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BookMyShow Data Scientist Interview Question

Company Name – Bookmyshow
Location –
Bangalore
Position –
Data Scientist


Number of Rounds – 4 
Round 1 –
Written Open book SQL and R/Python round
Round 2 –
Case Study
Round 3 –
Statistics and Project Discussion
Round 4 –
HR Round

Round 1 – SQL and R open book written test

1. Count the total salary department number wise where more than 2 employees exist.
2. How can I retrieve all records of emp1 those should not present in emp2?
3. How to fetch only common records from two tables emp and emp1?
4. How to get nth max salaries?
5. How to get 3 Min salaries?
6. Select all customers who purchased at least two items on two separate days.
7. Given a table with a combination of flight paths, how would you identify unique flights if you don’t care which city is the destination or arrival location.
8. If you have two SQL database tables that are not joined  together, how would you create another table to join them.
9. There were plotting questions in R, normal syntax of ggplot in R and seaborn package in Python to create countplot and catplot. Go through the visualizations in R/Python


Round 2 – Case Study

Case Study 1 – A client has a Diwali-themed e-commerce shop that sells five items. What are some potential problems you foresee with their revenue streams?

Case Study 2 – Taj Group of Hotels is planning to start a new branch, What are the parameters it should consider to find the appropriate place?

Round 3 – Statistics and Project Discussion
My project was on Natural Language Processing, so the questions were mostly around the same topic.

a. Give an example of Normal Distribution from daily life.
b. Why do we have N-1 as the denominator when calculating sample variance and N when calculating population variance?
c. How do you remove your own list of stop words from a line of text given below
‘Book My Show is the best website to book a show’
d. What is the difference between stemming and lemmatization?
e. What were the packages which you used in this project?
f. Suppose there is a column in a text file with lots of text and you have take only words and exclude special characters and number.
g. What are the steps involved in a typical Text-Analytics project
h. How many bi-grams can be generated from given sentence:
“Sachin Tendulkar is the best batsman in the World”
i. What Is The Significance Of Tf-idf?
j. What is Normalization in text or text normalization?
k. What kind of features can be followed by NLP for improving accuracy in the classification model?
l. How does a Sentiment analysis algorithm about customer review works?
m. Then how do you counter sarcasm?

Round 4 – HR Round
Basic HR Questions

This was it 

Amazon Interview Question
Sapient Interview Questions

Full interview question of these round is present in our book What do they ask in Top Data Science Interview Part 2: Amazon, Accenture, Sapient, Deloitte, and BookMyShow  

You can get your hand on our ebooks 

1. The Monk who knew Linear Regression (Python): Understand, Learn and Crack Data Science Interview
2. 100 Python Questions to crack Data Science/Analyst Interview
3. Complete Linear Regression and ARIMA Forecasting project using R
4. 100 Hadoop Questions to crack data science interview: Hadoop Cheat Sheet
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6. 100 Puzzles and Case Studies To Crack Data Science Interview
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10. 112 Questions To Crack Business Analyst Interview Using SQL
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13. In 2 Hours Create your first Azure ML in 23 Steps
14. How to Start A Career in Business Analysis
15. Web Analytics – The Way we do it
16. Write better SQL queries + SQL Interview Questions
17. How To Start a Career in Data Science
18. Top Interview Questions And All About Adobe Analytics
19. Business Analyst and MBA Aspirant’s Complete Guide to Case Study – Case Study Cheatsheet
20. 125 Must have Python questions before Data Science interview
21. 100 Questions To Understand Natural Language Processing in Python
22. 100 Questions to master forecasting in R: Learn Linear Regression, ARIMA, and ARIMAX
23. What do they ask in Top Data Science Interviews
24. What do they ask in Top Data Science Interviews: Part 1

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