Last answered:

05 Feb 2024

Posted on:

04 Feb 2024


Resolved: Suggestion for more content in the course

Hello :) I have not taken this course yet, but I think it would be a good idea to include SVD (singular value decomposition), since it is a technique with wide use in data science, both for recommendation systems and for dimensionality reduction. . All the best

1 answers ( 1 marked as helpful)
Posted on:

05 Feb 2024


Hi Enrique!

Thank you for your thoughtful suggestion! We truly appreciate your interest in our course. You've made a great point about the importance of SVD in data science.

We did consider exploring SVD as part of the course content. However, for the time being, we've decided to focus on PCA and LDA primarily because they are more widely utilized and recognized in the field. This decision was made to ensure that students first build a strong foundation in techniques that they are most likely to encounter in their data science journey.

SVD is a powerful technique used in complex data processing tasks such as noise reduction, image compression, and more sophisticated applications like natural language processing. Its ability to decompose a matrix into its constituent elements makes it invaluable for understanding the underlying structure of data, especially in systems where dimensionality reduction plays a crucial role in simplifying and interpreting data.

You are right about how important SVD is for advanced tasks, which highlights that we should consider adding it to our course. It's feedback like yours that helps us improve 😊

Thanks again for your input, and we hope you'll find the course enriching.



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