Data Scientist answers 30 Data Science Interview questions

Let's look at some data science interview questions!

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[kachrooabhishek]

blessed to like this video, Dude these are some serious scenarios which are not covered by the major channels . Bless you :)

[zyladd]

very well made video that adds details onto standard answers for ds interviews Good analysis.

[nay_codes]

I'm not one to write comments on YouTube, but I have to say I really love your content. And an Interview Questions series would be awesome.

[harshparikh]

Yooo, imma use this to study for some upcoming interviews. This video really dumbed down some of this stuff for me a lot.

[fahnub]

please do more, and also include case based problems if possible

[yk]

Feedback mechanism simply refers to the fact that true labels are known and during training the model gets feedback about the error, hence correcting it via gradient descent. Sounds like a tautology as it is just related to the fact that the data is labeled.

[sourajitsaha]

Feedback mechanism in this context basically means that you get to compare (think of loss functions) your model's output on data with the provided labels in order to update the weights of your model (a.k.a. learning) in the supervised settings, whereas in unsupervised setting you can't do that given you don't have the labels to compare to and you update the weights of your model without explicitly comparing your model's output with labels.

[RPiao]

You rock! Dude. Thank you youtube RECOMMENDATION system. Are you using ANNOY, youtube?

[mnthiet]

P-value is the probability that your null hypothesis is an extreme event. Let’s say that the p-value of observing the regression coefficient of a predictor (e.g. age as an independent variable to predict income) is 0.03. The latter means that you should have 97% confidence in what the data is telling about your age factor in explaining your expected income, hence you should confidently reject that the age’s regression coefficient is 0, no explanatory power.

[mnthiet]

A mistake that the majority of data scientists commit is stating that given that the outcome variable is a probability, [0-1], you should automatically use Logistic regression. That’s completely incorrect. Being a probability, [0-1], is just a necessary condition and not necessarily sufficient to be modelled using Logistic regression. There is an other factor that needs to be observed, being that the predictor variable should exhibit a “threshold effect”, hence the reason for the sigmoid shape in response to the change in the predictor values.

[rohitchan]

This was really helpful. Can you please make videos on reinforcement learning(MDPs, Model Free Learning, Monte Carlo tree) ?

[hardikvegad]

Hey i have a question... When is an outlier consider as important? If we can't drop it than what Techniques we should use to deal with that outlier.... I hope I'll receive an answer bcz I was asked this in an interview

[leotrisport]

Maybe the most important thing to keep in mind in order to improve generalisation (avoid overfitting) might be first to check if the validation/and train are coming from the same probability distribution … I mean no amount of regularisation would sort this issue

[paragjain]

11Oct is too far buddy! I have an interview on Friday! Anyways, better late than never! Thanks for doing this.

[davidcho]

This is the best interview question review video.

[clapdrix]

This is the second video in which the creator has emphasized model interpretability as a universal virtue so I have to call this out. While I agree it's nice to have and in cases of causal inference it's all that really matters, in 60-70% of the modeling done in DS we don't care about interpretability AT ALL provided a black box algorithm is statistically significantly better than the interpretable one in predicting or forecasting. Where is this coming from?

[mallikarjunshettar]

bro why are you stressing yourself by simply reading solutions just share the link we will go through the answer. simply a waste of time and bakwas video

[Srhrsh]

Please do less overaction while speaking and trying to sound cool🙏