
 wingspan = birds['MaxWingspan'] 
 wingspan.plot()
 ```  
-![Max Wingspan](../../../../3-Data-Visualization/09-visualization-quantities/images/max-wingspan-02.png)
+![Max Wingspan](../../../../translated_images/max-wingspan-02.e79fd847b2640b89e21e340a3a9f4c5d4b224c4fcd65f54385e84f1c9ed26d52.en.png)
 
-What stands out immediately? There appears to be at least one outlier—what a wingspan! A 2300-centimeter wingspan equals 23 meters—are there Pterodactyls in Minnesota? Let's investigate.
+What stands out immediately? There seems to be at least one outlier—what a wingspan! A 2300-centimeter wingspan equals 23 meters—are there Pterodactyls in Minnesota? Let's investigate.
 
 While you could quickly sort the data in Excel to find these outliers (likely typos), continue the visualization process by working directly within the plot.
 
-Add labels to the x-axis to indicate the types of birds being analyzed:
+Add labels to the x-axis to show the types of birds in question:
 
 ```
 plt.title('Max Wingspan in Centimeters')
@@ -81,9 +81,9 @@ plt.plot(x, y)
 
 plt.show()
 ```  
-![wingspan with labels](../../../../3-Data-Visualization/09-visualization-quantities/images/max-wingspan-labels-02.png)
+![Wingspan with labels](../../../../translated_images/max-wingspan-labels-02.aa90e826ca49a9d1dde78075e9755c1849ef56a4e9ec60f7e9f3806daf9283e2.en.png)
 
-Even with the labels rotated 45 degrees, there are too many to read. Let's try a different approach: label only the outliers and place the labels within the chart. You can use a scatter chart to make room for the labeling:
+Even with the labels rotated 45 degrees, there are too many to read. Let's try a different approach: label only the outliers and set the labels within the chart. You can use a scatter chart to make room for the labeling:
 
 ```python
 plt.title('Max Wingspan in Centimeters')
@@ -99,15 +99,15 @@ for i in range(len(birds)):
     
 plt.show()
 ```  
-What's happening here? You used `tick_params` to hide the bottom labels and then looped through your birds dataset. By plotting the chart with small round blue dots (`bo`), you checked for any bird with a maximum wingspan over 500 and displayed its label next to the dot. You offset the labels slightly on the y-axis (`y * (1 - 0.05)`) and used the bird name as the label.
+What's happening here? You used `tick_params` to hide the bottom labels and then looped through your bird dataset. By plotting the chart with small round blue dots (`bo`), you checked for any bird with a maximum wingspan over 500 and displayed its label next to the dot. You offset the labels slightly on the y-axis (`y * (1 - 0.05)`) and used the bird name as the label.
 
 What did you discover?
 
-![outliers](../../../../3-Data-Visualization/09-visualization-quantities/images/labeled-wingspan-02.png)
+![Outliers](../../../../translated_images/labeled-wingspan-02.6110e2d2401cd5238ccc24dfb6d04a6c19436101f6cec151e3992e719f9f1e1f.en.png)
 
 ## Filter your data
 
-Both the Bald Eagle and the Prairie Falcon, while likely large birds, seem to have been mislabeled with an extra `0` added to their maximum wingspan. It's unlikely you'll encounter a Bald Eagle with a 25-meter wingspan, but if you do, let us know! Let's create a new dataframe without these two outliers:
+Both the Bald Eagle and the Prairie Falcon, while likely large birds, appear to be mislabeled with an extra `0` added to their maximum wingspan. It's unlikely you'll encounter a Bald Eagle with a 25-meter wingspan, but if you do, let us know! Let's create a new dataframe without these two outliers:
 
 ```python
 plt.title('Max Wingspan in Centimeters')
@@ -122,21 +122,21 @@ for i in range(len(birds)):
 plt.show()
 ```  
 
-By filtering out the outliers, your data becomes more cohesive and easier to interpret.
+By filtering out the outliers, your data becomes more cohesive and easier to understand.
 
-![scatterplot of wingspans](../../../../3-Data-Visualization/09-visualization-quantities/images/scatterplot-wingspan-02.png)
+![Scatterplot of wingspans](../../../../translated_images/scatterplot-wingspan-02.1c33790094ce36a75f5fb45b25ed2cf27f0356ea609e43c11e97a2cedd7011a4.en.png)
 
-Now that we have a cleaner dataset in terms of wingspan, let's explore more about these birds.
+Now that we have a cleaner dataset, at least in terms of wingspan, let's explore more about these birds.
 
-While line and scatter plots can display information about data values and their distributions, we want to focus on the quantities inherent in this dataset. You could create visualizations to answer questions like:
+While line and scatter plots can display information about data values and their distributions, we want to focus on the values inherent in this dataset. You could create visualizations to answer questions about quantity, such as:
 
-> How many categories of birds are there, and what are their counts?  
+> How many categories of birds are there, and what are their numbers?  
 > How many birds are extinct, endangered, rare, or common?  
-> How many birds belong to various genera and orders in Linnaeus's classification?  
+> How many birds belong to various genera and orders in Linnaeus's classification?
 
 ## Explore bar charts
 
-Bar charts are useful for showing groupings of data. Let's examine the bird categories in this dataset to determine which is the most common.
+Bar charts are useful for showing groupings of data. Let's explore the bird categories in this dataset to see which is the most common.
 
 In the notebook file, create a basic bar chart.
 
@@ -151,30 +151,30 @@ birds.plot(x='Category',
         title='Birds of Minnesota')
 
 ```  
-![full data as a bar chart](../../../../3-Data-Visualization/09-visualization-quantities/images/full-data-bar-02.png)
+![Full data as a bar chart](../../../../translated_images/full-data-bar-02.aaa3fda71c63ed564b917841a1886c177dd9a26424142e510c0c0498fd6ca160.en.png)
 
-This bar chart, however, is difficult to read because the data isn't grouped. You need to select only the data you want to plot, so let's examine the length of birds based on their category.
+This bar chart, however, is hard to read because the data isn't grouped. You need to select only the data you want to plot, so let's look at the bird categories based on their count.
 
-Filter your data to include only the bird's category.
+Filter your data to include only the bird categories.
 
-✅ Note: Use Pandas to manage the data, and let Matplotlib handle the charting.
+✅ Notice how you use Pandas to manage the data and let Matplotlib handle the charting.
 
-Since there are many categories, display this chart vertically and adjust its height to accommodate all the data:
+Since there are many categories, display this chart vertically and adjust its height to fit all the data:
 
 ```python
 category_count = birds.value_counts(birds['Category'].values, sort=True)
 plt.rcParams['figure.figsize'] = [6, 12]
 category_count.plot.barh()
 ```  
-![category and length](../../../../3-Data-Visualization/09-visualization-quantities/images/category-counts-02.png)
+![Category and length](../../../../translated_images/category-counts-02.0b9a0a4de42275ae5096d0f8da590d8bf520d9e7e40aad5cc4fc8d276480cc32.en.png)
 
-This bar chart provides a clear view of the number of birds in each category. At a glance, you can see that the Ducks/Geese/Waterfowl category has the largest number of birds in this region. Given that Minnesota is the "land of 10,000 lakes," this isn't surprising!
+This bar chart provides a clear view of the number of birds in each category. At a glance, you can see that the largest number of birds in this region belong to the Ducks/Geese/Waterfowl category. Given that Minnesota is the "land of 10,000 lakes," this isn't surprising!
 
 ✅ Try counting other attributes in this dataset. Do any results surprise you?
 
 ## Comparing data
 
-You can explore different comparisons of grouped data by creating new axes. For example, compare the MaxLength of birds based on their category:
+You can compare grouped data by creating new axes. For example, compare the MaxLength of birds based on their category:
 
 ```python
 maxlength = birds['MaxLength']
@@ -182,7 +182,7 @@ plt.barh(y=birds['Category'], width=maxlength)
 plt.rcParams['figure.figsize'] = [6, 12]
 plt.show()
 ```  
-![comparing data](../../../../3-Data-Visualization/09-visualization-quantities/images/category-length-02.png)
+![Comparing data](../../../../translated_images/category-length-02.7304bf519375c9807d8165cc7ec60dd2a60f7b365b23098538e287d89adb7d76.en.png)
 
 Nothing surprising here: hummingbirds have the smallest MaxLength compared to pelicans or geese. It's reassuring when data aligns with logic!
 
@@ -198,19 +198,19 @@ plt.barh(category, minLength)
 
 plt.show()
 ```  
-In this plot, you can see the range of Minimum and Maximum Length for each bird category. Based on this data, you can confidently say that larger birds tend to have a wider length range. Fascinating!
+In this plot, you can see the range of Minimum and Maximum Length for each bird category. You can confidently say that, based on this data, larger birds tend to have a wider length range. Fascinating!
 
-![superimposed values](../../../../3-Data-Visualization/09-visualization-quantities/images/superimposed-02.png)
+![Superimposed values](../../../../translated_images/superimposed-02.f03058536baeb2ed7864f01102538464d4c2fd7ade881ddd7d5ba74dc5d2fdae.en.png)
 
 ## 🚀 Challenge
 
-This bird dataset offers a wealth of information about various bird types within a specific ecosystem. Search online for other bird-related datasets and practice building charts and graphs to uncover surprising facts.
+This bird dataset offers a wealth of information about different bird types within a specific ecosystem. Search online for other bird-related datasets and practice building charts and graphs to uncover surprising facts.
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/17)
 
 ## Review & Self Study
 
-This lesson introduced you to using Matplotlib for visualizing quantities. Research other methods for working with datasets to create visualizations. [Plotly](https://github.com/plotly/plotly.py) is one library we won't cover in these lessons, so explore its features.
+This lesson introduced you to using Matplotlib for visualizing quantities. Research other ways to work with datasets for visualization. [Plotly](https://github.com/plotly/plotly.py) is one library we won't cover in these lessons, so explore its features.
 
 ## Assignment
 
diff --git a/translations/en/3-Data-Visualization/10-visualization-distributions/README.md b/translations/en/3-Data-Visualization/10-visualization-distributions/README.md
index d26b801e..65fe90c5 100644
--- a/translations/en/3-Data-Visualization/10-visualization-distributions/README.md
+++ b/translations/en/3-Data-Visualization/10-visualization-distributions/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "02ce904bc1e2bfabb7dc05c25aae375c",
-  "translation_date": "2025-09-05T07:45:01+00:00",
+  "original_hash": "80a20467e046d312809d008395051fc7",
+  "translation_date": "2025-09-06T10:10:54+00:00",
   "source_file": "3-Data-Visualization/10-visualization-distributions/README.md",
   "language_code": "en"
 }
@@ -15,8 +15,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 In the previous lesson, you explored a dataset about the birds of Minnesota. You identified some erroneous data by visualizing outliers and examined differences between bird categories based on their maximum length.
 
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/18)
-
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/18)
 ## Explore the birds dataset
 
 Another way to analyze data is by examining its distribution, or how the data is spread along an axis. For instance, you might want to understand the general distribution of maximum wingspan or maximum body mass for the birds of Minnesota in this dataset.
@@ -49,36 +48,36 @@ plt.xlabel('Max Length')
 
 plt.show()
 ```
-![max length per order](../../../../3-Data-Visualization/10-visualization-distributions/images/scatter-wb.png)
+![max length per order](../../../../translated_images/scatter-wb.9d98b0ed7f0388af979441853361a11df5f518f5307938a503ca7913e986111b.en.png)
 
 This provides an overview of the general distribution of body length per bird Order, but it’s not the best way to display true distributions. That’s where histograms come in.
 
 ## Working with histograms
 
-Matplotlib provides excellent tools for visualizing data distributions using histograms. A histogram is similar to a bar chart, but it shows the distribution through the rise and fall of the bars. To create a histogram, you need numeric data. You can plot a histogram by specifying the chart type as 'hist'. This type of chart displays the distribution of MaxBodyMass across the dataset’s numeric range. By dividing the data into smaller bins, it reveals the spread of values:
+Matplotlib provides excellent tools for visualizing data distributions using histograms. This type of chart resembles a bar chart, where the rise and fall of the bars represent the distribution. To create a histogram, you need numeric data. You can plot a histogram by specifying the chart type as 'hist'. This chart shows the distribution of MaxBodyMass across the dataset’s numeric range. By dividing the data into smaller bins, it reveals the distribution of values:
 
 ```python
 birds['MaxBodyMass'].plot(kind = 'hist', bins = 10, figsize = (12,12))
 plt.show()
 ```
-![distribution over the entire dataset](../../../../3-Data-Visualization/10-visualization-distributions/images/dist1-wb.png)
+![distribution over the entire dataset](../../../../translated_images/dist1-wb.0d0cac82e2974fbbec635826fefead401af795f82e2279e2e2678bf2c117d827.en.png)
 
-As shown, most of the 400+ birds in this dataset have a Max Body Mass under 2000. You can gain more detailed insights by increasing the `bins` parameter to a higher number, such as 30:
+As you can see, most of the 400+ birds in this dataset have a Max Body Mass under 2000. You can gain more insight by increasing the `bins` parameter to a higher number, such as 30:
 
 ```python
 birds['MaxBodyMass'].plot(kind = 'hist', bins = 30, figsize = (12,12))
 plt.show()
 ```
-![distribution over the entire dataset with larger bins param](../../../../3-Data-Visualization/10-visualization-distributions/images/dist2-wb.png)
+![distribution over the entire dataset with larger bins param](../../../../translated_images/dist2-wb.2c0a7a3499b2fbf561e9f93b69f265dfc538dc78f6de15088ba84a88152e26ba.en.png)
 
-This chart provides a more granular view of the distribution. To create a chart that’s less skewed to the left, you can filter the data to include only birds with a body mass under 60 and set the `bins` parameter to 40:
+This chart provides a more detailed view of the distribution. To create a chart that’s less skewed to the left, you can filter the data to include only birds with a body mass under 60 and display 40 `bins`:
 
 ```python
 filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)]      
 filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12))
 plt.show()     
 ```
-![filtered histogram](../../../../3-Data-Visualization/10-visualization-distributions/images/dist3-wb.png)
+![filtered histogram](../../../../translated_images/dist3-wb.64b88db7f9780200bd486a2c2a3252548dd439672dbd3f778193db7f654b100c.en.png)
 
 ✅ Experiment with other filters and data points. To view the full distribution of the data, remove the `['MaxBodyMass']` filter to display labeled distributions.
 
@@ -93,11 +92,11 @@ y = filteredBirds['MaxLength']
 fig, ax = plt.subplots(tight_layout=True)
 hist = ax.hist2d(x, y)
 ```
-There seems to be a predictable correlation between these two variables along an expected axis, with one particularly strong point of convergence:
+There seems to be a predictable correlation between these two elements along a specific axis, with one particularly strong point of convergence:
 
-![2D plot](../../../../3-Data-Visualization/10-visualization-distributions/images/2D-wb.png)
+![2D plot](../../../../translated_images/2D-wb.ae22fdd33936507a41e3af22e11e4903b04a9be973b23a4e05214efaccfd66c8.en.png)
 
-Histograms work well for numeric data by default. But what if you want to analyze distributions based on text data?
+Histograms work well for numeric data by default. But what if you need to analyze distributions based on text data?
 
 ## Explore the dataset for distributions using text data 
 
@@ -135,13 +134,13 @@ plt.gca().set(title='Conservation Status', ylabel='Min Wingspan')
 plt.legend();
 ```
 
-![wingspan and conservation collation](../../../../3-Data-Visualization/10-visualization-distributions/images/histogram-conservation-wb.png)
+![wingspan and conservation collation](../../../../translated_images/histogram-conservation-wb.3c40450eb072c14de7a1a3ec5c0fcba4995531024760741b392911b567fd8b70.en.png)
 
 There doesn’t appear to be a strong correlation between minimum wingspan and conservation status. Test other elements of the dataset using this method. Try different filters as well. Do you find any correlations?
 
 ## Density plots
 
-You may have noticed that the histograms we’ve examined so far are 'stepped' and don’t flow smoothly in an arc. To create a smoother density chart, you can use a density plot.
+You may have noticed that the histograms we’ve examined so far are 'stepped' and don’t flow smoothly. To create a smoother density chart, you can use a density plot.
 
 To work with density plots, familiarize yourself with a new plotting library, [Seaborn](https://seaborn.pydata.org/generated/seaborn.kdeplot.html). 
 
@@ -153,25 +152,25 @@ import matplotlib.pyplot as plt
 sns.kdeplot(filteredBirds['MinWingspan'])
 plt.show()
 ```
-![Density plot](../../../../3-Data-Visualization/10-visualization-distributions/images/density1.png)
+![Density plot](../../../../translated_images/density1.8801043bd4af2567b0f706332b5853c7614e5e4b81b457acc27eb4e092a65cbd.en.png)
 
 This plot mirrors the previous one for Minimum Wingspan data but is smoother. According to Seaborn’s documentation, "Relative to a histogram, KDE can produce a plot that is less cluttered and more interpretable, especially when drawing multiple distributions. But it has the potential to introduce distortions if the underlying distribution is bounded or not smooth. Like a histogram, the quality of the representation also depends on the selection of good smoothing parameters." [source](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) In other words, outliers can still negatively impact your charts.
 
-If you want to smooth out the jagged MaxBodyMass line from the second chart you created, you can recreate it using this method:
+If you want to revisit the jagged MaxBodyMass line from the second chart you created, you can smooth it out using this method:
 
 ```python
 sns.kdeplot(filteredBirds['MaxBodyMass'])
 plt.show()
 ```
-![smooth bodymass line](../../../../3-Data-Visualization/10-visualization-distributions/images/density2.png)
+![smooth bodymass line](../../../../translated_images/density2.8e7647257060ff544a1aaded57e8dd1887586bfe340139e9b77ac1e5287f7977.en.png)
 
-If you prefer a smoother but not overly smooth line, adjust the `bw_adjust` parameter:
+If you prefer a smooth but not overly smooth line, adjust the `bw_adjust` parameter:
 
 ```python
 sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2)
 plt.show()
 ```
-![less smooth bodymass line](../../../../3-Data-Visualization/10-visualization-distributions/images/density3.png)
+![less smooth bodymass line](../../../../translated_images/density3.84ae27da82f31e6b83ad977646f029a1d21186574d7581facd70123b3eb257ee.en.png)
 
 ✅ Explore the available parameters for this type of plot and experiment!
 
@@ -185,7 +184,7 @@ sns.kdeplot(
 )
 ```
 
-![bodymass per order](../../../../3-Data-Visualization/10-visualization-distributions/images/density4.png)
+![bodymass per order](../../../../translated_images/density4.e9d6c033f15c500fd33df94cb592b9f5cf1ed2a3d213c448a3f9e97ba39573ce.en.png)
 
 You can also map the density of multiple variables in one chart. Compare the MaxLength and MinLength of a bird to its conservation status:
 
@@ -193,15 +192,15 @@ You can also map the density of multiple variables in one chart. Compare the Max
 sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationStatus")
 ```
 
-![multiple densities, superimposed](../../../../3-Data-Visualization/10-visualization-distributions/images/multi.png)
+![multiple densities, superimposed](../../../../translated_images/multi.56548caa9eae8d0fd9012a8586295538c7f4f426e2abc714ba070e2e4b1fc2c1.en.png)
 
-It might be worth investigating whether the cluster of 'Vulnerable' birds based on their lengths is significant.
+It might be worth investigating whether the cluster of 'Vulnerable' birds based on their lengths has any significance.
 
 ## 🚀 Challenge
 
 Histograms are a more advanced type of chart compared to basic scatterplots, bar charts, or line charts. Search online for examples of histograms. How are they used, what do they demonstrate, and in which fields or areas of study are they commonly applied?
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/19)
 
 ## Review & Self Study
 
@@ -214,4 +213,4 @@ In this lesson, you used Matplotlib and began working with Seaborn to create mor
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/3-Data-Visualization/11-visualization-proportions/README.md b/translations/en/3-Data-Visualization/11-visualization-proportions/README.md
index 83ebd5f8..b7685cc8 100644
--- a/translations/en/3-Data-Visualization/11-visualization-proportions/README.md
+++ b/translations/en/3-Data-Visualization/11-visualization-proportions/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "cc490897ee2d276870472bcb31602d03",
-  "translation_date": "2025-09-05T07:42:52+00:00",
+  "original_hash": "42119bcc97bee88254e381156d770f3c",
+  "translation_date": "2025-09-06T10:09:04+00:00",
   "source_file": "3-Data-Visualization/11-visualization-proportions/README.md",
   "language_code": "en"
 }
@@ -13,19 +13,19 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 |Visualizing Proportions - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-In this lesson, you'll work with a nature-focused dataset to visualize proportions, such as the distribution of different types of fungi in a dataset about mushrooms. We'll dive into these fascinating fungi using a dataset from Audubon that details 23 species of gilled mushrooms in the Agaricus and Lepiota families. You'll experiment with fun visualizations like:
+In this lesson, you'll work with a nature-focused dataset to visualize proportions, such as the distribution of different types of fungi in a dataset about mushrooms. We'll dive into these fascinating fungi using a dataset from Audubon that provides details about 23 species of gilled mushrooms in the Agaricus and Lepiota families. You'll experiment with fun visualizations like:
 
 - Pie charts 🥧
 - Donut charts 🍩
 - Waffle charts 🧇
 
-> 💡 Microsoft Research has an interesting project called [Charticulator](https://charticulator.com), which provides a free drag-and-drop interface for creating data visualizations. One of their tutorials uses this mushroom dataset! You can explore the data and learn the library simultaneously: [Charticulator tutorial](https://charticulator.com/tutorials/tutorial4.html).
+> 💡 A fascinating project called [Charticulator](https://charticulator.com) by Microsoft Research offers a free drag-and-drop interface for creating data visualizations. One of their tutorials even uses this mushroom dataset! This means you can explore the data while learning the library: [Charticulator tutorial](https://charticulator.com/tutorials/tutorial4.html).
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/20)
 
 ## Get to know your mushrooms 🍄
 
-Mushrooms are fascinating. Let's import a dataset to study them:
+Mushrooms are truly fascinating. Let's import a dataset to study them:
 
 ```python
 import pandas as pd
@@ -59,7 +59,7 @@ Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
        'ring-type', 'spore-print-color', 'population', 'habitat'],
       dtype='object')
 ```
-Convert the 'class' column into a category:
+Now, convert the 'class' column into a category:
 
 ```python
 cols = mushrooms.select_dtypes(["object"]).columns
@@ -71,7 +71,7 @@ edibleclass=mushrooms.groupby(['class']).count()
 edibleclass
 ```
 
-Now, if you print the mushrooms data, you'll see it has been grouped into categories based on the poisonous/edible class:
+When you print out the mushrooms data, you'll see that it has been grouped into categories based on the poisonous/edible class:
 
 |           | cap-shape | cap-surface | cap-color | bruises | odor | gill-attachment | gill-spacing | gill-size | gill-color | stalk-shape | ... | stalk-surface-below-ring | stalk-color-above-ring | stalk-color-below-ring | veil-type | veil-color | ring-number | ring-type | spore-print-color | population | habitat |
 | --------- | --------- | ----------- | --------- | ------- | ---- | --------------- | ------------ | --------- | ---------- | ----------- | --- | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- |
@@ -91,13 +91,13 @@ plt.show()
 ```
 And there you have it—a pie chart showing the proportions of the data based on the two mushroom classes. It's crucial to get the order of the labels correct, especially here, so double-check the order when building the label array!
 
-![pie chart](../../../../3-Data-Visualization/11-visualization-proportions/images/pie1-wb.png)
+![pie chart](../../../../translated_images/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.en.png)
 
 ## Donuts!
 
-A visually appealing variation of a pie chart is a donut chart, which is essentially a pie chart with a hole in the center. Let's use this method to examine our data.
+A slightly more visually appealing pie chart is a donut chart, which is essentially a pie chart with a hole in the center. Let's use this method to examine our data.
 
-Look at the various habitats where mushrooms grow:
+Take a look at the various habitats where mushrooms grow:
 
 ```python
 habitat=mushrooms.groupby(['habitat']).count()
@@ -121,7 +121,7 @@ plt.title('Mushroom Habitats')
 plt.show()
 ```
 
-![donut chart](../../../../3-Data-Visualization/11-visualization-proportions/images/donut-wb.png)
+![donut chart](../../../../translated_images/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.en.png)
 
 This code draws a chart and a center circle, then adds the center circle to the chart. You can adjust the width of the center circle by changing `0.40` to another value.
 
@@ -131,7 +131,7 @@ Now that you know how to group your data and display it as a pie or donut chart,
 
 ## Waffles!
 
-A waffle chart is a unique way to visualize quantities as a 2D array of squares. Let's visualize the different quantities of mushroom cap colors in this dataset. To do this, you'll need to install a helper library called [PyWaffle](https://pypi.org/project/pywaffle/) and use Matplotlib:
+A waffle chart is a unique way to visualize quantities as a 2D array of squares. Let's use this method to visualize the different quantities of mushroom cap colors in the dataset. To do this, you'll need to install a helper library called [PyWaffle](https://pypi.org/project/pywaffle/) and use Matplotlib:
 
 ```python
 pip install pywaffle
@@ -167,18 +167,19 @@ fig = plt.figure(
 )
 ```
 
-Using a waffle chart, you can clearly see the proportions of cap colors in this mushroom dataset. Interestingly, there are many green-capped mushrooms!
+Using a waffle chart, you can clearly see the proportions of cap colors in the mushroom dataset. Interestingly, there are many green-capped mushrooms!
 
-![waffle chart](../../../../3-Data-Visualization/11-visualization-proportions/images/waffle.png)
+![waffle chart](../../../../translated_images/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.en.png)
 
-✅ PyWaffle supports icons within the charts, allowing you to use any icon available in [Font Awesome](https://fontawesome.com/). Experiment with creating a more engaging waffle chart using icons instead of squares.
+✅ PyWaffle supports icons within the charts, allowing you to use any icon available in [Font Awesome](https://fontawesome.com/). Experiment with creating an even more engaging waffle chart using icons instead of squares.
 
-In this lesson, you learned three ways to visualize proportions. First, group your data into categories, then decide the best way to display it—pie, donut, or waffle. All are visually appealing and provide an instant snapshot of the dataset.
+In this lesson, you learned three ways to visualize proportions. First, group your data into categories, then decide which visualization method—pie, donut, or waffle—best represents the data. All three are visually appealing and provide an instant snapshot of the dataset.
 
 ## 🚀 Challenge
 
-Try recreating these charts in [Charticulator](https://charticulator.com).
-## [Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/21)
+Try recreating these fun charts in [Charticulator](https://charticulator.com).
+
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/21)
 
 ## Review & Self Study
 
@@ -201,4 +202,4 @@ Do some research to find more information on this tricky decision.
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/3-Data-Visualization/12-visualization-relationships/README.md b/translations/en/3-Data-Visualization/12-visualization-relationships/README.md
index bab34248..693054b3 100644
--- a/translations/en/3-Data-Visualization/12-visualization-relationships/README.md
+++ b/translations/en/3-Data-Visualization/12-visualization-relationships/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "b29e427401499e81f4af55a8c4afea76",
-  "translation_date": "2025-09-05T07:44:29+00:00",
+  "original_hash": "44de95649fcec43643cbe3962f904331",
+  "translation_date": "2025-09-06T10:10:30+00:00",
   "source_file": "3-Data-Visualization/12-visualization-relationships/README.md",
   "language_code": "en"
 }
@@ -13,21 +13,21 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 |Visualizing Relationships - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-Continuing with the nature focus of our research, let's explore some fascinating visualizations to illustrate the relationships between different types of honey, based on a dataset from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php).
+Continuing with the nature focus of our research, let's explore fascinating ways to visualize the relationships between different types of honey, based on a dataset from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php).
 
 This dataset, containing around 600 entries, showcases honey production across various U.S. states. For instance, you can examine the number of colonies, yield per colony, total production, stocks, price per pound, and the value of honey produced in a specific state from 1998 to 2012, with one row per year for each state.
 
-It would be intriguing to visualize the relationship between a state's annual production and, for example, the price of honey in that state. Alternatively, you could explore the relationship between states' honey yield per colony. This time period includes the emergence of the devastating 'CCD' or 'Colony Collapse Disorder,' first observed in 2006 (http://npic.orst.edu/envir/ccd.html), making this dataset particularly significant. 🐝
+It would be intriguing to visualize the relationship between a state's annual production and, for example, the price of honey in that state. Alternatively, you could explore the relationship between honey yield per colony across states. This time period includes the emergence of the devastating 'CCD' or 'Colony Collapse Disorder' first identified in 2006 (http://npic.orst.edu/envir/ccd.html), making this dataset particularly significant to study. 🐝
 
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/22)
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/22)
 
-In this lesson, you’ll use Seaborn, a library you’ve worked with before, to visualize relationships between variables. A particularly useful feature is Seaborn's `relplot` function, which enables scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships).' This helps data scientists better understand how variables interact.
+In this lesson, you'll use Seaborn, a library you've worked with before, to effectively visualize relationships between variables. One particularly useful function in Seaborn is `relplot`, which enables scatter plots and line plots to quickly illustrate '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', helping data scientists better understand how variables interact.
 
 ## Scatterplots
 
-Use a scatterplot to illustrate how the price of honey has changed year over year in each state. Seaborn's `relplot` makes it easy to group state data and display both categorical and numerical data points.
+Use a scatterplot to show how the price of honey has changed year over year, for each state. Seaborn's `relplot` conveniently organizes state data and displays data points for both categorical and numeric data.
 
-Let’s begin by importing the data and Seaborn:
+Let's begin by importing the data and Seaborn:
 
 ```python
 import pandas as pd
@@ -36,7 +36,7 @@ import seaborn as sns
 honey = pd.read_csv('../../data/honey.csv')
 honey.head()
 ```
-You’ll notice that the honey dataset contains several interesting columns, including year and price per pound. Let’s explore this data, grouped by U.S. state:
+You'll notice that the honey dataset includes several interesting columns, such as year and price per pound. Let's examine this data, grouped by U.S. state:
 
 | state | numcol | yieldpercol | totalprod | stocks   | priceperlb | prodvalue | year |
 | ----- | ------ | ----------- | --------- | -------- | ---------- | --------- | ---- |
@@ -46,23 +46,23 @@ You’ll notice that the honey dataset contains several interesting columns, inc
 | CA    | 450000 | 83          | 37350000  | 12326000 | 0.62       | 23157000  | 1998 |
 | CO    | 27000  | 72          | 1944000   | 1594000  | 0.7        | 1361000   | 1998 |
 
-Create a basic scatterplot to show the relationship between the price per pound of honey and its state of origin. Adjust the `y` axis to ensure all states are visible:
+Create a basic scatterplot to show the relationship between the price per pound of honey and its U.S. state of origin. Adjust the `y` axis to ensure all states are visible:
 
 ```python
 sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);
 ```
-![scatterplot 1](../../../../3-Data-Visualization/12-visualization-relationships/images/scatter1.png)
+![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.en.png)
 
-Next, use a honey-inspired color scheme to show how the price evolves over the years. Add a 'hue' parameter to highlight year-over-year changes:
+Next, use a honey-inspired color scheme to illustrate how the price evolves over the years. Add a 'hue' parameter to highlight year-over-year changes:
 
 > ✅ Learn more about the [color palettes you can use in Seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html) - try a beautiful rainbow color scheme!
 
 ```python
 sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5);
 ```
-![scatterplot 2](../../../../3-Data-Visualization/12-visualization-relationships/images/scatter2.png)
+![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.en.png)
 
-With this color scheme, you can clearly see a strong upward trend in honey prices over the years. For example, if you examine Arizona's data, you’ll notice a consistent pattern of price increases year over year, with only a few exceptions:
+With this color scheme, you can clearly see a strong upward trend in honey prices over the years. If you examine a specific state, such as Arizona, you can observe a consistent pattern of price increases year over year, with only a few exceptions:
 
 | state | numcol | yieldpercol | totalprod | stocks  | priceperlb | prodvalue | year |
 | ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- |
@@ -82,31 +82,31 @@ With this color scheme, you can clearly see a strong upward trend in honey price
 | AZ    | 23000  | 53          | 1219000   | 427000  | 1.55       | 1889000   | 2011 |
 | AZ    | 22000  | 46          | 1012000   | 253000  | 1.79       | 1811000   | 2012 |
 
-Another way to visualize this trend is by using size instead of color. For colorblind users, this might be a better option. Modify your visualization to show price increases through larger dot sizes:
+Another way to visualize this trend is by using size instead of color. For colorblind users, this might be a better option. Modify your visualization to represent price increases with larger dot sizes:
 
 ```python
 sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspect=.5);
 ```
-You’ll notice the dots gradually increasing in size.
+You can observe the dots growing larger over time.
 
-![scatterplot 3](../../../../3-Data-Visualization/12-visualization-relationships/images/scatter3.png)
+![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.en.png)
 
-Could this simply be a case of supply and demand? Are factors like climate change and colony collapse reducing the honey supply year over year, leading to higher prices?
+Is this simply a case of supply and demand? Could factors like climate change and colony collapse be reducing the availability of honey year over year, leading to price increases?
 
-To investigate correlations between variables in this dataset, let’s explore some line charts.
+To investigate correlations between variables in this dataset, let's explore some line charts.
 
 ## Line charts
 
-Question: Is there a clear upward trend in honey prices per pound year over year? A single line chart can help answer this:
+Question: Is there a clear upward trend in honey prices per pound year over year? The simplest way to find out is by creating a single line chart:
 
 ```python
 sns.relplot(x="year", y="priceperlb", kind="line", data=honey);
 ```
 Answer: Yes, although there are some exceptions around 2003:
 
-![line chart 1](../../../../3-Data-Visualization/12-visualization-relationships/images/line1.png)
+![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.en.png)
 
-✅ Seaborn aggregates data into one line, displaying "multiple measurements at each x value by plotting the mean and the 95% confidence interval around the mean." [Source](https://seaborn.pydata.org/tutorial/relational.html). You can disable this behavior by adding `ci=None`.
+✅ Seaborn aggregates data into one line, displaying "multiple measurements at each x value by plotting the mean and the 95% confidence interval around the mean." [Source](https://seaborn.pydata.org/tutorial/relational.html). You can disable this time-consuming behavior by adding `ci=None`.
 
 Question: In 2003, was there also a spike in honey supply? What happens if you examine total production year over year?
 
@@ -114,19 +114,19 @@ Question: In 2003, was there also a spike in honey supply? What happens if you e
 sns.relplot(x="year", y="totalprod", kind="line", data=honey);
 ```
 
-![line chart 2](../../../../3-Data-Visualization/12-visualization-relationships/images/line2.png)
+![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.en.png)
 
-Answer: Not really. Total production actually seems to have increased in 2003, even though honey production generally declined during these years.
+Answer: Not really. Total production seems to have increased in 2003, even though overall honey production has been declining during these years.
 
-Question: If not supply, what could have caused the price spike in 2003?
+Question: In that case, what might have caused the spike in honey prices around 2003?
 
-To investigate, let’s use a facet grid.
+To investigate further, let's use a facet grid.
 
 ## Facet grids
 
-Facet grids allow you to break down your dataset into smaller subsets (facets). For example, you can use 'year' as a facet to avoid creating too many grids. Seaborn can then plot each facet side by side for easier comparison. Does 2003 stand out in this comparison?
+Facet grids allow you to focus on one aspect of your dataset (e.g., 'year') and create a plot for each facet of your chosen x and y coordinates, making comparisons easier. Does 2003 stand out in this type of visualization?
 
-Create a facet grid using `relplot` as recommended in [Seaborn's documentation](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid).
+Create a facet grid using `relplot`, as recommended by [Seaborn's documentation](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid).
 
 ```python
 sns.relplot(
@@ -136,15 +136,15 @@ sns.relplot(
     col_wrap=3,
     kind="line"
 ```
-In this visualization, you can compare yield per colony and number of colonies year over year, with columns wrapped at 3:
+In this visualization, compare yield per colony and number of colonies year over year, with columns wrapped at 3:
 
-![facet grid](../../../../3-Data-Visualization/12-visualization-relationships/images/facet.png)
+![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.en.png)
 
-For this dataset, nothing particularly stands out regarding the number of colonies and their yield year over year or state by state. Is there another way to explore correlations between these variables?
+For this dataset, nothing particularly stands out regarding the number of colonies and their yield year over year or state over state. Is there another way to explore correlations between these variables?
 
 ## Dual-line Plots
 
-Try a multiline plot by overlaying two line plots, using Seaborn's 'despine' to remove the top and right spines, and `ax.twinx` [from Matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html). Twinx allows you to share the x-axis while displaying two y-axes. Plot yield per colony and number of colonies together:
+Try a multiline plot by overlaying two line plots, using Seaborn's 'despine' to remove the top and right spines, and `ax.twinx` [from Matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html). Twinx allows a chart to share the x-axis while displaying two y-axes. Display yield per colony and number of colonies, superimposed:
 
 ```python
 fig, ax = plt.subplots(figsize=(12,6))
@@ -161,22 +161,22 @@ sns.despine(right=False)
 plt.ylabel('colony yield')
 ax.figure.legend();
 ```
-![superimposed plots](../../../../3-Data-Visualization/12-visualization-relationships/images/dual-line.png)
+![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.en.png)
 
-While nothing particularly stands out around 2003, this visualization ends the lesson on a slightly positive note: although the number of colonies is declining overall, it seems to be stabilizing, even if their yield per colony is decreasing.
+While nothing particularly stands out around 2003, this visualization ends the lesson on a slightly positive note: although the number of colonies is declining overall, it appears to be stabilizing, even if their yield per colony is decreasing.
 
 Go, bees, go!
 
 🐝❤️
 ## 🚀 Challenge
 
-In this lesson, you explored scatterplots, line grids, and facet grids. Challenge yourself to create a facet grid using a different dataset, perhaps one you’ve used in previous lessons. Pay attention to how long it takes to create and how to manage the number of grids effectively.
+In this lesson, you explored scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, perhaps one you've worked with in previous lessons. Note how long it takes to create and consider how many grids are practical to draw using these techniques.
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/23)
 
 ## Review & Self Study
 
-Line plots can range from simple to complex. Spend some time reading the [Seaborn documentation](https://seaborn.pydata.org/generated/seaborn.lineplot.html) to learn about the various ways to build them. Try enhancing the line charts you created in this lesson using other methods from the documentation.
+Line plots can range from simple to complex. Spend some time reading the [Seaborn documentation](https://seaborn.pydata.org/generated/seaborn.lineplot.html) to learn about the various ways to build them. Try enhancing the line charts you created in this lesson using methods described in the documentation.
 
 ## Assignment
 
@@ -185,4 +185,4 @@ Line plots can range from simple to complex. Spend some time reading the [Seabor
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md
index 861aedd4..e04ae195 100644
--- a/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md
+++ b/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "0b380bb6d34102bb061eb41de23d9834",
-  "translation_date": "2025-09-05T07:44:02+00:00",
+  "original_hash": "cfb068050337a36e348debaa502a24fa",
+  "translation_date": "2025-09-06T10:10:07+00:00",
   "source_file": "3-Data-Visualization/13-meaningful-visualizations/README.md",
   "language_code": "en"
 }
@@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 > "If you torture the data long enough, it will confess to anything" -- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase)
 
-One of the essential skills for a data scientist is the ability to create meaningful data visualizations that help answer questions. Before visualizing your data, you need to ensure it has been cleaned and prepared, as you learned in previous lessons. Once that's done, you can start deciding how best to present the data.
+One of the essential skills for a data scientist is the ability to create meaningful data visualizations that help answer specific questions. Before visualizing your data, you need to ensure it has been cleaned and prepared, as you learned in previous lessons. Once that's done, you can start deciding how best to present the data.
 
 In this lesson, you will explore:
 
@@ -26,13 +26,13 @@ In this lesson, you will explore:
 5. How to create animated or 3D visualizations
 6. How to design creative visualizations
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/24)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/24)
 
 ## Choose the right chart type
 
-In earlier lessons, you experimented with creating various data visualizations using Matplotlib and Seaborn. Generally, you can use the [right type of chart](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/) based on the question you're trying to answer, as shown in this table:
+In earlier lessons, you experimented with creating various data visualizations using Matplotlib and Seaborn. Generally, you can select the [appropriate chart type](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/) based on the question you're trying to answer using this table:
 
-| You need to:               | You should use:                 |
+| Task                        | Recommended Chart Type          |
 | -------------------------- | ------------------------------- |
 | Show data trends over time | Line                            |
 | Compare categories         | Bar, Pie                        |
@@ -41,45 +41,45 @@ In earlier lessons, you experimented with creating various data visualizations u
 | Show distributions         | Scatter, Histogram, Box         |
 | Show proportions           | Pie, Donut, Waffle              |
 
-> ✅ Depending on the structure of your data, you might need to convert it from text to numeric to make certain charts work.
+> ✅ Depending on the structure of your data, you may need to convert it from text to numeric format to make it compatible with certain chart types.
 
 ## Avoid deception
 
-Even when a data scientist carefully selects the right chart for the data, there are many ways data can be presented misleadingly to support a particular narrative, often at the expense of the data's integrity. There are countless examples of deceptive charts and infographics!
+Even when a data scientist carefully selects the right chart for the data, there are many ways data can be presented misleadingly to support a particular narrative, often at the expense of the data's integrity. There are numerous examples of deceptive charts and infographics!
 
-[![How Charts Lie by Alberto Cairo](../../../../3-Data-Visualization/13-meaningful-visualizations/images/tornado.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie")
+[![How Charts Lie by Alberto Cairo](../../../../translated_images/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.en.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie")
 
 > 🎥 Click the image above to watch a conference talk about misleading charts.
 
-This chart flips the X-axis to show the opposite of the truth based on the dates:
+This chart reverses the X-axis to present the opposite of the truth based on the dates:
 
-![bad chart 1](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-1.png)
+![bad chart 1](../../../../translated_images/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.en.png)
 
-[This chart](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) is even more misleading. At first glance, it appears that COVID cases have declined over time in various counties. However, upon closer inspection, the dates have been rearranged to create a false downward trend.
+[This chart](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) is even more misleading, as the eye is drawn to the right, suggesting that COVID cases have declined over time in various counties. However, upon closer inspection of the dates, you'll notice they have been rearranged to create a deceptive downward trend.
 
-![bad chart 2](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-2.jpg)
+![bad chart 2](../../../../translated_images/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.en.jpg)
 
 This infamous example uses both color and a flipped Y-axis to mislead viewers. Instead of showing that gun deaths increased after the passage of gun-friendly legislation, the chart tricks the eye into believing the opposite:
 
-![bad chart 3](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-3.jpg)
+![bad chart 3](../../../../translated_images/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.en.jpg)
 
 This peculiar chart demonstrates how proportions can be manipulated, often to humorous effect:
 
-![bad chart 4](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-4.jpg)
+![bad chart 4](../../../../translated_images/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.en.jpg)
 
-Another deceptive tactic is comparing things that aren't truly comparable. A [fascinating website](https://tylervigen.com/spurious-correlations) showcases 'spurious correlations,' such as the divorce rate in Maine being linked to margarine consumption. A Reddit group also collects [examples of poor data usage](https://www.reddit.com/r/dataisugly/top/?t=all).
+Another deceptive tactic is comparing things that are not comparable. There is a [fascinating website](https://tylervigen.com/spurious-correlations) dedicated to 'spurious correlations,' showcasing absurd 'facts' like the correlation between Maine's divorce rate and margarine consumption. A Reddit group also collects [examples of poor data usage](https://www.reddit.com/r/dataisugly/top/?t=all).
 
-It's crucial to understand how easily the eye can be tricked by misleading charts. Even with good intentions, a poorly chosen chart type—like a pie chart with too many categories—can lead to confusion.
+It's crucial to understand how easily the eye can be tricked by misleading charts. Even with good intentions, a poor choice of chart type—like a pie chart with too many categories—can lead to confusion.
 
 ## Color
 
-The 'Florida gun violence' chart above illustrates how color can add another layer of meaning to visualizations, especially when charts aren't created using libraries like Matplotlib or Seaborn, which offer pre-vetted color palettes. If you're designing a chart manually, take some time to study [color theory](https://colormatters.com/color-and-design/basic-color-theory).
+The 'Florida gun violence' chart above illustrates how color can add another layer of meaning to visualizations, especially when charts are not created using libraries like Matplotlib or Seaborn, which offer pre-designed color palettes. If you're designing a chart manually, take some time to study [color theory](https://colormatters.com/color-and-design/basic-color-theory).
 
-> ✅ Keep accessibility in mind when designing charts. Some users may be colorblind—does your chart work well for those with visual impairments?
+> ✅ Keep in mind that accessibility is a key aspect of visualization design. Some users may be colorblind—does your chart work well for those with visual impairments?
 
 Be cautious when selecting colors for your chart, as they can convey unintended meanings. For example, the 'pink ladies' in the 'height' chart above add a distinctly 'feminine' connotation, which contributes to the chart's oddness.
 
-While [color meanings](https://colormatters.com/color-symbolism/the-meanings-of-colors) can vary across cultures and change depending on the shade, here are some general associations:
+While [color meanings](https://colormatters.com/color-symbolism/the-meanings-of-colors) can vary across cultures and change depending on the shade, general associations include:
 
 | Color  | Meaning             |
 | ------ | ------------------- |
@@ -90,33 +90,33 @@ While [color meanings](https://colormatters.com/color-symbolism/the-meanings-of-
 | purple | happiness           |
 | orange | vibrance            |
 
-If you're tasked with creating a chart with custom colors, ensure that your choices align with the intended message and that the chart remains accessible.
+If you're tasked with creating a chart with custom colors, ensure that your design is both accessible and that the chosen colors align with the intended message.
 
 ## Styling your charts for readability
 
-Charts lose their value if they're not easy to read! Take time to adjust the width and height of your chart to ensure it scales well with your data. For example, if you're displaying data for all 50 states, consider showing them vertically on the Y-axis to avoid horizontal scrolling.
+Charts lose their value if they aren't easy to read! Take time to adjust the width and height of your chart to ensure it scales well with your data. For example, if you need to display all 50 states, consider showing them vertically on the Y-axis to avoid a horizontally scrolling chart.
 
 Label your axes, include a legend if necessary, and provide tooltips for better data comprehension.
 
-If your data includes verbose text on the X-axis, you can angle the text for improved readability. [Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) also supports 3D plotting if your data warrants it. Advanced visualizations can be created using `mpl_toolkits.mplot3d`.
+If your data includes verbose text on the X-axis, you can angle the text for improved readability. [Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) also supports 3D plotting if your data is suitable for it. Advanced visualizations can be created using `mpl_toolkits.mplot3d`.
 
-![3d plots](../../../../3-Data-Visualization/13-meaningful-visualizations/images/3d.png)
+![3d plots](../../../../translated_images/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.en.png)
 
 ## Animation and 3D chart display
 
-Some of the most compelling data visualizations today are animated. Shirley Wu has created stunning examples using D3, such as '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/),' where each flower represents a movie. Another example is 'Bussed Out,' an interactive visualization for the Guardian that combines Greensock and D3 with a scrollytelling article format to illustrate how NYC addresses homelessness by bussing people out of the city.
+Some of the most engaging data visualizations today are animated. Shirley Wu has created stunning examples using D3, such as '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/),' where each flower represents a movie. Another example is 'Bussed Out,' an interactive experience for the Guardian that combines visualizations with Greensock and D3, along with a scrollytelling article format, to illustrate how NYC addresses homelessness by bussing people out of the city.
 
-![busing](../../../../3-Data-Visualization/13-meaningful-visualizations/images/busing.png)
+![busing](../../../../translated_images/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.en.png)
 
 > "Bussed Out: How America Moves its Homeless" from [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizations by Nadieh Bremer & Shirley Wu
 
-While this lesson doesn't delve deeply into these powerful visualization libraries, you can experiment with D3 in a Vue.js app to create an animated social network visualization of the book "Dangerous Liaisons."
+While this lesson doesn't delve deeply into these powerful visualization libraries, you can experiment with D3 in a Vue.js app to create a visualization of the book "Dangerous Liaisons" as an animated social network.
 
-> "Les Liaisons Dangereuses" is an epistolary novel, presented as a series of letters. Written in 1782 by Choderlos de Laclos, it tells the story of the morally corrupt social maneuvers of two French aristocrats, the Vicomte de Valmont and the Marquise de Merteuil. Both meet their downfall, but not before causing significant social damage. The novel unfolds through letters written to various individuals in their circles, plotting revenge or simply creating chaos. Create a visualization of these letters to identify the key players in the narrative.
+> "Les Liaisons Dangereuses" is an epistolary novel, presented as a series of letters. Written in 1782 by Choderlos de Laclos, it tells the story of the morally corrupt social maneuvers of two French aristocrats, the Vicomte de Valmont and the Marquise de Merteuil. Both meet their downfall, but not before causing significant social damage. The novel unfolds through letters written to various individuals in their circles, plotting revenge or simply stirring trouble. Create a visualization of these letters to identify the key players in the narrative, visually.
 
-You will complete a web app that displays an animated view of this social network. It uses a library designed to create a [network visualization](https://github.com/emiliorizzo/vue-d3-network) with Vue.js and D3. Once the app is running, you can drag the nodes around to rearrange the data visually.
+You will complete a web app that displays an animated view of this social network. It uses a library designed to create a [network visualization](https://github.com/emiliorizzo/vue-d3-network) using Vue.js and D3. When the app is running, you can drag the nodes around the screen to rearrange the data.
 
-![liaisons](../../../../3-Data-Visualization/13-meaningful-visualizations/images/liaisons.png)
+![liaisons](../../../../translated_images/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.en.png)
 
 ## Project: Build a chart to show a network using D3.js
 
@@ -124,11 +124,11 @@ You will complete a web app that displays an animated view of this social networ
 
 1. Follow the instructions in the README.md file in the starter folder's root. Ensure you have NPM and Node.js installed on your machine before setting up the project's dependencies.
 
-2. Open the `starter/src` folder. Inside, you'll find an `assets` folder containing a .json file with all the letters from the novel, annotated with 'to' and 'from' fields.
+2. Open the `starter/src` folder. Inside, you'll find an `assets` folder containing a .json file with all the letters from the novel, numbered, with 'to' and 'from' annotations.
 
 3. Complete the code in `components/Nodes.vue` to enable the visualization. Locate the method called `createLinks()` and add the following nested loop.
 
-Loop through the .json object to extract the 'to' and 'from' data for the letters, building the `links` object for the visualization library:
+Loop through the .json object to extract the 'to' and 'from' data for the letters and build the `links` object for the visualization library:
 
 ```javascript
 //loop through letters
@@ -152,9 +152,9 @@ Run your app from the terminal (npm run serve) and enjoy the visualization!
 
 ## 🚀 Challenge
 
-Explore the internet to find examples of misleading visualizations. How does the author mislead the viewer, and is it intentional? Try correcting the visualizations to show how they should look.
+Explore the internet to find examples of misleading visualizations. How does the author mislead the viewer, and is it intentional? Try correcting the visualizations to show how they should appear.
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/25)
 
 ## Review & Self Study
 
@@ -168,7 +168,7 @@ Check out these interesting visualizations of historical assets and artifacts:
 
 https://handbook.pubpub.org/
 
-Read this article on how animation can enhance your visualizations:
+Read this article on how animation can enhance visualizations:
 
 https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4
 
@@ -179,4 +179,4 @@ https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md
index 9de94693..658e637b 100644
--- a/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md
+++ b/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "79ca8a5a3135e94d2d43f56ba62d5205",
-  "translation_date": "2025-09-05T07:41:32+00:00",
+  "original_hash": "07478c2092203a69087b9c76b1f4dd56",
+  "translation_date": "2025-09-06T10:08:05+00:00",
   "source_file": "4-Data-Science-Lifecycle/14-Introduction/README.md",
   "language_code": "en"
 }
@@ -13,9 +13,9 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 | Introduction to the Data Science Lifecycle - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/26)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/26)
 
-By now, you’ve likely realized that data science is a structured process. This process can be divided into five stages:
+By now, you’ve likely realized that data science is a process. This process can be divided into five stages:
 
 - Capturing
 - Processing
@@ -25,13 +25,13 @@ By now, you’ve likely realized that data science is a structured process. This
 
 This lesson focuses on three parts of the lifecycle: capturing, processing, and maintenance.
 
-![Diagram of the data science lifecycle](../../../../4-Data-Science-Lifecycle/14-Introduction/images/data-science-lifecycle.jpg)
+![Diagram of the data science lifecycle](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.en.jpg)
 > Image by [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/)
 
 ## Capturing
 
 The first stage of the lifecycle is crucial because the subsequent stages depend on it. It essentially combines two steps: acquiring the data and defining the purpose and problems to be addressed.  
-Defining the project’s goals requires a deeper understanding of the problem or question. First, you need to identify and engage with those who need their problem solved. These could be stakeholders in a business or project sponsors who can help determine who or what will benefit from the project, as well as what they need and why. A well-defined goal should be measurable and quantifiable to establish an acceptable outcome.
+Defining the project’s goals requires a deeper understanding of the problem or question. First, we need to identify and engage with those whose problem needs solving. These could be stakeholders in a business or project sponsors who can help determine who or what will benefit from the project, as well as what they need and why. A well-defined goal should be measurable and quantifiable to establish a clear definition of success.
 
 Questions a data scientist might ask:
 - Has this problem been tackled before? What was discovered?
@@ -39,9 +39,9 @@ Questions a data scientist might ask:
 - Is there any ambiguity, and how can it be reduced?
 - What are the constraints?
 - What might the end result look like?
-- What resources (time, personnel, computational) are available?
+- What resources (time, people, computational) are available?
 
-Next, you need to identify, collect, and explore the data required to achieve these defined goals. During the acquisition step, data scientists must also assess the quantity and quality of the data. This involves some data exploration to ensure that the acquired data will support achieving the desired outcome.
+Next, we identify, collect, and explore the data needed to achieve these defined goals. During the acquisition step, data scientists must also evaluate the quantity and quality of the data. This involves some data exploration to ensure that the acquired data will support achieving the desired outcome.
 
 Questions a data scientist might ask about the data:
 - What data is already available to me?
@@ -49,66 +49,66 @@ Questions a data scientist might ask about the data:
 - What are the privacy concerns?
 - Do I have enough data to solve this problem?
 - Is the data of sufficient quality for this problem?
-- If additional insights are discovered through this data, should the goals be reconsidered or redefined?
+- If additional insights are discovered through this data, should we consider revising or redefining the goals?
 
 ## Processing
 
-The processing stage of the lifecycle focuses on uncovering patterns in the data and building models. Some techniques used in this stage rely on statistical methods to identify patterns. For large datasets, this task is typically too time-consuming for humans and requires computers to handle the workload efficiently. This stage is also where data science intersects with machine learning. As you learned in the first lesson, machine learning involves building models to understand the data. Models represent the relationships between variables in the data and help predict outcomes.
+The processing stage of the lifecycle focuses on uncovering patterns in the data and building models. Some techniques used in this stage involve statistical methods to identify patterns. For large datasets, this would typically be a tedious task for a human, so computers are used to handle the heavy lifting and speed up the process. This stage is also where data science and machine learning intersect. As you learned in the first lesson, machine learning involves building models to understand the data. Models represent the relationships between variables in the data and help predict outcomes.
 
 Common techniques used in this stage are covered in the ML for Beginners curriculum. Follow the links to learn more about them:
 
 - [Classification](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification): Organizing data into categories for more efficient use.
 - [Clustering](https://github.com/microsoft/ML-For-Beginners/tree/main/5-Clustering): Grouping data into similar clusters.
-- [Regression](https://github.com/microsoft/ML-For-Beginners/tree/main/2-Regression): Identifying relationships between variables to predict or forecast values.
+- [Regression](https://github.com/microsoft/ML-For-Beginners/tree/main/2-Regression): Determining relationships between variables to predict or forecast values.
 
 ## Maintaining
 
-In the lifecycle diagram, you may notice that maintenance is positioned between capturing and processing. Maintenance is an ongoing process of managing, storing, and securing the data throughout the project and should be considered throughout its entirety.
+In the lifecycle diagram, you may have noticed that maintenance sits between capturing and processing. Maintenance is an ongoing process of managing, storing, and securing the data throughout the project and should be considered throughout the entire lifecycle.
 
 ### Storing Data
 
-Decisions about how and where data is stored can impact storage costs and the performance of data access. These decisions are unlikely to be made solely by a data scientist, but they may influence how the data is handled based on its storage method.
+Decisions about how and where data is stored can impact storage costs and the performance of data access. These decisions are unlikely to be made by a data scientist alone, but they may influence how the data scientist works with the data based on its storage.
 
 Here are some aspects of modern data storage systems that can affect these decisions:
 
-**On-premise vs off-premise vs public or private cloud**
+**On-premise vs. off-premise vs. public or private cloud**
 
-On-premise refers to hosting and managing data on your own equipment, such as owning a server with hard drives to store the data. Off-premise relies on equipment you don’t own, such as a data center. The public cloud is a popular choice for storing data, requiring no knowledge of how or where the data is stored. Public refers to a shared underlying infrastructure used by all cloud users. Some organizations have strict security policies requiring complete access to the equipment hosting their data and may opt for a private cloud that offers dedicated cloud services. You’ll learn more about cloud data in [later lessons](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/5-Data-Science-In-Cloud).
+On-premise refers to hosting and managing data on your own equipment, such as owning a server with hard drives to store the data. Off-premise relies on equipment you don’t own, such as a data center. The public cloud is a popular choice for storing data, requiring no knowledge of how or where the data is stored. Public refers to a shared underlying infrastructure used by all cloud users. Some organizations have strict security policies requiring complete control over the equipment where the data is hosted, so they rely on a private cloud that provides dedicated cloud services. You’ll learn more about data in the cloud in [later lessons](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/5-Data-Science-In-Cloud).
 
-**Cold vs hot data**
+**Cold vs. hot data**
 
-When training models, you may need more training data. Once satisfied with your model, additional data will arrive for the model to fulfill its purpose. In either case, the cost of storing and accessing data increases as more data accumulates. Separating rarely used data (cold data) from frequently accessed data (hot data) can be a cost-effective storage solution using hardware or software services. Accessing cold data may take longer compared to hot data.
+When training your models, you may need more training data. Once your model is finalized, additional data will still arrive for the model to fulfill its purpose. In either case, the cost of storing and accessing data will increase as more data accumulates. Separating rarely used data (cold data) from frequently accessed data (hot data) can be a more cost-effective storage solution, whether through hardware or software services. However, accessing cold data may take longer compared to hot data.
 
 ### Managing Data
 
-As you work with data, you may find that some of it needs cleaning using techniques covered in the lesson on [data preparation](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/08-data-preparation) to build accurate models. When new data arrives, similar techniques will need to be applied to maintain quality consistency. Some projects use automated tools for cleansing, aggregation, and compression before moving the data to its final location. Azure Data Factory is an example of such a tool.
+As you work with data, you may find that some of it needs cleaning using techniques covered in the lesson on [data preparation](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/08-data-preparation) to build accurate models. When new data arrives, it will require similar processing to maintain quality consistency. Some projects use automated tools for cleansing, aggregation, and compression before moving the data to its final location. Azure Data Factory is an example of such a tool.
 
 ### Securing the Data
 
-A key goal of securing data is ensuring that those working with it control what is collected and how it is used. Keeping data secure involves limiting access to only those who need it, adhering to local laws and regulations, and maintaining ethical standards, as discussed in the [ethics lesson](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/1-Introduction/02-ethics).
+One of the main goals of securing data is ensuring that those working with it have control over what is collected and how it is used. Keeping data secure involves limiting access to only those who need it, adhering to local laws and regulations, and maintaining ethical standards, as discussed in the [ethics lesson](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/1-Introduction/02-ethics).
 
-Here are some security measures a team might take:
-- Ensure all data is encrypted
-- Provide customers with information on how their data is used
-- Remove data access for individuals who leave the project
-- Restrict data modification to specific project members
+Here are some steps a team might take to ensure data security:
+- Ensure all data is encrypted.
+- Provide customers with information about how their data is used.
+- Remove data access for individuals who have left the project.
+- Restrict data modification to specific project members.
 
 ## 🚀 Challenge
 
-There are various versions of the Data Science Lifecycle, where steps may have different names and numbers of stages but include the same processes discussed in this lesson.
+There are many versions of the Data Science Lifecycle, where each step may have different names and numbers of stages but still include the same processes discussed in this lesson.
 
 Explore the [Team Data Science Process lifecycle](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle) and the [Cross-industry standard process for data mining](https://www.datascience-pm.com/crisp-dm-2/). Identify three similarities and differences between the two.
 
 |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)|
 |--|--|
-|![Team Data Science Lifecycle](../../../../4-Data-Science-Lifecycle/14-Introduction/images/tdsp-lifecycle2.png) | ![Data Science Process Alliance Image](../../../../4-Data-Science-Lifecycle/14-Introduction/images/CRISP-DM.png) |
+|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.en.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.en.png) |
 | Image by [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Image by [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) |
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/27)
 
 ## Review & Self Study
 
-Applying the Data Science Lifecycle involves multiple roles and tasks, with some focusing on specific parts of each stage. The Team Data Science Process provides resources explaining the roles and tasks involved in a project.
+Applying the Data Science Lifecycle involves multiple roles and tasks, with some focusing on specific parts of each stage. The Team Data Science Process provides resources that explain the roles and tasks involved in a project.
 
 * [Team Data Science Process roles and tasks](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/roles-tasks)
 * [Execute data science tasks: exploration, modeling, and deployment](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/execute-data-science-tasks)
diff --git a/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md
index c442bf31..01e462ae 100644
--- a/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md
+++ b/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md
@@ -1,8 +1,8 @@
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+  "original_hash": "2baeafe1db4d58ee5b8ec85db9de728a",
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@@ -15,11 +15,11 @@ CO_OP_TRANSLATOR_METADATA:
 
 ## Pre-Lecture Quiz
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/28)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/28)
 
-The "Analyzing" phase in the data lifecycle ensures that the data can address the questions posed or solve a specific problem. This step may also focus on verifying that a model is effectively tackling these questions and problems. This lesson centers on Exploratory Data Analysis (EDA), which involves techniques for identifying features and relationships within the data and preparing it for modeling.
+The "Analyzing" phase in the data lifecycle ensures that the data can address the questions posed or solve a specific problem. This step may also involve verifying that a model is effectively tackling these questions and issues. This lesson focuses on Exploratory Data Analysis (EDA), which includes techniques for identifying features and relationships within the data and preparing it for modeling.
 
-We'll use an example dataset from [Kaggle](https://www.kaggle.com/balaka18/email-spam-classification-dataset-csv/version/1) to demonstrate how this can be applied using Python and the Pandas library. This dataset includes counts of common words found in emails, with the sources of these emails anonymized. Use the [notebook](../../../../4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb) in this directory to follow along.
+We'll use an example dataset from [Kaggle](https://www.kaggle.com/balaka18/email-spam-classification-dataset-csv/version/1) to demonstrate how this can be done using Python and the Pandas library. The dataset contains counts of common words found in emails, with the sources of these emails anonymized. Use the [notebook](notebook.ipynb) in this directory to follow along.
 
 ## Exploratory Data Analysis
 
@@ -32,24 +32,24 @@ A data scientist might ask the following questions when acquiring data:
 Exploratory Data Analysis is the process of familiarizing yourself with the data and can help answer these questions, as well as identify challenges associated with the dataset. Let’s explore some techniques used to achieve this.
 
 ## Data Profiling, Descriptive Statistics, and Pandas
-How can we determine if we have enough data to solve the problem? Data profiling provides a summary and general overview of the dataset using descriptive statistics techniques. Data profiling helps us understand what is available, while descriptive statistics help us understand the quantity and characteristics of the data.
+How can we determine if we have enough data to solve the problem? Data profiling provides a summary and general overview of the dataset using descriptive statistics techniques. Data profiling helps us understand what is available, while descriptive statistics help us understand how much is available.
 
-In previous lessons, we used Pandas to generate descriptive statistics with the [`describe()` function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html). This function provides the count, maximum and minimum values, mean, standard deviation, and quantiles for numerical data. Using descriptive statistics like `describe()` can help you evaluate whether you have sufficient data or need more.
+In previous lessons, we used Pandas to generate descriptive statistics with the [`describe()` function]( https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html). This function provides the count, maximum and minimum values, mean, standard deviation, and quantiles for numerical data. Using descriptive statistics like the `describe()` function can help you evaluate whether you have sufficient data or need more.
 
 ## Sampling and Querying
-Exploring every detail in a large dataset can be time-consuming and is often left to computers. However, sampling is a useful technique for gaining a better understanding of the data and what it represents. By working with a sample, you can apply probability and statistics to draw general conclusions about the dataset. While there’s no strict rule for how much data to sample, it’s important to note that larger samples lead to more accurate generalizations.
+Exploring every detail in a large dataset can be time-consuming and is often left to computers. However, sampling is a useful technique for gaining a better understanding of the data and what it represents. With a sample, you can apply probability and statistics to draw general conclusions about the dataset. While there’s no strict rule for how much data to sample, it’s important to note that larger samples lead to more accurate generalizations about the data.
 
-Pandas includes the [`sample()` function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html), which allows you to specify the number of random samples you want to extract and analyze.
+Pandas includes the [`sample()` function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html), which allows you to specify the number of random samples you want to retrieve and use.
 
 General querying of the data can help answer specific questions or test theories you may have. Unlike sampling, queries allow you to focus on particular parts of the dataset that are relevant to your questions. The [`query()` function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html) in the Pandas library lets you select columns and retrieve rows to answer specific questions about the data.
 
 ## Exploring with Visualizations
-You don’t need to wait until the data is fully cleaned and analyzed to start creating visualizations. In fact, visual representations during exploration can help identify patterns, relationships, and issues within the data. Additionally, visualizations provide a way to communicate findings to stakeholders who may not be directly involved in data management. This can also be an opportunity to address new questions that weren’t considered during the "Capture" phase. Refer to the [section on Visualizations](../../../../../../../../../3-Data-Visualization) to learn more about popular methods for visual exploration.
+You don’t need to wait until the data is fully cleaned and analyzed to start creating visualizations. In fact, visual representations during exploration can help identify patterns, relationships, and issues within the data. Additionally, visualizations provide a way to communicate findings to stakeholders who may not be directly involved in data management. This can also be an opportunity to address additional questions that weren’t considered during the "Capture" phase. Refer to the [section on Visualizations](../../../../../../../../../3-Data-Visualization) to learn more about popular methods for visual exploration.
 
 ## Exploring to Identify Inconsistencies
-The techniques covered in this lesson can help identify missing or inconsistent values, but Pandas also offers specific functions for detecting these issues. [isna() or isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) can be used to check for missing values. An important aspect of exploring these values is understanding why they occurred in the first place. This insight can guide you in deciding what [actions to take to resolve them](../../../../../../../../../2-Working-With-Data/08-data-preparation/notebook.ipynb).
+The techniques covered in this lesson can help identify missing or inconsistent values, but Pandas also provides specific functions for this purpose. [isna() or isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) can be used to check for missing values. An important aspect of exploring these values is understanding why they are missing in the first place. This insight can guide you in deciding what [actions to take to address them](/2-Working-With-Data/08-data-preparation/notebook.ipynb).
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/29)
 
 ## Assignment
 
diff --git a/translations/en/4-Data-Science-Lifecycle/16-communication/README.md b/translations/en/4-Data-Science-Lifecycle/16-communication/README.md
index d392e915..8af5f59c 100644
--- a/translations/en/4-Data-Science-Lifecycle/16-communication/README.md
+++ b/translations/en/4-Data-Science-Lifecycle/16-communication/README.md
@@ -1,8 +1,8 @@
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+  "original_hash": "e4eae0345bfb6c9c936fca47081e57f8",
+  "translation_date": "2025-09-06T10:08:26+00:00",
   "source_file": "4-Data-Science-Lifecycle/16-communication/README.md",
   "language_code": "en"
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@@ -13,26 +13,26 @@ CO_OP_TRANSLATOR_METADATA:
 |:---:|
 | Data Science Lifecycle: Communication - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/30)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/30)
 
-Test your knowledge of the upcoming content with the Pre-Lecture Quiz above!
+Test your knowledge of what's coming up with the Pre-Lecture Quiz above!
 
 # Introduction
 
 ### What is Communication?
-Let’s begin this lesson by defining communication. **To communicate is to share or exchange information.** This information can include ideas, thoughts, feelings, messages, signals, data—anything that a **_sender_** (someone sharing information) wants a **_receiver_** (someone receiving information) to understand. In this lesson, we’ll refer to senders as communicators and receivers as the audience.
+Let’s begin this lesson by defining communication. **To communicate is to share or exchange information.** Information can include ideas, thoughts, feelings, messages, signals, data—anything that a **_sender_** (someone sharing information) wants a **_receiver_** (someone receiving information) to understand. In this lesson, we’ll refer to senders as communicators and receivers as the audience.
 
 ### Data Communication & Storytelling
-When communicating, the goal is to share or exchange information. However, when communicating data, the goal isn’t just to pass along numbers. Instead, the aim is to tell a story informed by your data—effective data communication and storytelling go hand-in-hand. Your audience is more likely to remember a story than a set of numbers. Later in this lesson, we’ll explore ways to use storytelling to communicate your data more effectively.
+When communicating, the goal is to share or exchange information. However, when communicating data, your goal shouldn’t be to simply present numbers to your audience. Instead, you should aim to tell a story informed by your data—effective data communication and storytelling go hand-in-hand. Your audience is more likely to remember a story you tell than a number you provide. Later in this lesson, we’ll explore ways to use storytelling to communicate your data more effectively.
 
 ### Types of Communication
 This lesson will cover two types of communication: One-Way Communication and Two-Way Communication.
 
-**One-way communication** occurs when a sender shares information with a receiver without expecting feedback or a response. Examples include mass emails, news broadcasts, or TV commercials. In these cases, the sender’s goal is simply to deliver information, not to engage in an exchange.
+**One-way communication** occurs when a sender shares information with a receiver without expecting feedback or a response. Examples of one-way communication include mass emails, news broadcasts, or television commercials. In these cases, the sender’s goal is to deliver information, not to engage in an exchange.
 
-**Two-way communication** involves all parties acting as both senders and receivers. A sender shares information, and the receiver provides feedback or a response. This is the type of communication we typically associate with conversations—whether in person, over the phone, via social media, or through text messages.
+**Two-way communication** happens when all parties act as both senders and receivers. A sender shares information, and the receiver provides feedback or a response. This is the type of communication we typically associate with conversations—whether in person, over the phone, on social media, or via text.
 
-When communicating data, you may use one-way communication (e.g., presenting at a conference or to a large group where questions aren’t asked immediately) or two-way communication (e.g., persuading stakeholders or convincing a teammate to invest time and effort in a new initiative).
+When communicating data, you may use one-way communication (e.g., presenting at a conference or to a large group where questions won’t be asked immediately) or two-way communication (e.g., persuading stakeholders for buy-in or convincing a teammate to invest time and effort in a new initiative).
 
 # Effective Communication
 
@@ -46,110 +46,119 @@ How do you tell a story with data? There are countless ways, but here are six st
 4. Use Meaningful Words & Phrases
 5. Use Emotion
 
-Each strategy is explained in detail below.
+Each of these strategies is explained in detail below.
 
 ### 1. Understand Your Audience, Your Channel & Your Communication Method
-The way you communicate with family members is likely different from how you communicate with friends. You use words and phrases tailored to the people you’re speaking to. The same principle applies when communicating data. Consider who your audience is, their goals, and the context they have regarding the situation you’re explaining.
+The way you communicate with family members is likely different from how you communicate with friends. You probably use different words and phrases tailored to the people you’re speaking to. The same principle applies when communicating data. Consider who your audience is, their goals, and the context they have regarding the situation you’re explaining.
 
-You can often categorize your audience into one of five groups, as outlined by Dell Executive Strategist Jim Stikeleather in a _Harvard Business Review_ article, “[How to Tell a Story with Data](http://blogs.hbr.org/2013/04/how-to-tell-a-story-with-data/).”
+You can often categorize your audience into groups. In a _Harvard Business Review_ article, “[How to Tell a Story with Data](http://blogs.hbr.org/2013/04/how-to-tell-a-story-with-data/),” Dell Executive Strategist Jim Stikeleather identifies five audience categories:
 
- - **Novice**: First exposure to the subject, but doesn’t want oversimplification.
- - **Generalist**: Familiar with the topic, seeking an overview and major themes.
- - **Managerial**: Requires actionable understanding of details and interrelationships.
- - **Expert**: Prefers exploration and discovery, with less emphasis on storytelling.
- - **Executive**: Focused on significance and conclusions, with limited time.
+- **Novice**: First exposure to the subject but doesn’t want oversimplification.
+- **Generalist**: Familiar with the topic but seeking an overview and major themes.
+- **Managerial**: Requires actionable understanding of details and interrelationships.
+- **Expert**: Prefers exploration and discovery with less storytelling and more detail.
+- **Executive**: Focused on significance and conclusions, with limited time.
 
 These categories can guide how you present data to your audience.
 
 Additionally, consider the channel you’re using to communicate—whether it’s a memo, email, meeting, or conference presentation. Your approach should adapt accordingly.
 
-Finally, understand whether you’ll be using one-way or two-way communication. For example:
-- If your audience is primarily Novice and you’re using one-way communication, you’ll need to educate them, provide context, and clearly explain your data and its significance.
-- If your audience is primarily Managerial and you’re using two-way communication, you can likely skip the context and dive straight into the data. However, you’ll need to manage timing and keep the discussion focused on your story, as questions may arise that could derail the conversation.
+Understanding whether you’ll use one-way or two-way communication is also crucial. For example:
+- If your audience is primarily Novice and you’re using one-way communication, you’ll need to educate them, provide context, present your data, and explain its significance clearly, as they won’t be able to ask questions.
+- If your audience is primarily Managerial and you’re using two-way communication, you can skip extensive context and dive into the data and its implications. However, you’ll need to manage timing and keep the discussion focused, as questions may arise that could derail your story.
 
 ### 2. Begin With The End In Mind
-Starting with the end in mind means identifying your intended takeaways for your audience before you begin communicating. This helps you craft a coherent story that your audience can follow. This approach works for both one-way and two-way communication.
+Starting with the end in mind means identifying your intended takeaways for your audience before you begin communicating. Being clear about what you want your audience to learn can help you craft a coherent story. This approach works for both one-way and two-way communication.
 
 How do you start with the end in mind? Write down your key takeaways before you begin. As you prepare your story, continually ask yourself, “How does this fit into the story I’m telling?”
 
-Be cautious, though—don’t cherry-pick data. Cherry-picking occurs when a communicator only shares data that supports their point while ignoring other data. If some of your data contradicts your intended takeaways, share it anyway. Be transparent with your audience about why you’re sticking to your story despite conflicting data.
+A word of caution: While starting with the end in mind is ideal, avoid cherry-picking data—selecting only the information that supports your intended takeaways while ignoring the rest. If some of your data contradicts your takeaways, share it honestly and explain why you’re sticking with your story despite the conflicting evidence.
 
 ### 3. Approach It Like an Actual Story
-Traditional stories follow five phases: Exposition, Rising Action, Climax, Falling Action, and Denouement—or, more simply, Context, Conflict, Climax, Closure, and Conclusion. You can use a similar structure when communicating your data.
+Traditional stories often follow five phases: Exposition, Rising Action, Climax, Falling Action, and Denouement—or, more simply, Context, Conflict, Climax, Closure, and Conclusion. You can use a similar structure when communicating your data and story.
 
-Start with context to ensure your audience is on the same page. Introduce the conflict—why did you collect this data? What problem were you trying to solve? Then present the climax—what does the data reveal? What solutions does it suggest? Follow with closure, reiterating the problem and proposed solutions. Finally, conclude by summarizing key takeaways and recommending next steps.
+Start with context to ensure your audience is on the same page. Introduce the conflict—why you collected the data and the problem you aimed to solve. Present the climax—what the data reveals, its implications, and potential solutions. Move to closure, reiterating the problem and proposed solutions. Finally, conclude by summarizing key takeaways and recommending next steps.
 
 ### 4. Use Meaningful Words & Phrases
-Vague language can lead to confusion. For example, if you say, “Our users take a long time to onboard onto our platform,” your audience might interpret “long time” differently—some might think an hour, others a week. Instead, say, “Our users take, on average, 3 minutes to sign up and onboard onto our platform.”
+Imagine you’re working on a product, and you say, “Our users take a long time to onboard onto our platform.” How long is “a long time”? An hour? A week? It’s unclear. Now imagine saying this to an audience—each person might interpret “a long time” differently.
 
-Driving clarity is your responsibility as a communicator. If your data or story isn’t clear, your audience will struggle to follow and may not understand your key takeaways.
+Instead, you could say, “Our users take, on average, 3 minutes to sign up and onboard onto our platform.” This is much clearer.
+
+When communicating data, don’t assume your audience thinks like you. Clarity is your responsibility as a communicator. If your message isn’t clear, your audience may struggle to follow, making it less likely they’ll understand your key takeaways.
 
 Use specific, meaningful words and phrases instead of vague ones. For example:
-- “We had an *impressive* year!” (How impressive? 2% growth? 50% growth?)
-- “Our users’ success rates increased *dramatically*.” (What does “dramatically” mean?)
-- “This project will require *significant* effort.” (How much effort is “significant”?)
+- “We had an *impressive* year!”  
+  - One person might think “impressive” means a 2–3% revenue increase, while another might think it means 50–60%.
+- “Our users’ success rates increased *dramatically*.”  
+  - How much of an increase is “dramatic”?
+- “This undertaking will require *significant* effort.”  
+  - How much effort is “significant”?
 
 While vague words can be useful for introductions or summaries, ensure the core of your presentation is clear.
 
 ### 5. Use Emotion
-Emotion is a powerful tool in storytelling, especially when communicating data. Evoking emotion helps your audience empathize, increases the likelihood they’ll remember your message, and motivates them to take action.
+Emotion is a powerful tool in storytelling, especially when communicating data. Evoking emotion helps your audience empathize, making them more likely to take action and remember your message.
 
-You’ve likely seen this in TV commercials—some use somber tones to highlight serious issues, while others use upbeat emotions to associate their data with positivity.
+You’ve likely seen this in TV commercials—some use somber tones to highlight serious issues, while others are upbeat and associate their data with happiness.
 
-Here are a few ways to incorporate emotion into your data communication:
+How can you use emotion when communicating data? Here are a few ways:
 - **Testimonials and Personal Stories**: Collect both quantitative and qualitative data, and integrate personal stories to complement your numbers.
-- **Imagery**: Use visuals to help your audience connect emotionally with your data.
+- **Imagery**: Use visuals to help your audience see themselves in the situation and connect emotionally with your data.
 - **Color**: Different colors evoke different emotions. For example:
   - Blue: Peace and trust
   - Green: Nature and environment
   - Red: Passion and excitement
-  - Yellow: Optimism and happiness
-  Be mindful that color meanings can vary across cultures.
+  - Yellow: Optimism and happiness  
+  Be mindful that colors may have different meanings in different cultures.
 
 # Communication Case Study
-Emerson is a Product Manager for a mobile app. Emerson notices that customers submit 42% more complaints and bug reports on weekends. Additionally, customers whose complaints go unanswered for more than 48 hours are 32% more likely to rate the app 1 or 2 stars in the app store.
+Emerson is a Product Manager for a mobile app. Emerson notices that customers submit 42% more complaints and bug reports on weekends. Additionally, customers whose complaints go unanswered for over 48 hours are 32% more likely to rate the app 1 or 2 stars in the app store.
 
 After researching, Emerson identifies two solutions to address the issue. Emerson schedules a 30-minute meeting with the three company leads to present the data and proposed solutions.
 
-The goal of the meeting is to help the company leads understand that the following two solutions can improve the app’s rating, which could lead to higher revenue:
+The goal of the meeting is for the company leads to understand that the two solutions below can improve the app’s rating, which could lead to higher revenue.
 
 **Solution 1.** Hire customer service reps to work on weekends.
 
 **Solution 2.** Purchase a new customer service ticketing system that allows reps to prioritize complaints based on how long they’ve been in the queue.
-In the meeting, Emerson spends 5 minutes explaining why having a low rating on the app store is problematic, 10 minutes discussing the research process and how trends were identified, 10 minutes reviewing recent customer complaints, and the last 5 minutes briefly mentioning two potential solutions.
 
+During the meeting, Emerson spends:
+- 5 minutes explaining why a low app store rating is problematic,
+- 10 minutes detailing the research process and how trends were identified,
+- 10 minutes reviewing recent customer complaints, and
+- 5 minutes briefly covering the two potential solutions.
 Was this an effective way for Emerson to communicate during this meeting?
 
-During the meeting, one company lead became fixated on the 10 minutes of customer complaints Emerson presented. After the meeting, these complaints were the only thing this team lead remembered. Another company lead primarily focused on Emerson’s explanation of the research process. The third company lead did recall the solutions Emerson proposed but wasn’t sure how those solutions could be implemented.
+During the meeting, one company lead fixated on the 10 minutes of customer complaints that Emerson went through. After the meeting, these complaints were the only thing that this team lead remembered. Another company lead primarily focused on Emerson describing the research process. The third company lead did remember the solutions proposed by Emerson but wasn’t sure how those solutions could be implemented.
 
-In the situation above, it’s clear there was a significant gap between what Emerson intended for the team leads to take away and what they actually took away from the meeting. Below is an alternative approach Emerson could consider.
+In the situation above, you can see that there was a significant gap between what Emerson wanted the team leads to take away, and what they ended up taking away from the meeting. Below is another approach that Emerson could consider.
 
 How could Emerson improve this approach?  
 Context, Conflict, Climax, Closure, Conclusion  
-**Context** - Emerson could spend the first 5 minutes introducing the overall situation and ensuring the team leads understand how the problems impact key company metrics, such as revenue.
+**Context** - Emerson could spend the first 5 minutes introducing the entire situation and making sure that the team leads understand how the problems affect metrics that are critical to the company, like revenue.
 
-It could be framed like this: "Currently, our app's rating in the app store is 2.5. App store ratings are crucial for App Store Optimization, which affects how many users discover our app in search results and how potential users perceive it. Naturally, the number of users we attract is directly tied to revenue."
+It could be laid out this way: "Currently, our app's rating in the app store is a 2.5. Ratings in the app store are critical to App Store Optimization, which impacts how many users see our app in search, and how our app is viewed to prospective users. And of course, the number of users we have is tied directly to revenue."
 
-**Conflict** Emerson could then dedicate the next 5 minutes to discussing the conflict.
+**Conflict** Emerson could then move to talk for the next 5 minutes or so on the conflict.
 
-It could be presented like this: “Users submit 42% more complaints and bug reports on weekends. Customers who submit a complaint that remains unanswered for over 48 hours are 32% less likely to rate our app above a 2 in the app store. Improving our app's rating to a 4 would boost visibility by 20-30%, which I estimate could increase revenue by 10%." Emerson should be ready to back up these figures with evidence.
+It could go like this: “Users submit 42% more complaints and bug reports on the weekends. Customers who submit a complaint that goes unanswered after 48 hours are 32% less likely to give our app a rating over a 2 in the app store. Improving our app's rating in the app store to a 4 would improve our visibility by 20-30%, which I project would increase revenue by 10%." Of course, Emerson should be prepared to justify these numbers.
 
-**Climax** After establishing the context and conflict, Emerson could move to the climax for about 5 minutes.
+**Climax** After laying the groundwork, Emerson could then move to the Climax for 5 or so minutes.
 
-Here, Emerson could introduce the proposed solutions, explain how they address the outlined issues, detail how they could be integrated into current workflows, provide cost estimates, highlight the ROI, and perhaps even share screenshots or wireframes of how the solutions would look in practice. Emerson could also include testimonials from users who experienced delays in complaint resolution and feedback from a current customer service representative about the existing ticketing system.
+Emerson could introduce the proposed solutions, lay out how those solutions will address the issues outlined, how those solutions could be implemented into existing workflows, how much the solutions cost, what the ROI of the solutions would be, and maybe even show some screenshots or wireframes of how the solutions would look if implemented. Emerson could also share testimonials from users who took over 48 hours to have their complaint addressed, and even a testimonial from a current customer service representative within the company who has comments on the current ticketing system.
 
-**Closure** Emerson could then spend 5 minutes summarizing the company’s challenges, revisiting the proposed solutions, and reinforcing why these solutions are the right choice.
+**Closure** Now Emerson can spend 5 minutes restating the problems faced by the company, revisit the proposed solutions, and review why those solutions are the right ones.
 
-**Conclusion** Since this is a meeting with a few stakeholders involving two-way communication, Emerson could allocate 10 minutes for questions to ensure any confusion among the team leads is addressed before the meeting ends.
+**Conclusion** Because this is a meeting with a few stakeholders where two-way communication will be used, Emerson could then plan to leave 10 minutes for questions, to make sure that anything that was confusing to the team leads could be clarified before the meeting is over.
 
-If Emerson adopted this second approach, it’s much more likely the team leads would leave the meeting with the intended takeaways: that the way complaints and bugs are handled needs improvement, and there are two actionable solutions to make that improvement happen. This approach would be far more effective in communicating the data and the story Emerson wants to convey.
+If Emerson took approach #2, it is much more likely that the team leads will take away from the meeting exactly what Emerson intended for them to take away – that the way complaints and bugs are handled could be improved, and there are 2 solutions that could be put in place to make that improvement happen. This approach would be a much more effective approach to communicating the data, and the story, that Emerson wants to communicate.
 
 # Conclusion  
 ### Summary of main points  
-- Communication is the act of conveying or exchanging information.  
-- When communicating data, the goal isn’t just to share numbers—it’s to tell a story informed by the data.  
-- There are two types of communication: One-Way Communication (information is shared without expecting a response) and Two-Way Communication (information is exchanged interactively).  
-- There are various strategies for telling a story with data. The five strategies discussed are:  
+- To communicate is to convey or exchange information.  
+- When communicating data, your aim shouldn't be to simply pass along numbers to your audience. Your aim should be to communicate a story that is informed by your data.  
+- There are 2 types of communication, One-Way Communication (information is communicated with no intention of a response) and Two-Way Communication (information is communicated back and forth.)  
+- There are many strategies you can use to tell a story with your data, 5 strategies we went over are:  
   - Understand Your Audience, Your Medium, & Your Communication Method  
   - Begin with the End in Mind  
   - Approach it Like an Actual Story  
@@ -195,7 +204,7 @@ If Emerson adopted this second approach, it’s much more likely the team leads
 
 [1. Communicating Data - Communicating Data with Tableau [Book] (oreilly.com)](https://www.oreilly.com/library/view/communicating-data-with/9781449372019/ch01.html)  
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)  
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/31)  
 
 Review what you've just learned with the Post-Lecture Quiz above!  
 
@@ -206,4 +215,4 @@ Review what you've just learned with the Post-Lecture Quiz above!
 ---
 
 **Disclaimer**:  
-This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
+This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.
\ No newline at end of file
diff --git a/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md
index 32ea379a..87749274 100644
--- a/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md
+++ b/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
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-  "original_hash": "6a0556b17de4c8d1a9470b02247b01d4",
-  "translation_date": "2025-09-05T07:37:30+00:00",
+  "original_hash": "5f8e7cdefa096664ae86f795be571580",
+  "translation_date": "2025-09-06T10:04:41+00:00",
   "source_file": "5-Data-Science-In-Cloud/17-Introduction/README.md",
   "language_code": "en"
 }
@@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 In this lesson, you will learn the basic concepts of the Cloud, understand why using Cloud services can be beneficial for your data science projects, and explore examples of data science projects implemented in the Cloud.
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/32)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/32)
 
 ## What is the Cloud?
 
@@ -23,15 +23,15 @@ The Cloud, or Cloud Computing, refers to the delivery of a variety of pay-as-you
 
 Clouds are typically categorized into Public, Private, and Hybrid clouds:
 
-* Public cloud: A public cloud is owned and operated by a third-party cloud service provider that delivers its computing resources over the internet to the general public.
+* Public cloud: A public cloud is owned and operated by a third-party cloud service provider that delivers its computing resources over the internet to the public.
 * Private cloud: Refers to cloud computing resources used exclusively by a single business or organization, with services and infrastructure maintained on a private network.
-* Hybrid cloud: A hybrid cloud combines public and private clouds. Users can maintain an on-premises datacenter while running data and applications on one or more public clouds.
+* Hybrid cloud: A hybrid cloud combines public and private clouds. Users can maintain an on-premises datacenter while allowing data and applications to run on one or more public clouds.
 
 Most cloud computing services fall into three main categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
 
 * Infrastructure as a Service (IaaS): Users rent IT infrastructure such as servers, virtual machines (VMs), storage, networks, and operating systems.
 * Platform as a Service (PaaS): Users rent an environment for developing, testing, delivering, and managing software applications without worrying about the underlying infrastructure like servers, storage, networks, and databases.
-* Software as a Service (SaaS): Users access software applications over the internet, typically on a subscription basis, without managing hosting, infrastructure, or maintenance tasks like upgrades and security patches.
+* Software as a Service (SaaS): Users access software applications over the internet, typically on a subscription basis, without managing hosting, infrastructure, or maintenance tasks like software updates and security patches.
 
 Some of the largest Cloud providers include Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
 
@@ -39,21 +39,21 @@ Some of the largest Cloud providers include Amazon Web Services, Google Cloud Pl
 
 Developers and IT professionals opt for the Cloud for various reasons, including:
 
-* Innovation: Integrate cutting-edge services provided by Cloud providers directly into your applications.
-* Flexibility: Pay only for the services you need, with a wide range of options. Typically, you pay as you go and adjust services based on your evolving needs.
-* Budget: Avoid upfront investments in hardware and software, as well as the setup and operation of on-site datacenters. Pay only for what you use.
-* Scalability: Scale resources based on project needs, allowing applications to adjust computing power, storage, and bandwidth in response to external factors.
-* Productivity: Focus on your business rather than spending time on tasks like managing datacenters.
-* Reliability: Benefit from continuous data backups and disaster recovery plans to ensure business continuity during crises.
-* Security: Leverage policies, technologies, and controls to enhance the security of your projects.
+* Innovation: You can enhance your applications by integrating innovative services provided by Cloud providers directly into your apps.
+* Flexibility: You pay only for the services you need and can choose from a wide range of options. Typically, you pay as you go and adjust services based on your evolving needs.
+* Budget: You avoid upfront investments in hardware and software, as well as the setup and operation of on-site datacenters, paying only for what you use.
+* Scalability: Resources can scale according to your project’s needs, allowing your apps to use more or less computing power, storage, and bandwidth as required.
+* Productivity: You can focus on your business rather than spending time on tasks like managing datacenters, which can be handled by others.
+* Reliability: Cloud Computing offers continuous data backup options and disaster recovery plans to ensure your business and services remain operational during crises.
+* Security: You benefit from policies, technologies, and controls that enhance the security of your project.
 
 These are some of the common reasons why people choose Cloud services. Now that we understand the Cloud and its benefits, let’s explore how it can assist data scientists and developers working with data in overcoming challenges such as:
 
-* Storing large amounts of data: Instead of managing and protecting large servers, store data directly in the Cloud using solutions like Azure Cosmos DB, Azure SQL Database, and Azure Data Lake Storage.
-* Performing Data Integration: Data integration is crucial for transitioning from data collection to actionable insights. Cloud services like Data Factory enable you to collect, transform, and integrate data from various sources into a single data warehouse.
-* Processing data: Processing large datasets requires significant computing power, which many individuals lack. The Cloud’s vast computing resources can be harnessed to run and deploy solutions.
-* Using data analytics services: Services like Azure Synapse Analytics, Azure Stream Analytics, and Azure Databricks help transform data into actionable insights.
-* Using Machine Learning and data intelligence services: Instead of building algorithms from scratch, leverage machine learning services like AzureML. Cognitive services such as speech-to-text, text-to-speech, and computer vision are also available.
+* Storing large amounts of data: Instead of purchasing, managing, and securing large servers, you can store data directly in the Cloud using solutions like Azure Cosmos DB, Azure SQL Database, and Azure Data Lake Storage.
+* Performing Data Integration: Data integration is a crucial step in Data Science, enabling the transition from data collection to actionable insights. Cloud services like Data Factory allow you to collect, transform, and integrate data from various sources into a single data warehouse.
+* Processing data: Processing large datasets requires significant computing power, which not everyone has access to. Many choose to leverage the Cloud’s vast computing resources to run and deploy their solutions.
+* Using data analytics services: Cloud services like Azure Synapse Analytics, Azure Stream Analytics, and Azure Databricks help transform data into actionable insights.
+* Using Machine Learning and data intelligence services: Instead of starting from scratch, you can use machine learning algorithms provided by Cloud services like AzureML. Additionally, cognitive services like speech-to-text, text-to-speech, computer vision, and more are available.
 
 ## Examples of Data Science in the Cloud
 
@@ -62,9 +62,9 @@ Let’s explore a couple of scenarios to make this more concrete.
 ### Real-time social media sentiment analysis
 A common beginner project in machine learning is real-time sentiment analysis of social media data.
 
-Imagine you run a news media website and want to use live data to understand what content your readers might be interested in. You can build a program to analyze sentiment in real-time from Twitter posts on topics relevant to your audience.
+Imagine you run a news media website and want to use live data to understand what content your readers might be interested in. You can build a program that performs real-time sentiment analysis on Twitter data related to topics relevant to your audience.
 
-Key indicators include the volume of tweets on specific topics (hashtags) and sentiment analysis using tools designed for this purpose.
+Key indicators include the volume of tweets on specific topics (hashtags) and sentiment, determined using analytics tools that perform sentiment analysis on the specified topics.
 
 Steps to create this project:
 
@@ -78,9 +78,9 @@ Steps to create this project:
 For the full process, refer to the [documentation](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&ocid=AID30411099).
 
 ### Scientific papers analysis
-Here’s another example: analyzing COVID-related scientific papers, a project created by [Dmitry Soshnikov](http://soshnikov.com), one of the authors of this curriculum.
+Here’s another example from [Dmitry Soshnikov](http://soshnikov.com), one of the authors of this curriculum.
 
-Dmitry developed a tool to extract knowledge from scientific papers, gain insights, and help researchers efficiently navigate large collections of documents.
+Dmitry developed a tool to analyze COVID-related scientific papers. This project demonstrates how to create a tool that extracts knowledge from scientific papers, provides insights, and helps researchers efficiently navigate large collections of papers.
 
 Steps involved:
 
@@ -91,7 +91,7 @@ Steps involved:
 
 For the full process, visit [Dmitry’s blog](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/).
 
-As demonstrated, Cloud services offer numerous ways to perform Data Science.
+As you can see, Cloud services offer numerous ways to perform Data Science.
 
 ## Footnote
 
@@ -102,7 +102,7 @@ Sources:
 
 ## Post-Lecture Quiz
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/33)
 
 ## Assignment
 
diff --git a/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md
index d959e642..e6800e79 100644
--- a/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md
+++ b/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "39f3b3a9d873eaa522c2e792ce0ca503",
-  "translation_date": "2025-09-05T07:36:26+00:00",
+  "original_hash": "bd4da10766c64fce4294a98f6479dfb0",
+  "translation_date": "2025-09-06T10:03:50+00:00",
   "source_file": "5-Data-Science-In-Cloud/18-Low-Code/README.md",
   "language_code": "en"
 }
@@ -36,34 +36,34 @@ Table of contents:
   - [Review & Self Study](../../../../5-Data-Science-In-Cloud/18-Low-Code)
   - [Assignment](../../../../5-Data-Science-In-Cloud/18-Low-Code)
   
-## [Pre-Lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Pre-Lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/34)
 
 ## 1. Introduction
 ### 1.1 What is Azure Machine Learning?
 
-The Azure cloud platform offers over 200 products and cloud services designed to help you create innovative solutions. Data scientists spend a significant amount of time exploring and preparing data, as well as testing various model-training algorithms to achieve accurate results. These tasks can be time-intensive and often lead to inefficient use of costly compute resources.
+The Azure cloud platform offers over 200 products and services designed to help you create innovative solutions. Data scientists spend a significant amount of time exploring and preparing data, as well as testing various model-training algorithms to achieve accurate results. These tasks can be time-intensive and often lead to inefficient use of costly compute resources.
 
-[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) is a cloud-based platform for developing and managing machine learning solutions in Azure. It provides a variety of tools and features to help data scientists prepare data, train models, deploy predictive services, and monitor their usage. Most importantly, it enhances efficiency by automating many of the repetitive tasks involved in training models. It also allows the use of scalable cloud-based compute resources, enabling the handling of large datasets while incurring costs only when resources are in use.
+[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) is a cloud-based platform for developing and managing machine learning solutions in Azure. It provides a variety of tools and features to help data scientists prepare data, train models, deploy predictive services, and monitor their usage. Most importantly, it automates many of the time-consuming tasks involved in model training and allows for scalable cloud-based compute resources, enabling efficient handling of large datasets while incurring costs only when resources are used.
 
-Azure ML offers all the tools developers and data scientists need for their machine learning workflows, including:
+Azure ML offers a comprehensive suite of tools for developers and data scientists, including:
 
-- **Azure Machine Learning Studio**: A web portal for low-code and no-code options for model training, deployment, automation, tracking, and asset management. It integrates seamlessly with the Azure Machine Learning SDK.
-- **Jupyter Notebooks**: For quickly prototyping and testing ML models.
+- **Azure Machine Learning Studio**: A web-based portal for low-code and no-code options for model training, deployment, automation, tracking, and asset management. It integrates seamlessly with the Azure Machine Learning SDK.
+- **Jupyter Notebooks**: For quickly prototyping and testing machine learning models.
 - **Azure Machine Learning Designer**: A drag-and-drop interface for building experiments and deploying pipelines in a low-code environment.
-- **Automated machine learning UI (AutoML)**: Automates repetitive tasks in model development, enabling scalable, efficient, and productive ML model creation while maintaining quality.
+- **Automated machine learning UI (AutoML)**: Automates repetitive tasks in model development, enabling high-scale, efficient, and productive model creation while maintaining quality.
 - **Data Labeling**: A tool that assists in automatically labeling data.
-- **Machine learning extension for Visual Studio Code**: A full-featured development environment for building and managing ML projects.
+- **Machine learning extension for Visual Studio Code**: A full-featured development environment for managing machine learning projects.
 - **Machine learning CLI**: Command-line tools for managing Azure ML resources.
-- **Integration with open-source frameworks**: Compatibility with PyTorch, TensorFlow, Scikit-learn, and more for training, deploying, and managing the ML lifecycle.
-- **MLflow**: An open-source library for managing the lifecycle of ML experiments. **MLFlow Tracking** logs and tracks training metrics and model artifacts, regardless of the experiment's environment.
+- **Integration with open-source frameworks**: Compatibility with PyTorch, TensorFlow, Scikit-learn, and more for end-to-end machine learning processes.
+- **MLflow**: An open-source library for managing the lifecycle of machine learning experiments. **MLFlow Tracking** logs and tracks metrics and artifacts from training runs, regardless of the experiment's environment.
 
 ### 1.2 The Heart Failure Prediction Project:
 
-Building projects is one of the best ways to test your skills and knowledge. In this lesson, we will explore two approaches to building a data science project for predicting heart failure in Azure ML Studio: the Low code/No code method and the Azure ML SDK method, as illustrated in the following diagram:
+Building projects is one of the best ways to test your skills and knowledge. In this lesson, we will explore two approaches to creating a data science project for predicting heart failure in Azure ML Studio: the Low code/No code method and the Azure ML SDK method, as illustrated in the following diagram:
 
-![project-schema](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/project-schema.PNG)
+![project-schema](../../../../translated_images/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.en.png)
 
-Each approach has its advantages and disadvantages. The Low code/No code method is easier to start with, as it involves interacting with a graphical user interface (GUI) and requires no prior coding knowledge. This method is ideal for quickly testing a project's feasibility and creating a proof of concept (POC). However, as the project scales and needs to be production-ready, relying solely on the GUI becomes impractical. At this stage, programmatically automating tasks such as resource creation and model deployment becomes essential, which is where the Azure ML SDK comes into play.
+Each approach has its advantages and disadvantages. The Low code/No code method is beginner-friendly, as it relies on a graphical user interface (GUI) and requires no prior coding knowledge. This method is ideal for quickly testing a project's feasibility and creating a Proof of Concept (POC). However, as the project scales and requires production-level readiness, relying solely on the GUI becomes impractical. At this stage, programmatic automation of tasks—such as resource creation and model deployment—becomes essential, making the Azure ML SDK a critical tool.
 
 |                   | Low code/No code | Azure ML SDK              |
 |-------------------|------------------|---------------------------|
@@ -73,7 +73,7 @@ Each approach has its advantages and disadvantages. The Low code/No code method
 
 ### 1.3 The Heart Failure Dataset: 
 
-Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for 31% of all global deaths. Factors such as tobacco use, unhealthy diets, obesity, physical inactivity, and excessive alcohol consumption can serve as features for predictive models. Estimating the likelihood of developing a CVD can be invaluable in preventing heart attacks in high-risk individuals.
+Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for 31% of all global deaths. Factors such as tobacco use, unhealthy diets, obesity, physical inactivity, and excessive alcohol consumption can serve as features for predictive models. Estimating the likelihood of developing CVDs can be instrumental in preventing heart attacks among high-risk individuals.
 
 Kaggle provides a publicly available [Heart Failure dataset](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data), which we will use for this project. You can download the dataset now. It is a tabular dataset with 13 columns (12 features and 1 target variable) and 299 rows.
 
@@ -98,7 +98,7 @@ Once you have the dataset, we can begin the project in Azure.
 
 ## 2. Low code/No code training of a model in Azure ML Studio
 ### 2.1 Create an Azure ML workspace
-To train a model in Azure ML, you first need to create an Azure ML workspace. The workspace is the top-level resource in Azure Machine Learning, serving as a centralized hub for all the artifacts you create. It keeps a history of all training runs, including logs, metrics, outputs, and snapshots of your scripts. This information helps you identify which training run produced the best model. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
+To train a model in Azure ML, you first need to create an Azure ML workspace. The workspace is the central resource for Azure Machine Learning, where you can manage all the artifacts created during your machine learning workflows. It keeps a record of all training runs, including logs, metrics, outputs, and snapshots of your scripts. This information helps you identify which training run produced the best model. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
 
 It is recommended to use the latest browser version compatible with your operating system. Supported browsers include:
 
@@ -107,22 +107,22 @@ It is recommended to use the latest browser version compatible with your operati
 - Chrome (latest version)
 - Firefox (latest version)
 
-To use Azure Machine Learning, create a workspace in your Azure subscription. This workspace will allow you to manage data, compute resources, code, models, and other artifacts related to your machine learning workloads.
+To use Azure Machine Learning, create a workspace in your Azure subscription. This workspace will allow you to manage data, compute resources, code, models, and other artifacts related to your machine learning projects.
 
-> **_NOTE:_** Your Azure subscription will incur a small charge for data storage as long as the Azure Machine Learning workspace exists. It is recommended to delete the workspace when it is no longer in use.
+> **_NOTE:_** Your Azure subscription will incur a small charge for data storage as long as the Azure Machine Learning workspace exists. It is recommended to delete the workspace when it is no longer needed.
 
 1. Sign in to the [Azure portal](https://ms.portal.azure.com/) using the Microsoft credentials associated with your Azure subscription.
 2. Select **＋Create a resource**.
    
-   ![workspace-1](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-1.PNG)
+   ![workspace-1](../../../../translated_images/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.en.png)
 
    Search for Machine Learning and select the Machine Learning tile.
 
-   ![workspace-2](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-2.PNG)
+   ![workspace-2](../../../../translated_images/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.en.png)
 
    Click the create button.
 
-   ![workspace-3](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-3.PNG)
+   ![workspace-3](../../../../translated_images/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.en.png)
 
    Fill in the settings as follows:
    - Subscription: Your Azure subscription
@@ -134,38 +134,38 @@ To use Azure Machine Learning, create a workspace in your Azure subscription. Th
    - Application insights: Note the default new application insights resource that will be created for your workspace
    - Container registry: None (one will be created automatically the first time you deploy a model to a container)
 
-    ![workspace-4](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-4.PNG)
+    ![workspace-4](../../../../translated_images/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.en.png)
 
    - Click **Review + create**, then click the **Create** button.
-3. Wait for your workspace to be created (this may take a few minutes). Once it's ready, navigate to it in the portal. You can find it under the Machine Learning Azure service.
-4. On the Overview page for your workspace, launch Azure Machine Learning Studio (or open a new browser tab and go to https://ml.azure.com). Sign in to Azure Machine Learning Studio using your Microsoft account. If prompted, select your Azure directory, subscription, and workspace.
+3. Wait for your workspace to be created (this may take a few minutes). Once ready, navigate to it in the portal. You can find it under the Machine Learning Azure service.
+4. On the Overview page for your workspace, launch Azure Machine Learning Studio (or open a new browser tab and go to https://ml.azure.com). Sign in using your Microsoft account. If prompted, select your Azure directory, subscription, and workspace.
    
-![workspace-5](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-5.PNG)
+![workspace-5](../../../../translated_images/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.en.png)
 
 5. In Azure Machine Learning Studio, click the ☰ icon at the top left to explore the various pages in the interface. These pages allow you to manage the resources in your workspace.
 
-![workspace-6](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-6.PNG)
+![workspace-6](../../../../translated_images/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.en.png)
 
-You can manage your workspace through the Azure portal, but Azure Machine Learning Studio provides a more user-friendly interface tailored for data scientists and ML engineers.
+While you can manage your workspace through the Azure portal, Azure Machine Learning Studio provides a more user-friendly interface tailored for data scientists and machine learning engineers.
 
 ### 2.2 Compute Resources
 
-Compute resources are cloud-based resources used for running model training and data exploration processes. There are four types of compute resources you can create:
+Compute resources are cloud-based environments where you can run model training and data exploration tasks. There are four types of compute resources you can create:
 
 - **Compute Instances**: Development workstations for data scientists to work with data and models. This involves creating a Virtual Machine (VM) and launching a notebook instance. Models can then be trained by calling a compute cluster from the notebook.
-- **Compute Clusters**: Scalable clusters of VMs for on-demand processing of experiment code. These are essential for training models and can include specialized GPU or CPU resources.
+- **Compute Clusters**: Scalable clusters of VMs for running experiment code on demand. These are essential for model training and can include specialized GPU or CPU resources.
 - **Inference Clusters**: Deployment targets for predictive services using trained models.
 - **Attached Compute**: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
 
 #### 2.2.1 Choosing the right options for your compute resources
 
-There are several important factors to consider when creating a compute resource, and these choices can significantly impact your project.
+There are several important factors to consider when creating a compute resource, and these choices can be critical decisions.
 
 **Do you need CPU or GPU?**
 
-A CPU (Central Processing Unit) is the main electronic circuitry that executes instructions in a computer program. A GPU (Graphics Processing Unit) is a specialized electronic circuit designed to process graphics-related tasks at high speeds.
+A CPU (Central Processing Unit) is the electronic circuitry that executes instructions in a computer program. A GPU (Graphics Processing Unit) is a specialized electronic circuit designed to execute graphics-related code at a very high rate.
 
-The key difference between CPU and GPU architecture is that CPUs are optimized for handling a wide range of tasks quickly (measured by clock speed) but are limited in the number of tasks they can run concurrently. GPUs, on the other hand, are designed for parallel computing, making them ideal for deep learning tasks.
+The main difference between CPU and GPU architecture is that a CPU is optimized for handling a wide range of tasks quickly (as measured by CPU clock speed), but it has limited concurrency for running tasks. GPUs, on the other hand, are designed for parallel computing and are much better suited for deep learning tasks.
 
 | CPU                                     | GPU                         |
 |-----------------------------------------|-----------------------------|
@@ -175,113 +175,113 @@ The key difference between CPU and GPU architecture is that CPUs are optimized f
 
 **Cluster Size**
 
-Larger clusters are more expensive but offer better responsiveness. If you have more time but a limited budget, start with a small cluster. If you have a larger budget but limited time, opt for a larger cluster.
+Larger clusters are more expensive but provide better responsiveness. If you have time but limited funds, start with a small cluster. Conversely, if you have funds but limited time, opt for a larger cluster.
 
 **VM Size**
 
-You can adjust the size of your RAM, disk, number of cores, and clock speed based on your time and budget constraints. Increasing these parameters will improve performance but also increase costs.
+Depending on your time and budget constraints, you can adjust the size of your RAM, disk, number of cores, and clock speed. Increasing these parameters will be more expensive but will result in better performance.
 
 **Dedicated or Low-Priority Instances?**
 
-Low-priority instances are interruptible, meaning Microsoft Azure can reassign these resources to other tasks, potentially interrupting your job. Dedicated instances, which are non-interruptible, ensure your job won't be terminated without your permission. This is another trade-off between time and cost, as interruptible instances are cheaper than dedicated ones.
+A low-priority instance is interruptible, meaning Microsoft Azure can reassign those resources to another task, interrupting your job. A dedicated instance, or non-interruptible instance, ensures your job will not be terminated without your permission. This is another consideration of time versus cost, as interruptible instances are less expensive than dedicated ones.
 
 #### 2.2.2 Creating a compute cluster
 
-In the [Azure ML workspace](https://ml.azure.com/) we created earlier, navigate to the "Compute" section to view the different compute resources discussed (e.g., compute instances, compute clusters, inference clusters, and attached compute). For this project, we need a compute cluster for model training. In the Studio, click on the "Compute" menu, then the "Compute cluster" tab, and click the "+ New" button to create a compute cluster.
+In the [Azure ML workspace](https://ml.azure.com/) we created earlier, navigate to the "Compute" section to view the different compute resources we discussed (e.g., compute instances, compute clusters, inference clusters, and attached compute). For this project, we need a compute cluster for model training. In the Studio, click on the "Compute" menu, then the "Compute cluster" tab, and click the "+ New" button to create a compute cluster.
 
-![22](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-1.PNG)
+![22](../../../../translated_images/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.en.png)
 
 1. Choose your options: Dedicated vs Low priority, CPU or GPU, VM size, and core number (you can keep the default settings for this project).
 2. Click the "Next" button.
 
-![23](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-2.PNG)
+![23](../../../../translated_images/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.en.png)
 
 3. Assign a name to the cluster.
-4. Configure options such as the minimum/maximum number of nodes, idle seconds before scale-down, and SSH access. Note that setting the minimum number of nodes to 0 will save money when the cluster is idle. A higher maximum number of nodes will shorten training time. The recommended maximum number of nodes is 3.
+4. Choose your options: Minimum/Maximum number of nodes, idle seconds before scale-down, SSH access. Note that setting the minimum number of nodes to 0 will save money when the cluster is idle. A higher maximum number of nodes will shorten training time. The recommended maximum number of nodes is 3.  
 5. Click the "Create" button. This step may take a few minutes.
 
-![29](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-3.PNG)
+![29](../../../../translated_images/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.en.png)
 
 Great! Now that we have a compute cluster, we need to load the data into Azure ML Studio.
 
 ### 2.3 Loading the Dataset
 
-1. In the [Azure ML workspace](https://ml.azure.com/) we created earlier, click on "Datasets" in the left menu and then click the "+ Create dataset" button. Select the "From local files" option and upload the Kaggle dataset we downloaded earlier.
-
-   ![24](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-1.PNG)
-
-2. Provide a name, type, and description for your dataset. Click "Next." Upload the data files and click "Next."
+1. In the [Azure ML workspace](https://ml.azure.com/) we created earlier, click on "Datasets" in the left menu and then click the "+ Create dataset" button to create a dataset. Choose the "From local files" option and select the Kaggle dataset we downloaded earlier.
+   
+   ![24](../../../../translated_images/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.en.png)
 
-   ![25](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-2.PNG)
+2. Assign a name, type, and description to your dataset. Click "Next." Upload the data from files. Click "Next."
+   
+   ![25](../../../../translated_images/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.en.png)
 
 3. In the Schema section, change the data type to Boolean for the following features: anaemia, diabetes, high blood pressure, sex, smoking, and DEATH_EVENT. Click "Next" and then "Create."
+   
+   ![26](../../../../translated_images/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.en.png)
 
-   ![26](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-3.PNG)
-
-Excellent! With the dataset uploaded and the compute cluster created, we can now begin training the model.
+Fantastic! Now that the dataset is ready and the compute cluster is created, we can start training the model!
 
-### 2.4 Low code/No Code training with AutoML
+### 2.4 Low code/No Code training with AutoML 
 
-Traditional machine learning model development is resource-intensive, requiring significant domain expertise and time to produce and compare multiple models. Automated machine learning (AutoML) simplifies this process by automating the iterative tasks of model development. AutoML enables data scientists, analysts, and developers to build ML models efficiently and at scale, while maintaining high model quality. It significantly reduces the time required to create production-ready ML models. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
+Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. Automated machine learning (AutoML) automates the time-consuming, iterative tasks of machine learning model development. It enables data scientists, analysts, and developers to build ML models at scale, efficiently, and productively, while maintaining model quality. AutoML reduces the time needed to create production-ready ML models with ease and efficiency. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
 
 1. In the [Azure ML workspace](https://ml.azure.com/) we created earlier, click on "Automated ML" in the left menu and select the dataset you just uploaded. Click "Next."
 
-   ![27](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-1.PNG)
-
-2. Enter a new experiment name, specify the target column (DEATH_EVENT), and select the compute cluster we created. Click "Next."
+   ![27](../../../../translated_images/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.en.png)
 
-   ![28](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-2.PNG)
-
-3. Choose "Classification" and click "Finish." This step may take 30 minutes to 1 hour, depending on your compute cluster size.
-
-   ![30](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-3.PNG)
+2. Enter a new experiment name, the target column (DEATH_EVENT), and the compute cluster we created. Click "Next."
+   
+   ![28](../../../../translated_images/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.en.png)
 
-4. Once the run is complete, click on the "Automated ML" tab, select your run, and click on the algorithm listed in the "Best model summary" card.
+3. Choose "Classification" and click "Finish." This step may take between 30 minutes to 1 hour, depending on your compute cluster size.
+    
+    ![30](../../../../translated_images/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.en.png)
 
-   ![31](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-4.PNG)
+4. Once the run is complete, click on the "Automated ML" tab, select your run, and click on the algorithm in the "Best model summary" card.
+    
+    ![31](../../../../translated_images/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.en.png)
 
-Here, you can view detailed information about the best model generated by AutoML. You can also explore other models in the "Models" tab. Spend some time reviewing the explanations (preview button) for the models. Once you've chosen the model you want to use (in this case, we'll select the best model chosen by AutoML), we can proceed to deploy it.
+Here, you can view a detailed description of the best model AutoML generated. You can also explore other models in the "Models" tab. Take a few minutes to explore the models in the "Explanations (preview)" section. Once you've chosen the model you want to use (in this case, we'll select the best model chosen by AutoML), we'll proceed to deploy it.
 
 ## 3. Low code/No Code model deployment and endpoint consumption
 ### 3.1 Model deployment
 
-The AutoML interface allows you to deploy the best model as a web service in just a few steps. Deployment integrates the model so it can make predictions based on new data, enabling applications to identify opportunities or risks. For this project, deploying the model as a web service allows medical applications to make live predictions about patients' risk of heart failure.
+The automated machine learning interface allows you to deploy the best model as a web service in just a few steps. Deployment integrates the model so it can make predictions based on new data and identify potential opportunities. For this project, deploying to a web service means medical applications can use the model to make live predictions about patients' risk of heart failure.
 
 In the best model description, click the "Deploy" button.
+    
+![deploy-1](../../../../translated_images/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.en.png)
 
-![deploy-1](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-1.PNG)
+15. Assign a name, description, compute type (Azure Container Instance), enable authentication, and click "Deploy." This step may take about 20 minutes to complete. The deployment process involves several steps, including registering the model, generating resources, and configuring them for the web service. A status message appears under "Deploy status." Select "Refresh" periodically to check the deployment status. The status will show "Healthy" once the deployment is complete and running.
 
-15. Provide a name, description, compute type (Azure Container Instance), enable authentication, and click "Deploy." This step may take about 20 minutes. The deployment process involves registering the model, generating resources, and configuring them for the web service. A status message will appear under "Deploy status." Click "Refresh" periodically to check the status. The deployment is complete and running when the status is "Healthy."
-
-![deploy-2](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-2.PNG)
+![deploy-2](../../../../translated_images/deploy-2.94dbb13f239086473aa4bf814342fd40483d136849b080f02bafbb995383940e.en.png)
 
 16. Once deployed, click on the "Endpoint" tab and select the endpoint you just deployed. Here, you can find all the details about the endpoint.
 
-![deploy-3](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-3.PNG)
+![deploy-3](../../../../translated_images/deploy-3.fecefef070e8ef3b28e802326d107f61ac4e672d20bf82d05f78d025f9e6c611.en.png)
 
-Fantastic! With the model deployed, we can now consume the endpoint.
+Amazing! Now that the model is deployed, we can start consuming the endpoint.
 
 ### 3.2 Endpoint consumption
 
-Click on the "Consume" tab. Here, you'll find the REST endpoint and a Python script under the consumption options. Take some time to review the Python code.
+Click on the "Consume" tab. Here, you'll find the REST endpoint and a Python script in the consumption section. Take some time to review the Python code.
 
-This script can be run directly from your local machine to consume the endpoint.
+This script can be run directly from your local machine to consume your endpoint.
 
-![35](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/consumption-1.PNG)
+![35](../../../../translated_images/consumption-1.700abd196452842a020c7d745908637a6e4c5c50494ad1217be80e283e0de154.en.png)
 
-Pay attention to these two lines of code:
+Take a moment to review these two lines of code: 
 
 ```python
 url = 'http://98e3715f-xxxx-xxxx-xxxx-9ec22d57b796.centralus.azurecontainer.io/score'
 api_key = '' # Replace this with the API key for the web service
-```  
-The `url` variable contains the REST endpoint found in the "Consume" tab, and the `api_key` variable contains the primary key (if authentication is enabled). These variables allow the script to consume the endpoint.
+```
+The `url` variable contains the REST endpoint found in the "Consume" tab, and the `api_key` variable contains the primary key also found in the "Consume" tab (only if authentication is enabled). This is how the script consumes the endpoint.
 
-18. Running the script should produce the following output:  
-```python
+18. Running the script should produce the following output:
+    ```python
     b'"{\\"result\\": [true]}"'
-    ```  
-This indicates that the prediction for heart failure based on the given data is true. This makes sense because the default data in the script has all values set to 0 or false. You can modify the data using the following sample input:
+    ```
+This indicates that the prediction of heart failure for the given data is true. This makes sense because, by default, all values in the script are set to 0 and false. You can modify the data using the following input sample:
 
 ```python
 data = {
@@ -317,27 +317,27 @@ data = {
         },
     ],
 }
-```  
-The script should return:  
-```python
+```
+The script should return:
+    ```python
     b'"{\\"result\\": [true, false]}"'
-    ```  
+    ```
 
-Congratulations! You've successfully trained, deployed, and consumed a model on Azure ML!
+Congratulations! You've successfully consumed the deployed model and trained it on Azure ML!
 
 > **_NOTE:_** Once you're done with the project, don't forget to delete all the resources.
 
 ## 🚀 Challenge
 
-Examine the model explanations and details generated by AutoML for the top models. Try to understand why the best model outperformed the others. What algorithms were compared? What are the differences between them? Why is the best model more effective in this case?
+Examine the model explanations and details AutoML generated for the top models. Try to understand why the best model outperformed the others. What algorithms were compared? What are the differences between them? Why is the best model performing better in this case?
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/35)
 
 ## Review & Self Study
 
-In this lesson, you learned how to train, deploy, and consume a model to predict heart failure risk using a Low code/No code approach in the cloud. If you haven't already, explore the model explanations generated by AutoML for the top models and try to understand why the best model is superior.
+In this lesson, you learned how to train, deploy, and consume a model to predict heart failure risk using a Low code/No code approach in the cloud. If you haven't already, dive deeper into the model explanations AutoML generated for the top models and try to understand why the best model is better than the others.
 
-You can further explore Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109).
+Explore more about Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109).
 
 ## Assignment
 
diff --git a/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md
index 97d42487..d7b8d175 100644
--- a/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md
+++ b/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "5da2d6b3736f6d668b89de9bf3bdd31b",
-  "translation_date": "2025-09-05T07:37:53+00:00",
+  "original_hash": "472d3fab1c5be50f387336e7a686dbe1",
+  "translation_date": "2025-09-06T10:04:58+00:00",
   "source_file": "5-Data-Science-In-Cloud/19-Azure/README.md",
   "language_code": "en"
 }
@@ -37,76 +37,76 @@ Table of contents:
   - [Review & Self Study](../../../../5-Data-Science-In-Cloud/19-Azure)
   - [Assignment](../../../../5-Data-Science-In-Cloud/19-Azure)
 
-## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/36)
+## [Pre-Lecture Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/36)
 
 ## 1. Introduction
 
 ### 1.1 What is Azure ML SDK?
 
-Data scientists and AI developers use the Azure Machine Learning SDK to create and manage machine learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, such as Jupyter Notebooks, Visual Studio Code, or your preferred Python IDE.
+Data scientists and AI developers use the Azure Machine Learning SDK to design and execute machine learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, such as Jupyter Notebooks, Visual Studio Code, or your preferred Python IDE.
 
 Key features of the SDK include:
 
 - Explore, prepare, and manage the lifecycle of datasets used in machine learning experiments.
 - Manage cloud resources for monitoring, logging, and organizing machine learning experiments.
-- Train models locally or using cloud resources, including GPU-accelerated training.
-- Use automated machine learning, which takes configuration parameters and training data, and automatically tests algorithms and hyperparameter settings to find the best model for predictions.
-- Deploy web services to turn trained models into RESTful services that can be integrated into applications.
+- Train models locally or using cloud resources, including GPU-accelerated model training.
+- Use automated machine learning, which takes configuration parameters and training data, and automatically iterates through algorithms and hyperparameter settings to find the best model for predictions.
+- Deploy web services to transform trained models into RESTful services that can be integrated into any application.
 
 [Learn more about the Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
 
-In the [previous lesson](../18-Low-Code/README.md), we explored how to train, deploy, and use a model in a low-code/no-code approach. We used the Heart Failure dataset to create a heart failure prediction model. In this lesson, we will achieve the same goal but using the Azure Machine Learning SDK.
+In the [previous lesson](../18-Low-Code/README.md), we explored how to train, deploy, and consume a model using a Low code/No code approach. We used the Heart Failure dataset to create a heart failure prediction model. In this lesson, we will achieve the same goal but using the Azure Machine Learning SDK.
 
-![project-schema](../../../../5-Data-Science-In-Cloud/19-Azure/images/project-schema.PNG)
+![project-schema](../../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.en.png)
 
 ### 1.2 Heart failure prediction project and dataset introduction
 
-Refer to [this section](../18-Low-Code/README.md) for an introduction to the Heart Failure prediction project and dataset.
+Refer [here](../18-Low-Code/README.md) for the introduction to the Heart Failure prediction project and dataset.
 
 ## 2. Training a model with the Azure ML SDK
 ### 2.1 Create an Azure ML workspace
 
 To simplify, we will work in a Jupyter notebook. This assumes you already have a Workspace and a compute instance. If you already have a Workspace, you can skip to section 2.3 Notebook creation.
 
-If not, follow the instructions in the section **2.1 Create an Azure ML workspace** in the [previous lesson](../18-Low-Code/README.md) to set up a workspace.
+If not, follow the instructions in section **2.1 Create an Azure ML workspace** in the [previous lesson](../18-Low-Code/README.md) to create a workspace.
 
 ### 2.2 Create a compute instance
 
-In the [Azure ML workspace](https://ml.azure.com/) created earlier, navigate to the compute menu to view available compute resources.
+In the [Azure ML workspace](https://ml.azure.com/) created earlier, navigate to the compute menu to view the available compute resources.
 
-![compute-instance-1](../../../../5-Data-Science-In-Cloud/19-Azure/images/compute-instance-1.PNG)
+![compute-instance-1](../../../../translated_images/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.en.png)
 
-To create a compute instance for provisioning a Jupyter notebook:
-1. Click the + New button.
+Let’s create a compute instance to host a Jupyter notebook. 
+1. Click the + New button. 
 2. Assign a name to your compute instance.
-3. Select options: CPU or GPU, VM size, and core count.
+3. Select your options: CPU or GPU, VM size, and core count.
 4. Click the Create button.
 
-Congratulations! You've created a compute instance. We'll use this instance to create a Notebook in the [Creating Notebooks section](../../../../5-Data-Science-In-Cloud/19-Azure).
+Congratulations! You’ve created a compute instance. We’ll use this instance to create a Notebook in the [Creating Notebooks section](../../../../5-Data-Science-In-Cloud/19-Azure).
 
 ### 2.3 Loading the Dataset
-If you haven't uploaded the dataset yet, refer to the section **2.3 Loading the Dataset** in the [previous lesson](../18-Low-Code/README.md).
+Refer to the [previous lesson](../18-Low-Code/README.md) under section **2.3 Loading the Dataset** if you haven’t uploaded the dataset yet.
 
 ### 2.4 Creating Notebooks
 
-> **_NOTE:_** For the next step, you can either create a new notebook from scratch or upload the [notebook we created](../../../../5-Data-Science-In-Cloud/19-Azure/notebook.ipynb) to your Azure ML Studio. To upload it, click on the "Notebook" menu and upload the file.
+> **_NOTE:_** For the next step, you can either create a new notebook from scratch or upload the [notebook we created](notebook.ipynb) to your Azure ML Studio. To upload it, simply click on the "Notebook" menu and upload the notebook.
 
-Notebooks are a crucial part of the data science workflow. They can be used for Exploratory Data Analysis (EDA), training models on compute clusters, and deploying endpoints on inference clusters.
+Notebooks are a crucial part of the data science workflow. They can be used for Exploratory Data Analysis (EDA), calling a compute cluster to train a model, or calling an inference cluster to deploy an endpoint.
 
-To create a Notebook, you need a compute node running the Jupyter notebook instance. Return to the [Azure ML workspace](https://ml.azure.com/) and click on Compute instances. In the list, locate the [compute instance created earlier](../../../../5-Data-Science-In-Cloud/19-Azure). 
+To create a Notebook, you need a compute node running the Jupyter notebook instance. Return to the [Azure ML workspace](https://ml.azure.com/) and click on Compute instances. In the list of compute instances, you should see the [compute instance we created earlier](../../../../5-Data-Science-In-Cloud/19-Azure). 
 
-1. In the Applications section, click the Jupyter option. 
-2. Check the "Yes, I understand" box and click Continue.
-![notebook-1](../../../../5-Data-Science-In-Cloud/19-Azure/images/notebook-1.PNG)
-3. A new browser tab will open with your Jupyter notebook instance. Click the "New" button to create a notebook.
+1. In the Applications section, click on the Jupyter option. 
+2. Tick the "Yes, I understand" box and click Continue.
+![notebook-1](../../../../translated_images/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.en.png)
+3. This will open a new browser tab with your Jupyter notebook instance. Click the "New" button to create a notebook.
 
-![notebook-2](../../../../5-Data-Science-In-Cloud/19-Azure/images/notebook-2.PNG)
+![notebook-2](../../../../translated_images/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.en.png)
 
-Now that we have a Notebook, we can begin training the model using the Azure ML SDK.
+Now that we have a Notebook, we can start training the model using the Azure ML SDK.
 
 ### 2.5 Training a model
 
-If you have any doubts, refer to the [Azure ML SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109). It contains all the necessary details about the modules we'll use in this lesson.
+If you ever have questions, refer to the [Azure ML SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109). It contains all the necessary details about the modules we’ll cover in this lesson.
 
 #### 2.5.1 Setup Workspace, experiment, compute cluster and dataset
 
@@ -124,7 +124,7 @@ from azureml.core import Experiment
 experiment_name = 'aml-experiment'
 experiment = Experiment(ws, experiment_name)
 ```
-To get or create an experiment in a workspace, request the experiment by name. Experiment names must be 3-36 characters long, start with a letter or number, and contain only letters, numbers, underscores, and dashes. If the experiment doesn't exist, a new one is created.
+To get or create an experiment from a workspace, request the experiment using its name. Experiment names must be 3-36 characters, start with a letter or number, and can only contain letters, numbers, underscores, and dashes. If the experiment doesn’t exist in the workspace, a new one is created.
 
 Now, create a compute cluster for training using the following code. Note that this step may take a few minutes. 
 
@@ -145,7 +145,7 @@ cts = ws.compute_targets
 compute_target = cts[aml_name]
 ```
 
-Retrieve the dataset from the workspace using its name:
+Retrieve the dataset from the workspace using its name as follows:
 
 ```python
 dataset = ws.datasets['heart-failure-records']
@@ -156,19 +156,19 @@ df.describe()
 
 Set the AutoML configuration using the [AutoMLConfig class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109).
 
-The documentation lists many parameters you can customize. For this project, we'll use the following:
+As described in the documentation, there are many parameters you can customize. For this project, we’ll use the following:
 
-- `experiment_timeout_minutes`: Maximum time (in minutes) for the experiment to run before stopping automatically.
+- `experiment_timeout_minutes`: Maximum time (in minutes) the experiment can run before stopping automatically.
 - `max_concurrent_iterations`: Maximum number of concurrent training iterations allowed.
-- `primary_metric`: Metric used to evaluate the experiment's progress.
-- `compute_target`: Azure Machine Learning compute target for the experiment.
-- `task`: Type of task (e.g., 'classification', 'regression', or 'forecasting').
-- `training_data`: Training data containing features and a label column (optionally sample weights).
+- `primary_metric`: Metric used to evaluate the experiment’s status.
+- `compute_target`: Azure Machine Learning compute target for running the experiment.
+- `task`: Type of task to run (e.g., 'classification', 'regression', or 'forecasting').
+- `training_data`: Training data for the experiment, including features and a label column (optionally a sample weights column).
 - `label_column_name`: Name of the label column.
 - `path`: Full path to the Azure Machine Learning project folder.
-- `enable_early_stopping`: Whether to stop early if the score doesn't improve.
-- `featurization`: Whether to automate or customize the featurization step.
-- `debug_log`: Log file for debug information.
+- `enable_early_stopping`: Whether to enable early termination if the score doesn’t improve in the short term.
+- `featurization`: Whether to perform automatic featurization or use customized featurization.
+- `debug_log`: Log file for writing debug information.
 
 ```python
 from azureml.train.automl import AutoMLConfig
@@ -192,12 +192,12 @@ automl_config = AutoMLConfig(compute_target=compute_target,
                              **automl_settings
                             )
 ```
-With the configuration set, train the model using the following code. This step may take up to an hour depending on your cluster size.
+Once the configuration is set, train the model using the following code. This step may take up to an hour depending on your cluster size.
 
 ```python
 remote_run = experiment.submit(automl_config)
 ```
-Use the RunDetails widget to display the experiment progress.
+Use the RunDetails widget to display the different experiments.
 ```python
 from azureml.widgets import RunDetails
 RunDetails(remote_run).show()
@@ -206,12 +206,12 @@ RunDetails(remote_run).show()
 
 ### 3.1 Saving the best model
 
-The `remote_run` object is of type [AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109). It has a `get_output()` method that returns the best run and its fitted model.
+The `remote_run` is an object of type [AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109). This object has a `get_output()` method that returns the best run and its corresponding fitted model.
 
 ```python
 best_run, fitted_model = remote_run.get_output()
 ```
-View the parameters of the best model by printing `fitted_model`. Check its properties using the [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) method.
+View the parameters used for the best model by printing the fitted_model, and check the properties of the best model using the [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) method.
 
 ```python
 best_run.get_properties()
@@ -230,7 +230,7 @@ model = best_run.register_model(model_name = model_name,
 ```
 ### 3.2 Model Deployment
 
-After saving the best model, deploy it using the [InferenceConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py?ocid=AID3041109) class. InferenceConfig specifies settings for a custom environment used for deployment. The [AciWebservice](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aciwebservice?view=azure-ml-py) class represents a machine learning model deployed as a web service endpoint on Azure Container Instances. The deployed service is a load-balanced HTTP endpoint with a REST API. You can send data to this API and receive predictions from the model.
+After saving the best model, deploy it using the [InferenceConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py?ocid=AID3041109) class. InferenceConfig specifies the configuration settings for a custom environment used for deployment. The [AciWebservice](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aciwebservice?view=azure-ml-py) class represents a machine learning model deployed as a web service endpoint on Azure Container Instances. The deployed service is a load-balanced HTTP endpoint with a REST API. You can send data to this API and receive predictions from the model.
 
 Deploy the model using the [deploy](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) method.
 
@@ -292,11 +292,11 @@ Congratulations! You have successfully used the model deployed and trained on Az
 
 ## 🚀 Challenge
 
-There are many other things you can do with the SDK, but unfortunately, we can't cover them all in this lesson. The good news is that learning how to navigate the SDK documentation can help you explore further on your own. Check out the Azure ML SDK documentation and look for the `Pipeline` class, which allows you to create pipelines. A pipeline is a sequence of steps that can be executed as a workflow.
+There are many other things you can do with the SDK, but unfortunately, we can't cover them all in this lesson. The good news is that learning how to navigate the SDK documentation can help you explore further on your own. Check out the Azure ML SDK documentation and look for the `Pipeline` class, which allows you to create pipelines. A Pipeline is a sequence of steps that can be executed as a workflow.
 
 **HINT:** Visit the [SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) and use keywords like "Pipeline" in the search bar. You should find the `azureml.pipeline.core.Pipeline` class in the search results.
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/37)
 
 ## Review & Self Study
 
diff --git a/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
index 37809d25..77698084 100644
--- a/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
+++ b/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "f95679140c7cb39c30ccba535cd8f03f",
-  "translation_date": "2025-09-05T07:46:42+00:00",
+  "original_hash": "06bac7959b46b6e8aedcae014278c476",
+  "translation_date": "2025-09-06T10:14:29+00:00",
   "source_file": "6-Data-Science-In-Wild/20-Real-World-Examples/README.md",
   "language_code": "en"
 }
@@ -21,29 +21,29 @@ In this lesson, we'll delve into real-world applications of data science across
 
 ## Pre-Lecture Quiz
 
-[Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/38)
 
 ## Data Science + Industry
 
-The democratization of AI has made it easier for developers to design and integrate AI-driven decision-making and data-driven insights into user experiences and workflows. Here are some examples of how data science is applied in real-world industry scenarios:
+The democratization of AI has made it easier for developers to design and integrate AI-driven decision-making and data-driven insights into user experiences and development workflows. Here are some examples of how data science is applied in real-world industry contexts:
 
- * [Google Flu Trends](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/) used data science to correlate search terms with flu trends. While the approach had its flaws, it highlighted the potential (and challenges) of data-driven healthcare predictions.
+ * [Google Flu Trends](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/) used data science to correlate search terms with flu trends. Although the approach had flaws, it highlighted the potential (and challenges) of data-driven healthcare predictions.
 
- * [UPS Routing Predictions](https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather/) - explains how UPS uses data science and machine learning to predict optimal delivery routes, factoring in weather, traffic, deadlines, and more.
+ * [UPS Routing Predictions](https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather/) - explains how UPS uses data science and machine learning to predict optimal delivery routes, factoring in weather conditions, traffic patterns, delivery deadlines, and more.
 
- * [NYC Taxicab Route Visualization](http://chriswhong.github.io/nyctaxi/) - data obtained through [Freedom Of Information Laws](https://chriswhong.com/open-data/foil_nyc_taxi/) was used to visualize a day in the life of NYC taxis, providing insights into navigation, earnings, and trip durations over a 24-hour period.
+ * [NYC Taxicab Route Visualization](http://chriswhong.github.io/nyctaxi/) - data obtained through [Freedom Of Information Laws](https://chriswhong.com/open-data/foil_nyc_taxi/) was used to visualize a day in the life of NYC cabs, providing insights into navigation patterns, earnings, and trip durations over a 24-hour period.
 
  * [Uber Data Science Workbench](https://eng.uber.com/dsw/) - leverages data from millions of daily Uber trips (pickup/dropoff locations, trip durations, preferred routes, etc.) to build analytics tools for pricing, safety, fraud detection, and navigation decisions.
 
  * [Sports Analytics](https://towardsdatascience.com/scope-of-analytics-in-sports-world-37ed09c39860) - focuses on _predictive analytics_ (team and player analysis, like [Moneyball](https://datasciencedegree.wisconsin.edu/blog/moneyball-proves-importance-big-data-big-ideas/), and fan management) and _data visualization_ (team dashboards, fan engagement, etc.) with applications in talent scouting, sports betting, and venue management.
 
- * [Data Science in Banking](https://data-flair.training/blogs/data-science-in-banking/) - showcases the role of data science in finance, including risk modeling, fraud detection, customer segmentation, real-time predictions, and recommender systems. Predictive analytics also play a key role in measures like [credit scores](https://dzone.com/articles/using-big-data-and-predictive-analytics-for-credit).
+ * [Data Science in Banking](https://data-flair.training/blogs/data-science-in-banking/) - showcases the role of data science in finance, including risk modeling, fraud detection, customer segmentation, real-time predictions, and recommender systems. Predictive analytics also drive critical metrics like [credit scores](https://dzone.com/articles/using-big-data-and-predictive-analytics-for-credit).
 
- * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - highlights applications such as medical imaging (MRI, X-Ray, CT-Scan), genomics (DNA sequencing), drug development (risk assessment, success prediction), predictive analytics (patient care and logistics), and disease tracking/prevention.
+ * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - highlights applications such as medical imaging (MRI, X-Ray, CT-Scan), genomics (DNA sequencing), drug development (risk assessment, success prediction), predictive analytics (patient care and logistics), disease tracking, and prevention.
 
-![Data Science Applications in The Real World](../../../../6-Data-Science-In-Wild/20-Real-World-Examples/images/data-science-applications.png) Image Credit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/)
+![Data Science Applications in The Real World](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.en.png) Image Credit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/)
 
-The figure illustrates other domains and examples of data science applications. Want to explore more? Check out the [Review & Self Study](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) section below.
+The figure illustrates other domains and examples of applying data science techniques. Interested in exploring more applications? Check out the [Review & Self Study](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) section below.
 
 ## Data Science + Research
 
@@ -51,24 +51,24 @@ The figure illustrates other domains and examples of data science applications.
 | :---------------------------------------------------------------------------------------------------------------: |
 |              Data Science & Research - _Sketchnote by [@nitya](https://twitter.com/nitya)_              |
 
-While industry applications often focus on large-scale use cases, research projects can provide value in two key areas:
+While industry applications often focus on large-scale use cases, research projects can be valuable for two reasons:
 
-* _Innovation opportunities_ - enabling rapid prototyping of advanced concepts and testing user experiences for next-generation applications.
-* _Deployment challenges_ - examining potential harms or unintended consequences of data science technologies in real-world contexts.
+* _Innovation opportunities_ - enable rapid prototyping of advanced concepts and testing of user experiences for next-generation applications.
+* _Deployment challenges_ - investigate potential harms or unintended consequences of data science technologies in real-world scenarios.
 
-For students, research projects offer learning and collaboration opportunities that deepen understanding and foster connections with experts in areas of interest. So, what do research projects look like, and how can they make an impact?
+For students, research projects offer learning and collaboration opportunities that deepen understanding and foster connections with experts or teams working in areas of interest. So, what do research projects look like, and how can they make an impact?
 
-Consider the [MIT Gender Shades Study](http://gendershades.org/overview.html) by Joy Buolamwini (MIT Media Labs), with a [signature research paper](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf) co-authored with Timnit Gebru (then at Microsoft Research):
+Consider the [MIT Gender Shades Study](http://gendershades.org/overview.html) by Joy Buolamwini (MIT Media Labs) and Timnit Gebru (then at Microsoft Research), which produced a [signature research paper](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf):
 
  * **What:** The study aimed to _evaluate bias in automated facial analysis algorithms and datasets_ based on gender and skin type.
- * **Why:** Facial analysis is used in areas like law enforcement, airport security, and hiring systems—contexts where inaccurate classifications (e.g., due to bias) can lead to economic and social harm. Addressing bias is crucial for fairness.
- * **How:** Researchers noted that existing benchmarks predominantly featured lighter-skinned subjects. They curated a new dataset (1000+ images) balanced by gender and skin type, which was used to evaluate the accuracy of three gender classification products (Microsoft, IBM, Face++).
+ * **Why:** Facial analysis is used in contexts like law enforcement, airport security, and hiring systems, where inaccurate classifications (e.g., due to bias) can lead to economic and social harm. Addressing bias is crucial for fairness.
+ * **How:** Researchers noted that existing benchmarks predominantly featured lighter-skinned subjects. They curated a new dataset (1000+ images) balanced by gender and skin type, which was used to assess the accuracy of three gender classification products (Microsoft, IBM, Face++).
 
 Results revealed that while overall accuracy was good, error rates varied significantly across subgroups, with **misgendering** being higher for females and individuals with darker skin tones, indicating bias.
 
-**Key Outcomes:** The study emphasized the need for _representative datasets_ (balanced subgroups) and _inclusive teams_ (diverse backgrounds) to identify and address biases early in AI solutions. Such research has influenced organizations to adopt principles and practices for _responsible AI_ to enhance fairness in their products and processes.
+**Key Outcomes:** The study underscored the need for more _representative datasets_ (balanced subgroups) and _inclusive teams_ (diverse backgrounds) to identify and address biases early in AI solutions. Such research also informs principles and practices for _responsible AI_ to enhance fairness in AI products and processes.
 
-**Interested in related research at Microsoft?**
+**Want to learn about relevant research efforts at Microsoft?**
 
 * Explore [Microsoft Research Projects](https://www.microsoft.com/research/research-area/artificial-intelligence/?facet%5Btax%5D%5Bmsr-research-area%5D%5B%5D=13556&facet%5Btax%5D%5Bmsr-content-type%5D%5B%5D=msr-project) on Artificial Intelligence.
 * Check out student projects from [Microsoft Research Data Science Summer School](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/).
@@ -80,21 +80,21 @@ Results revealed that while overall accuracy was good, error rates varied signif
 | :---------------------------------------------------------------------------------------------------------------: |
 |              Data Science & Digital Humanities - _Sketchnote by [@nitya](https://twitter.com/nitya)_              |
 
-Digital Humanities [is defined](https://digitalhumanities.stanford.edu/about-dh-stanford) as "a collection of practices and approaches combining computational methods with humanistic inquiry." [Stanford projects](https://digitalhumanities.stanford.edu/projects) like _"rebooting history"_ and _"poetic thinking"_ highlight the connection between [Digital Humanities and Data Science](https://digitalhumanities.stanford.edu/digital-humanities-and-data-science), showcasing techniques like network analysis, information visualization, spatial analysis, and text analysis to uncover new insights from historical and literary datasets.
+Digital Humanities [is defined](https://digitalhumanities.stanford.edu/about-dh-stanford) as "a collection of practices and approaches combining computational methods with humanistic inquiry." [Stanford projects](https://digitalhumanities.stanford.edu/projects) like _"rebooting history"_ and _"poetic thinking"_ demonstrate the connection between [Digital Humanities and Data Science](https://digitalhumanities.stanford.edu/digital-humanities-and-data-science), emphasizing techniques like network analysis, information visualization, spatial analysis, and text analysis to revisit historical and literary datasets for new insights.
 
-*Want to explore or expand a project in this field?*
+*Want to explore and extend a project in this field?*
 
-Check out ["Emily Dickinson and the Meter of Mood"](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671) by [Jen Looper](https://twitter.com/jenlooper), which uses data science to revisit familiar poetry and reinterpret its meaning. For example, _can we predict the season in which a poem was written by analyzing its tone or sentiment?_ What might this reveal about the author's state of mind during that time?
+Check out ["Emily Dickinson and the Meter of Mood"](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671) by [Jen Looper](https://twitter.com/jenlooper), which uses data science to reinterpret familiar poetry and examine its meaning in new contexts. For example, _can we predict the season in which a poem was written by analyzing its tone or sentiment?_ What does this reveal about the author's mindset during that time?
 
 To answer this, follow the steps of the data science lifecycle:
- * [`Data Acquisition`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset) - collect a relevant dataset using APIs (e.g., [Poetry DB API](https://poetrydb.org/index.html)) or web scraping tools (e.g., [Project Gutenberg](https://www.gutenberg.org/files/12242/12242-h/12242-h.htm)).
- * [`Data Cleaning`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#clean-the-data) - format and sanitize text using tools like Visual Studio Code and Microsoft Excel.
- * [`Data Analysis`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#working-with-the-data-in-a-notebook) - import the dataset into "Notebooks" for analysis using Python packages like pandas, numpy, and matplotlib.
+ * [`Data Acquisition`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset) - collect a relevant dataset for analysis using APIs (e.g., [Poetry DB API](https://poetrydb.org/index.html)) or web scraping tools (e.g., [Project Gutenberg](https://www.gutenberg.org/files/12242/12242-h/12242-h.htm)).
+ * [`Data Cleaning`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#clean-the-data) - format, sanitize, and simplify text using tools like Visual Studio Code and Microsoft Excel.
+ * [`Data Analysis`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#working-with-the-data-in-a-notebook) - import the dataset into "Notebooks" for analysis using Python packages (e.g., pandas, numpy, matplotlib) to organize and visualize the data.
  * [`Sentiment Analysis`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#sentiment-analysis-using-cognitive-services) - integrate cloud services like Text Analytics and use low-code tools like [Power Automate](https://flow.microsoft.com/en-us/) for automated workflows.
 
-This workflow allows you to explore seasonal impacts on poem sentiment and develop your own interpretations of the author. Try it out, then extend the notebook to ask new questions or visualize the data differently!
+This workflow allows you to explore seasonal impacts on poem sentiment and develop your own interpretations of the author. Try it yourself, then extend the notebook to ask new questions or visualize the data differently!
 
-> Use tools from the [Digital Humanities toolkit](https://github.com/Digital-Humanities-Toolkit) to explore further.
+> Use tools from the [Digital Humanities toolkit](https://github.com/Digital-Humanities-Toolkit) to pursue these inquiries.
 
 ## Data Science + Sustainability
 
@@ -102,40 +102,40 @@ This workflow allows you to explore seasonal impacts on poem sentiment and devel
 | :---------------------------------------------------------------------------------------------------------------: |
 |              Data Science & Sustainability - _Sketchnote by [@nitya](https://twitter.com/nitya)_              |
 
-The [2030 Agenda For Sustainable Development](https://sdgs.un.org/2030agenda), adopted by all United Nations members in 2015, outlines 17 goals, including those aimed at **Protecting the Planet** from degradation and climate change. The [Microsoft Sustainability](https://www.microsoft.com/en-us/sustainability) initiative supports these goals by leveraging technology to build a more sustainable future, focusing on [four key objectives](https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh): being carbon negative, water positive, zero waste, and bio-diverse by 2030.
+The [2030 Agenda For Sustainable Development](https://sdgs.un.org/2030agenda), adopted by all United Nations members in 2015, outlines 17 goals, including those aimed at **Protecting the Planet** from degradation and climate change. The [Microsoft Sustainability](https://www.microsoft.com/en-us/sustainability) initiative supports these goals by exploring technology solutions to build a more sustainable future, focusing on [four key objectives](https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh): being carbon negative, water positive, zero waste, and bio-diverse by 2030.
 
-Addressing these challenges requires large-scale data and cloud-based solutions. The [Planetary Computer](https://planetarycomputer.microsoft.com/) initiative offers four components to assist data scientists and developers:
+Addressing these challenges at scale requires cloud-scale thinking and large datasets. The [Planetary Computer](https://planetarycomputer.microsoft.com/) initiative offers four components to assist data scientists and developers:
 
- * [Data Catalog](https://planetarycomputer.microsoft.com/catalog) - provides petabytes of Earth Systems data (free and hosted on Azure).
- * [Planetary API](https://planetarycomputer.microsoft.com/docs/reference/stac/) - enables users to search for data across space and time.
+ * [Data Catalog](https://planetarycomputer.microsoft.com/catalog) - provides petabytes of Earth Systems data (free and Azure-hosted).
+ * [Planetary API](https://planetarycomputer.microsoft.com/docs/reference/stac/) - enables users to search for relevant data across space and time.
  * [Hub](https://planetarycomputer.microsoft.com/docs/overview/environment/) - a managed environment for processing massive geospatial datasets.
- * [Applications](https://planetarycomputer.microsoft.com/applications) - showcases tools and use cases for sustainability insights.
+ * [Applications](https://planetarycomputer.microsoft.com/applications) - showcases use cases and tools for sustainability insights.
 **The Planetary Computer Project is currently in preview (as of Sep 2021)** - here's how you can start contributing to sustainability solutions using data science.
 
-* [Request access](https://planetarycomputer.microsoft.com/account/request) to begin exploring and connect with others in the community.
-* [Explore documentation](https://planetarycomputer.microsoft.com/docs/overview/about) to learn about the available datasets and APIs.
-* Check out applications like [Ecosystem Monitoring](https://analytics-lab.org/ecosystemmonitoring/) for ideas and inspiration.
+* [Request access](https://planetarycomputer.microsoft.com/account/request) to begin exploring and connect with others.
+* [Explore documentation](https://planetarycomputer.microsoft.com/docs/overview/about) to learn about supported datasets and APIs.
+* Check out applications like [Ecosystem Monitoring](https://analytics-lab.org/ecosystemmonitoring/) for inspiration on project ideas.
 
 Consider how you can use data visualization to highlight or amplify insights related to issues like climate change and deforestation. Or think about how these insights can be leveraged to design new user experiences that encourage behavioral changes for more sustainable living.
 
 ## Data Science + Students
 
-We've discussed real-world applications in industry and research, and looked at examples of data science applications in digital humanities and sustainability. So how can you, as beginners in data science, develop your skills and share your expertise?
+We've discussed real-world applications in industry and research, and looked at examples of data science applications in digital humanities and sustainability. So how can you develop your skills and share your knowledge as beginners in data science?
 
 Here are some examples of student data science projects to inspire you:
 
  * [MSR Data Science Summer School](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/#!projects) with GitHub [projects](https://github.com/msr-ds3) exploring topics such as:
     - [Racial Bias in Police Use of Force](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2019-replicating-an-empirical-analysis-of-racial-differences-in-police-use-of-force/) | [Github](https://github.com/msr-ds3/stop-question-frisk)
     - [Reliability of NYC Subway System](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2018-exploring-the-reliability-of-the-nyc-subway-system/) | [Github](https://github.com/msr-ds3/nyctransit)
- * [Digitizing Material Culture: Exploring socio-economic distributions in Sirkap](https://claremont.maps.arcgis.com/apps/Cascade/index.html?appid=bdf2aef0f45a4674ba41cd373fa23afc) - a project by [Ornella Altunyan](https://twitter.com/ornelladotcom) and her team at Claremont, using [ArcGIS StoryMaps](https://storymaps.arcgis.com/).
+ * [Digitizing Material Culture: Exploring socio-economic distributions in Sirkap](https://claremont.maps.arcgis.com/apps/Cascade/index.html?appid=bdf2aef0f45a4674ba41cd373fa23afc) - by [Ornella Altunyan](https://twitter.com/ornelladotcom) and her team at Claremont, using [ArcGIS StoryMaps](https://storymaps.arcgis.com/).
 
 ## 🚀 Challenge
 
-Look for articles that suggest beginner-friendly data science projects, such as [these 50 topic areas](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/), [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 analyze and adapt. Don't forget to blog about your learning experiences and share your insights with the community.
+Look for articles that suggest beginner-friendly data science projects - like [these 50 topic areas](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/), [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 analyze and adapt. And don't forget to blog about your learning experiences and share your insights with the community.
 
 ## Post-Lecture Quiz
 
-## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/39)
 
 ## Review & Self Study