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!! :))
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 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 🙂
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!! :))
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!
Glad to help!!
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 🙂
Question. PCA should not be used as feature selection tool right? But more of a tool to enable visualizations in a highly dimensional space.
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