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translations/en/1-Introduction/01-defining-data-science/README.md

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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)
 
 Check out [`notebook.ipynb`](../../../../../../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') to explore the code. You can also run the code to see how it performs all data transformations in real time.
 
 > 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