Hey Nash, I consume such random DS content and got to say this is really good. Got a better view on the environment and hopefully apply the same at my work. Thanks Priya!
As someone who's been desperately trying to figure out my niche in time for my internship program, this video and Priya's insights are incredibly invaluable!! Thanks for giving us the scoop. I feel more confident in developing a more fleshed out data science project now since I did my first independent project on ML. Cheers!! :))
As somebody currently changing careers to become a data scientist, this interview has been beneficial. Thank you! I am certainly saving it in my favourite playlist to watch back when needed 🙂
Hey Nash,
I consume such random DS content and got to say this is really good. Got a better view on the environment and hopefully apply the same at my work. Thanks Priya!
As someone who's been desperately trying to figure out my niche in time for my internship program, this video and Priya's insights are incredibly invaluable!! Thanks for giving us the scoop. I feel more confident in developing a more fleshed out data science project now since I did my first independent project on ML. Cheers!! :))
Question. PCA should not be used as feature selection tool right? But more of a tool to enable visualizations in a highly dimensional space.
As somebody currently changing careers to become a data scientist, this interview has been beneficial. Thank you! I am certainly saving it in my favourite playlist to watch back when needed 🙂
One question, getting real world data for practical purpose is a challenge for data science enthusiast. How did you navigate that terrain?
I know the struggle the solution is free/cheap apis (github.com/public-apis/public-apis) this repo has a bunch.
Or learn to scrape data