Data Analytics Interview Quiz | Day 4
Welcome to the 3rd Day of Data Analytics Interview Quiz.
You can go through the question and answer for
Day 1
Day 2
Day 3
The best answer to the quiz was provided by Raahul for all three days
Data Analytics Interview Quiz
In this series, all you need to do is to go through the questions and try to attempt it with whatever knowledge you have.
Before or after you attempt the questions, do go through the complete road map about what you need to study to crack an analytics role. We have covered from 3rd year college student to 8 years of analytics or non-analytic experience. Make sure you understand where to emphasize more(after all by the end of the day you want to crack an interview)
Complete Roadmap for cracking an Analytics Role in the next 30 days
Now, coming back to our questions. Remember, these are the most asked questions in any interview, if you are shy to answer the questions in the comment section, then do make sure to Google the answer and jot it down in your notebook.
Following are the topic wise questions
SQL
- An employee table has 3 columns, Name, Employee id, and Date. The column date contains two values i.e. Date of Joining and Date of Leaving. Calculate the average number of days served by employees in this company (Very Very Important Concept)
- What is the difference between 1 NF and 2 NF normalization?
Python
- Print all the words that starts with R and ends with a in a sentence.
- Print the star pattern
*
**
***
****
*****
Case Study
- Licious wants to understand the most important metrics that should be tracked on all the important verticals like supply, demand, inventory, CSAT, Operations. Come up with as many metrics as you can.
Machine Learning
- In positive skewed data, how are mean, median and mode spread?
Example – Mean > Mode > Media - What is covariance? How is it used?
The Data Monk Interview Books – Don’t Miss
Now we are also available on our website where you can directly download the PDF of the topic you are interested in. At Amazon, each book costs ~299, on our website we have put it at a 60-80% discount. There are ~4000 solved interview questions prepared for you.
10 e-book bundle with 1400 interview questions spread across SQL, Python, Statistics, Case Studies, and Machine Learning Algorithms – Ideal for 0-3 years experienced candidates
23 E-book with ~2000 interview questions spread across AWS, SQL, Python, 10+ ML algorithms, MS Excel, and Case Studies – Complete Package for someone between 0 to 8 years of experience (The above 10 e-book bundle has a completely different set of e-books)
12 E-books for 12 Machine Learning algorithms with 1000+ interview questions – For those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package
Individual 50+ e-books on separate topics
Important Resources to crack interviews (Mostly Free)
There are a few things that might be very useful for your preparation
The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews with Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics managers, etc.
Go through the watchlist which makes you uncomfortable:-
All the list of 200 videos
Complete Python Playlist for Data Science
Company-wise Data Science Interview Questions – Must Watch
All important Machine Learning Algorithm with code in Python
Complete Python Numpy Playlist
Complete Python Pandas Playlist
SQL Complete Playlist
Case Study and Guesstimates Complete Playlist
Complete Playlist of Statistics
Comment ( 1 )
SQL
1. The table name is employee
select avg(datediff(day, date_of_joining, date_of_leaving)) as days_served
from employee
Python
1. sentence = ‘Raahul is a Rasta man’
for word in sentence.split():
if (word[0]==’R’) & (word[-1]==’a’):
print(word)
Output – Rasta
2. import numpy as np
for i in np.arange(1,6):
for j in np.arange(0,i):
print(‘*’,end=’ ‘)
print(‘n’)
Case Study
supply metrics – delivery fleet, average delivery time, order location, order fulfilment %, return rate
demand metrics – order value, order quantity, order location, Licious on-app behaviour (clicks, impressions, drop-off, conversion)
inventory metrics – inventory turnover, inventory accuracy, shipment time (to replenish inventory, if run out)
CSAT – order reviews and ratings, repeat rate (% customers repeating), avg time between repeat orders
Operations – order information (cancelled, delivered, pending etc.), HR (attrition, absenteeism etc.), Marketing, Finance (revenue, costs, loans etc.)
Machine Learning
1. In positive skewed data, mean>median>mode
2. covariance explains how 2 variables are moving together. if it is positive, it indicates that if one variable increases, other one too will increase and if it is negative, it indicates that if one variable increases, the other one will decrease. covariance and standard deviation are used to calculate correlation (which also gives strength of this relationship).