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Data Scientist Interview Questions

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Case study on Retail analysis case. As I was from BFSI Domain this was new to me, but interesting so.

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From statistical and basic knowledge on online shopping I could at least try possible solutions for the question posed. I think that was very close to what they expected.

R cannot handle large amounts of data.what are the others ways to handle it.(except going with hadoop infrastructure)

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long discussion on the written..how did you solve this question is asked for almost every question.

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A. Technical interview: 1. The most challenging project done so far? Some cross questionings over that 2. How will you design a Recommender System? with lots of optimizations and variants 3. Supervised learning? Random Forest? 4. Knowledge of Unix? Python? R? B. HR interview: 1. Why you are a good fit? 2. Why Innoplexus? 3. Key Strengths? 4. Best achievement so far? 5. Why want to leave currently company so early?

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Aptitude 1 what is the value of a in x2-ax+25 (I did not missed any =0 after 25 the question was exactly this) a 20 b 10 c -25 2 b/c=3 b/a is in lowest form find value of a (again question was exactly thisno other detail was given) a 180 b 240 c 104 According to me above questions are technically incorrect so be ready to face wrong questions in aptitude Coding Best part was that we were given choice to solve in language whatever we want 1 Maximum sub sequence problem 2 given a number find next highest number from same digits eg input 4639 output 4693 input 4693 output 4936

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Puzzles: 1. there are 10 bottles containing coins of 1 gram each. Of the 10, 1 bottle contains coins of 1.1 gram. We have to find out that 1 bottle after only 1 measurement. 2.There is a cylindrical glass filled with water. We have to determine if it is EXACTLY(not approximately) half-filled or not. We can't measure the water, nor can we dip anything in water, or measure the height of glass. We have to just look (may be hold) and tell. HR questions:- 1. What would you want to be if you were a traffic sign? 2. What would you do if you had 6 months left before you die? Why would you do that?

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3. Difference between supervised and unsupervised learning.

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2. How you validate your models and why the specific technique is used?

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