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translations/en/1-Introduction/03-defining-data/README.md

@@ -1,8 +1,8 @@
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@@ -22,18 +22,18 @@ This lesson focuses on identifying and classifying data based on its characteris
 ## 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 classifications depend on the source but ultimately fall into one of these three categories.
+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.
 
 ### 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.
 
 ### 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, like 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 to identify popular keywords in job application resumes.
+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.
 
 ### 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 the 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 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.
 
-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 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 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.
 
 Examples of structured data: spreadsheets, relational databases, phone numbers, bank statements.
 
@@ -43,15 +43,15 @@ Unstructured data cannot typically be organized into rows or columns and lacks a
 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 for easy integration of new information. Metadata provides indicators for 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. 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.
 
 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 "lives," 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 other scientists, it becomes secondary data for those users.
+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.
 
-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 create ways 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 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.
 
 ## 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://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/5)
+## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/)
 
 ## Review & Self Study