In this section, I will highlight some answers from DataScienceGPT that I find particularly insightful and add some comments/context to them.

I am focusing here on novel or lesser known concepts related to data science. That is, there won't be anything in this section about the classical foundational knowledge (what's A/B testing, proxy metrics, percentile-based metrics, novelty effect, network effects, etc.). Instead, the focus here will be on more recent developments of data science or things that are not widely known yet.

When using DataScienceGPT yourself, you can certainly ask questions about foundational knowledge as well (and it actually works really well on that). However, I just figured that this section would be more interesting by focusing on lesser known stuff vs writing yet another lesson on why percentile metrics are better than averages, novelty effect, etc. After all, the full course in product data science has already >100 lessons on those concepts.

This section will be continuously updated. As I am also learning about data science via DataScienceGPT, whenever I stumble upon some interesting answers, I will create a new lesson on it.

I decided to start the course from this section as a way to emphasize the incredible capabilities of generative AI and provide a practical example of it. Also, as a more gentle introduction. Then, the following sections after this one are about everything needed to learn about AI and build AI apps.

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