Data Science Interview Questions | Data Science Interview Questions Answers And Tips | Simplilearn
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This video on Data science interview questions will take you through some of the most popular questions that you face in your Data science interviews. If you’re moving down the path to be a data scientist, you need to be prepared to impress prospective employers with your knowledge. So, here we discuss the list of most popular Data Science interview questions you can expect in an interview and how to frame your answers.
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As a physicist – data scientist, I first planned to make two pendulums using ropes, find the period using T =2Pi sqrt(length/gravitational acceleration). Measure time by using this pendulum clock. 🙂
For question 15 you assume independence, which is (with the provided data) the only way to go, but it's a BIG assumption.
28:36 – it's not union, it's intersection
correct the error @42:32 minus sign should be there for both first and second terms
Very rich and informative video.. thanks for the great effort.
Dimension reduction does not take account tg the redundant features, it only take care of the variance…..
In regard to the task with 9 balls. Many years ago I was give the similar task but a bit more complicated.
You are given 12 gems, one of them is fake and you don’t know which one and you don’t know whether it is lighter or heavier. You have to find this gem and tell it’s weight. You are allowed to weight them 3 times. After the third time you have to give an answer.
For the question at around the 45th minute
The first solution that comes to mind is your solution. However, if the rope is not uniform, doesn't that mean that folding it in half would not work? Let's say the left half burns completely in 20 minutes while the right half in 40 minutes, folding it in half would not really help you measure 30 minutes, and same goes to the folding in 4.
Great video! Well done.
Explanation of sigmoid function (5:15..) is shady. Casts doubt on the quality of the whole presentation and creating organization.
Excellent video. Very much helpful
Great video, thank you!
Additional info : 36:11, this is also called pigeonhole principle.
Thank you so much. Keep the good stuff coming
24:29 how did you get X=1?
In the final question: Does offering coupons impacts purchase decision ?
Here we have 2 categorical variables – 'Coupons' and 'Purchased' both cotain 0 & 1.
Can't this be done using Cho Square?
In Question 6: about Multivariate analysis, it should be 'data with 3 or more INDEPENDENT VARIABLE' instead of 'dependent variable'.
In the bucket example I will fill each bucket with half 2.5 and 1.5 which is 4L
good video. just one point, entropy answer does not look correct. second term also should be negative. is it not?
Where is the part 2
For the 1st question, I did it differently. Step 1 Fiil in the 3 liter bucket and pour the water in 5 liter bucket. (2 liter still not filled) Step 2 Fill in the 3 liter bucket again and pour the water in 5 liter bucket until it is filled (2 liter was available) so you have 1 liter left in 3 liter bucket. Step 3 Empty your 5 liter bucket completely and pour your 1 liter from 3 liter bucket in 5 liter bucket (you have 1 liter of water in 5 liter bucket). Step 4 Fill in the 3 liter bucket completely and then pour the 3 liters in 5 liter bucket (you have 4 liter in 5 liter bucket). This is more steps involved but also possible.
The question at 36th minute falls into Pigeonhole principle.
In Random Forest, we bootstrap sample both features and training instances (rows). Very important point. Bootstrap sampling the features reduce bias error, and second one controls overfitting to a slight extend only though
" e to the base 2" might want to reconsider that one.
You got it right the second time you said it!
Thanks
This is a great video! Thank you for sharing.
Is association rule mining type of content based filtering?
Nice! There's an issue with Entropy formula though…
Thank you. No video has impacted me this much.
Chai square hehe ;D
are you the same guy as the instructor in linear algebra on Khanacademy ?
One of the greatest videos so far in the field of data science.
What do we mean by Feedback mechanism?
Correct the error at 35:13 => Recall Rate = (True Positive) / (True Positive + False Negative)
Thanks for sharing. Can you explain a little bit more about ANOVA/one-way ANOVA, when should we use ANOVA?
Great video. Thanks for sharing. I think answer to question 11 could has more to do with curse of dimensionality, rather than computation and storage.
Excellent video. Compiled almost all the important aspects of Data Science interview.
I have a doubt. For the recommendation, the algorithm that is being used is Decision tree.
Random forest algorithm randomly chooses 'k' features at each split not just within a decision tree.