Hi Emma, I had an interview couple of weeks back and i got the role. They had one scenario based question about testing a fair coin which was just like one of your other video. However, now I got to know that the team I'll be joining also does data engineering tasks. So my question is if i take this role will it affect my profile for future changes as I dont want to get into data engineering? thanks
Emma's response is likely to be more reliable than mine, but permit me to share my 2 cents. It's rare to find Data Science roles, especially junior ones, that don't involve a little bit of Data Engineering --- even in research. The engineering aspect helps you ensure you're getting and transforming the right data for your DS task, but also that you're post-processing them in the manner that would help monitor/maintain the models. DS solutions don't work in a vacuum, so more often than not, a Data Scientist is required to wear multiple hats. If you're lucky to be in a large team, you can outsource/delegate some of those engineering tasks to a specialist. In general, in my humble opinion, it's usually a huge plus for your future prospects if you're adept at not just Data Science but also the engineering bits that support your ability to solve business problems with your DS skills. PS: Congratulations on the job offer! And thanks Emma for helping all of us :)
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Thank you so much for the update
Hi Emma, I had an interview couple of weeks back and i got the role. They had one scenario based question about testing a fair coin which was just like one of your other video. However, now I got to know that the team I'll be joining also does data engineering tasks. So my question is if i take this role will it affect my profile for future changes as I dont want to get into data engineering? thanks
Emma's response is likely to be more reliable than mine, but permit me to share my 2 cents.
It's rare to find Data Science roles, especially junior ones, that don't involve a little bit of Data Engineering --- even in research. The engineering aspect helps you ensure you're getting and transforming the right data for your DS task, but also that you're post-processing them in the manner that would help monitor/maintain the models.
DS solutions don't work in a vacuum, so more often than not, a Data Scientist is required to wear multiple hats. If you're lucky to be in a large team, you can outsource/delegate some of those engineering tasks to a specialist.
In general, in my humble opinion, it's usually a huge plus for your future prospects if you're adept at not just Data Science but also the engineering bits that support your ability to solve business problems with your DS skills.
PS: Congratulations on the job offer! And thanks Emma for helping all of us :)
Strata should always be based on uncorellated categories. Each strata should be orthogonol to each other.