Data Science Interview Questions (with answers) 

01:49 (Q1) What are the assumptions for Linear Regression? 03:33 (Q2) What is the difference between Precision and Accuracy, can you explain in terms of Confusion Matrix and Confidence Interval? 06:36 (Q3) Normal distribution question 09:36 (Q4) What is the power of a hypothesis test? Why is it important? 11:33 (Q5) What is the difference between K nearest neighbors and K means? 13:19 (Q6) Explain Random Forest in Layman terms. 17:40 (Q7) What is K fold cross-validation? Why do we use it? 19:31 (Q8) Explain how we can handle missing values in our data? 22:21 (Q9) What is the difference between a Bar Graph and a Histogram? 24:17 (Q10) What is a Box and Whisker Plot and when should we use it?

These are great! This goes really well with a video I made about what to expect from the data science interview process. Keep up the good work!

Much appreciating your efforts. Useful video to get a fair idea on what diff type of questions one can face. I have one small observation as follow: Explanation of question 6, is more of a feature selection rather than how actually a random forest works. It should be first deciding some most important parameters (feature selection) and then asking people what is best out of 3 offers based on selected parameters. People will respond as per their experience and learning and recommend an offer to go ahead. The highest number of people recommending an offer is what I will go ahead. Now, in technical term, these number of people are my trees (forest).

Great video and great topics you propose for interviews! Thank you.

Please do more interview videos. This is great help. Thank you!

Please more vids on interview questions! This was great!!

Very helpful video. And I loved your explanations and advice! Keep posting videos, I enjoyed learning with you!

It was very helpful video. Keep posting videos on data science and how to answer them

Excellent questions , looking forward with more questions

Um, the way you explained precision and accuracy was so good, I can rest now 😅

I appreciate your attempt 👏👏

Thank you ! We need more videos based on data science interview questions.

I was asked about some ML and deel plearning algorithms, about accuracy recall f1 score and precision. Also about testing... some iq questions 2....

Appreciate Your efforts. Thank you. Keep posting 🙏

16:00 Random Forest LinkedIn Example. There are 10 companies you got a job offer. You show 3 of them to your LinkedIn contacts. If change the example like this we consider both bagging and subspace sampling. (Bagging-Bootstrap Aggregation is to use some of the data, subspace sampling is to use some of the features. )

GREAT QUESTIONS

Thanks for the work... Good efforts

Thanks for sharing this video really helpful for new students.

Good content and explanation..

Best Random forest ever!.. Thank you so much!

Your voice modulation is very audible and soothing.. Thanks for the video..

Appreciate the video!

You have done a good job. It is very helpful

Great stuff!

Great work. Thanks

Thanks for sharing!!!

Really appreciate 😊

Good Work 😁

Hi, can you please tell me why is multicollinearity bad? Thanks in advance :)

In Z score question can we do it with a t test

Very well explained ! Thank you.

Thank You Ma'am !

Great job

Great efforts 👌👌👌💐💐💐 please do post more videos. Its was very useful

Very helpful

Thankyou so much 💖

good one please domore videos on layman term explanations

thank you very good questions

Very helpful video

Bar graph gives us insight about categorical data. I think Discrete is not the right term.

👍

If it deviates from the standard more than (k) times , the probability of the z score is squared exponentially

I like the way you tried it, but unfortunately you lack explanation , when you say linear relationship please be specific about linear relation between what? if i have a quadratic data, you mean i can't run linear regression?

Are these questions for freshers? Or experienced one as well?

Not sure about the explanation for question 1. Nor why linear regression requires the assumptions for best linear unbiased estimators. Can still fit a linear regression just fine without them, albeit it might invalidate confidence intervals and interpretability. Who cares what the assumptions are if MSE is low?

you are indian and smart. good news for you and india

Aaa... You ggggggive excellent explanation.. Please use perfecttttttt mic .. Because a lotttt of unnecessary sounds coming from your mouthhhh.. Those are getting irritattttted me.

These questions are way way way too easy for any good ds job. These are more like statistics 101 problems.

for the sake of it, please use a different, more pleasant computer voice for your videos. Thanks in advance.

Not engaging.

no offense-- your content seems to be great; but it's very tiring to listen to you're voice-over. sorry, but TRUE

for someone who says "communication is key to data science" you seem to use the visual medium of video very poorly... Reading exactly what's on screen 10x faster than you reading it out really bugged me. I realize actual video can take a lot longer to make, or cost a lot more. But PowerPoints with text dumps haven't been 'good communication' since 2000. Sorry if this was harsh, great info. I just muted it and scrolled through each slide - which would've been a better experience on slideshare sadly.

Very helpful