Python Interview Questions for Analysts – 5-10

Python Interview Questions for Analysts will help you understand the type of questions that you can expect in an analytics interview. Product-based companies mostly focus on asking at least 5-7 Python questions around the basic logic like Palindrome, rotating an array, sum of diagonal, string, Armstrong numbers, etc. to check the experience of the candidate in Python. We will have a complete set of 200 questions to make sure you can handle these questions like a breeze

Python Interview Questions for Analysts



Link to Question 1 to 5

Python Interview Questions for Analysts

6. Check if a number is prime or not

x = int(input("Enter a number = "))
if x > 1:
for i in range(2,int(x/2)+1):
if (x%i == 0):
print("Not a Prime Number")
break
else:
print("Prime Number")
else:
print("Not a prime number, infact it is a negative number")

7. Prime number in a list of numbers with starting and endpoint

x = int(input("Enter a starting point = "))
y = int(input("Enter an ending point = "))
def prime(x,y):
prime = []
for i in range(x,y):
if (i == 0 or i == 1):
continue
else:
for j in range(2,int(i/2)+1):
if(i%j == 0):
break
else:
prime.append(i)
return prime
print("The list of prime numbers are = ", prime(x,y))

8.Fibonacci Series 0,1,1,2,3,5,8,13…

x = int(input("Enter the n-th fibonacci series number = "))
def fib(x):
if x<=0:
print("Incorrect number")
elif x==1:
return 0
elif x==2:
return 1
else:
return fib(x-1)+fib(x-2)
print("Fibonacci number on the n-th place is ", fib(x))

Python Interview Questions for Analysts

9. check if a given number is a perfect square or not

n = int(input("Enter a number = "))
def sq(n):
s = int(math.sqrt(n))
return s*s == n
print("The status of number = " , sq(n))

10. Print ASCII value of a character in python

x = input("Take a character")
print("The ASCII Value of the character is ", ord(x))

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Python Analytics interview questions 1-5

Python Analytics interview questions
In this series, we will add some of the basic questions to start with Python. Slowly we will move to moderate level DSA questions followed by Pandas, Numpy, and OS module.
All you need to do is to install Python in your system and start from scratch.

Python Analytics interview questions



We will try to solve each question in multiple ways, if you are missing out on the basics then do consult Tutorialspoint or w3school. Invest around 6 hours on these websites and you should be good to go

Python Analytics interview questions

Python Basic Question

1. Take input of two numbers from users and find out the maximum

a = input("Enter first number :")
b = input("Enter second number ")
if a>b:
print(a + " is greater than " + b)
else:
print(b + " is greater than "+a)

or

a = 30
b = 40
print(a if a>b else b)

or

a = 10
b = 30
c = max(a,b)
print(c)

2. Print the factorial of any number

a = 10
def fact(n):
if (n == 1 or n==0):
return 1
else:
return n*fact(n-1)
print(fact(5))

or

import math
print(math.factorial(5))
Python Analytics interview questions



3. Get square of the number if the number is odd and cube if even

def yoo(n):
if (n%2 == 0):
return nnn
else:
return n*n
print(yoo(3))
print(yoo(2))



4. Print square of first n natural numbers

x = int(input("Enter a natural number = "))
def sum_of_natural(x):
ss = 0
for i in range(1,x):
ss = ss+(i*i)
return ss
print("Sum of square of natural numbers are = " , sum_of_natural(x))
Python Analytics interview questions



5. Find if a number is an Armstrong number.

Example – 153 is an Armstrong number as 1^3+5^3+3^3 = 153

num = int(input("Enter a number = "))
s = 0
x = num
while (x >0 ):
digit = x%10
s = s+(digitdigitdigit)
x = x//10
print(s)
print("Armstrong" if s == num else "Not Armstrong")
Python Analytics interview questions


The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Big Data Pipeline Infrastructure

Big Data Pipeline Infrastructure consists of a lot of components and as an analyst, you are expected to understand the complete infrastructure in order to provide better insights on data lineage, integrity, finding loopholes in the source, and final data.
In this blog, we will discuss the most important part of Big Data Pipeline Infrastructure in brief, why brief? because in every organization the technology stack will be different, so you need to dig deep into those components which are currently being used in your organization.

Big Data Pipeline Infrastructure
Big Data Pipeline Infrastructure

Above is a diagram of the overall picture of a complete infrastructure

Data Source

What is a data source?

Anything and everything which creates data is a source. Suppose you have a small sweet shop, then your register which can be turned into an excel sheet is a source of data. Now, you launched an application and a website, thus your new sources are from these applications. Your sweet shop became popular, so you started getting mentions on Twitter (Woohoo !!) , now you have another source of data that you can pull from an API.

All these are your source of data. In general following are the major sources of data:-
1. APIs
2. Web services
3. Data Stream
4. Clickstream Data
5. Social platform
6. Sensor deives

Types of Data

What are the different types of data?

Take the same example as above, you have now two types of data:-

-Structured – Very simple to understand, all your data which can be put in a table (rows and columns) and which follow all the general principles like primary keys, ACID properties, etc. will fall into structured data

-Semi-structured – These are the sources that are sort of structured and you can write a SQL query to get results from here, but it’s not optimal for writing queries. Example – Suppose you have maintained a register for the phone numbers of your top customers. You created one column for each customer, now JIO launched a dual sim and every customer now has two phone numbers. Next year, iPhone will launch a simple and cheap phone (in your dreams) and everyone will have another phone. Increasing the number of columns each time is not a feasible solution.

Solution – Create a column with JSON filed and put all the phone numbers in it, in fact, put all the customer level information in one column of the table. Explore more about JSON on google.

This is an example of semi-structured data. Another example is XML

-Unstructured – Suppose you want to store videos, images, and other types of multimedia data, these are completely unstructured and has a separate way of storing

Where to store the data?

You have a lot of data, but every data is not of the same nature. There are multiple ways to store these data points:-

– Relational DataBase Management System (RDBMS) – Here you can store your structured data in a proper format with all the conventions like ACID property, in rows and columns, keys, etc.

Examples of RDBMS are IBM DB2, MySQL, Oracle Database, PostgreSQL

-NoSQL – Explore more about NoSQL in this blog
Here data is stored in a key-value pair or in a document type, these are complicated to query as these contain semistructured and unstructured data

Example – Redic, MongoDB, Cassandra, Neo4j

Database vs Data warehouse vs Data Lake

Where to store these databases?

First of all database as discussed above is a collection of data for input, storage, search and retrieval of data
It is of two types – RDBMS and NoSQL

Data warehouse is clubbing some databases into one comprehensive dataset for analytical and Business Intelligence purposes. Example – Oracle Exadata, Amazon Redshift, IBM DB2 Warehouse

Data warehouses mostly store transactional data

Data Mart – Subsection of a data warehouse built specifically for a purpose. In the sweet store example, you can have a data warehouse with all the data, but you can create multiple datamarts each for domains like sales, revenue, clickstream data, etc.

Data Lake – It stores large amounts of structured,semi-structured, and unstructured data in their native format. Data can be loaded without knowing the schema of the data. It is very easy to scale if the volume of data increases.

How to move data from source to your table?

You have your data in the source, now there are mainly two ways to load the data in your table:-

1. ETL – It stands for Extract Transform and Load, here the data is extracted from the source. Suppor after extracting you need to make some changes in the table like splitting name into First and last name or masking the phone number of customer, etc. Then that part is called transforming. Once transformation is done, we load it to a warehouse

2. ELT – It stands for Extract Load and Transform – Extracted data is first loaded in a data repository which could be a data lake or data warehouse. It is helpful in processing large datasets of structured and non-relational database


The data pipeline is a broader term of ETL and ELT, a superset of these two. Data pipeline encompassed complete journey from one system to another.



The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com


NoSQL vs Relational Database

NoSQL vs Relational Database
If you are already working in the analytics domain then you might have heard words like PostgreSQL, MongoDB, MySQL, Oracle, etc. We also do have a fair bit of idea about the fact that we store our tables and databases in these places. But why can’t we have everything under one hood?
We should have heard people saying that ABC is a NoSQL database and we should store these data in it rather than a traditional Relational database, what does that mean?

Does NoSQL mean that you don’t have to write SQL on top of it? If yes, then what is the use?
Let’s try to understand NoSQL vs Relational Database

NoSQL stands for Not Only SQL
Not Only SQL – Every item in the database stands on its own

NoSQL vs Relational Database
NoSQL vs Relational Database

Relational databases are like predefined table structures where you can surely add as many rows as possible but adding a new column is a pain as you have to change the schema of the table, thus it is a vertically scaling database. Whereas NoSQL is built in such a way that you can add any number of rows but at the same time you can add any additional information pertaining to a row.

Example – Suppose you have an employee table and you have a NoSQL database, in this table, there will be only 2 columns i.e. a primary key and a values column where you can add as many details as possible, for one employee you can add only name and phone number whereas for another employee you can add the complete profile ranging from name to spouse’s work status, how?

In general, the value thing is stored in a JSON format which again is a key-value pair and you can add any number of attributes corresponding to an employe



Relational Database – Vertical scaling Ex. PostgreSQL, SQLite, MySql
NoSQL – Vertical and Horizontal scaling – Each item in the database only has two things – unique key and values

Different formates in which you can store data in your NoSQL database

1. Document Database – JSON
2. Key-Value store
3. Graphical database


How does this scaling makes database life easier?
As discussed, Scaling in NoSQL can work in a horizontal way

Let’s take an example – A company XYZ runs a NoSQL database which has 10000 servers, All these servers work as a partition i.e. your data which you want to fetch is not present in only one server, it can be present in any partition.

Won’t it be too cumbersome for the database to check all the partition to find a detail ?How do find where is the data stored?

NoSQL databases are key-value stores

It’s not just a key-value pair – It is a key-hash-value where each key is associated with a hash value and this hash value works as a partition identifier 😛

Suppose the hash value is in the range of 0 to 1000 and all your data is stored in one DB. Now, if you want to double your DB performance, you can add another server, and now
Server 1 – Hash value 0 to 500
Server 2 – Hash value 501 to 1000

This range of 0 to 1000 is called a keyspace. Keyspace tells you where to store new items and where to find the existing ones. 

NoSQL is schemaless – anyways you are storing everything in a JSON format and you can increase or decrease the amount of information in each field. In a relational database, you have to define the schema of the table and have to adhere to it

Example of NoSQL –
AWS – DynamoDB
Google Cloud – Big Table
Azure – CosmoDB

You can also run NoSQL DB yourself by using software like Cassandra, CouchDB, MongoDB.

We will try to create a video on the same to give a glimpse of what is JSON and NoSQL

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

OS module in Python – Complete Tutorial

We all have worked with Pandas and Numpy libraries, we might have also used matplot and some machine learning libraries. But, definitely os module in Python is one of the important modules present in the language. Once you recognize the importance of this module, then there is no going back.
OS module in Python is used to interact with your operating system.

When you working in a big data domain then you are bound to work on Hadoop ecosystem and if you working on something where you need to keep a tap on the way things are stored (whether Hadoop or your personal operating system) you need to understand the ways in which you can interact with the files, folders, and directories of the system. OS module helps you in the same.

In this blog, we will look into some important functions of the os module and rest you can practice on your own.


1. How to import the os module?

import os

2. How to get the current working directory?

os.getcwd()

OS module in Python

3. How to make a directory using this os module?

os.makedirs(“Name of the directory”)

Once you execute this code, you will have a new folder in your current working directory.
os.makedirs() method in Python is used to create a directory recursively. That means while making leaf directory if any intermediate-level directory is missing, os.makedirs() method will create them all.

OS module in Python

4. How to get the list of all the files in your root directory using os module?

Use the power of listdir()

Example :-

x = “The location from where you need to list the directories”
dir_list = os.listdir(x)
print(list_dir)

OS module in Python

5. How to remove a directory using os module?

os.remove()
os.rmdir()

os.remove() method in Python is used to remove or delete a file path. This method can not remove or delete a directory. If the specified path is a directory then OSError will be raised by the method.
os.rmdir() method in Python is used to remove or delete an empty directory. OSError will be raised if the specified path is not an empty directory.

OS module in Python

6. You have a file name in one list or variable and path in another, how will you get to the file?

x = “Alpha”
path = “Users/kamal/Documents/prod”
final_path = os.path.join(path,x)
print(final_path)

OS module in Python

7. You have to extract all the file with extension .sql names present in a particular folder, how to do this?

The problem statement is to create a list of all the sql files present in any directory or folder in your root folder.
Here we will be using the os.walk()

We declared a list with the name sql_file, then in the mypath variable we have the complete path of the directory in which you have to find the .sql file
Now walk() is used when you have to parse in multi-level folders.
In each of the variable the folder,path and file contains the name of the directory, path of the file and file name. Since we need to check the file name which ends with .sql, so we will parse only the file. The if and append conditions are intuitive

OS module in Python

8. Check if there is a particular file present in your directory?

print(os.path.exists(“Name of the file”)

OS module in Python

9. How to split the file name and path of the file using os module?

use the function os.path.split(“Complete path”)
This method splits the pathname into a pair of head and tail. Here, the tail is the last pathname component and the head is everything that comes before it. The method returns a tuple of the head and tail of the specified path.

OS module in Python

10. How to get the time of the last modification of a file?

os.path.getmtime() :
This method returns the time of the last modification of the path

These examples are just to show the capability of os module in Python, a lot of problems can be solved if you know how to use this module.

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Amazon Leadership Principle Questions for interview

This article is all about Amazon Leadership Principles for interviews.
But why do we need to understand and prepare these principles?
First and foremost, when you interview for Amazon, in either all the rounds or in some of the rounds there will be a lot of case studies or questions which need to be answered using the Amazon Leader Principle.
Secondly, once you understand these principles you will actually feel that you understand how good or bad you were at solving business problems, what mistakes did you do, and how you figured it out knowingly or unknowingly.
Amazon Leadership Principle helps you in understanding your work in a much better way.

Always be prepared with these principles, I will put out questions on these principles and you need to think through and answer these questions. These might seem to be very natural when you are practicing at home but when these questions are asked in an interview then you might end up blabbering 😛

Under no means, I would like to say that these are the only definitions and answers to the questions. I sincerely urge you to write your own answer in the STAR format, give it a shot and we will evaluate the answers 🙂

Amazon Leadership Principle Questions for interview

Amazon Leadership Principle Questions


Amazon 14 Leadership Principles:-

  1. Customer Obsession
    This world is customer centric, you make an app for customers, you provide services to customers, you do everything for you customer. If you are working in a client facing company then you client is your customer.

    Give an example when you did not meet the expectation of your client. What did you do next? How did you react?
    You can’t just go and say that you were perfect and did not commit any mistake. May be you will remember a blunder which you did in the last MBR, you stated that but you can’t answer ‘How did you mend that mistake’

    My example – I once created an ML model but since it was a high priority task with time crunch so I missed an important variable thus went with half cooked model.
    How I mended – Looked for more variables, did rigorous EDA and came up with far better results

    This is STAR methodology
    S – What was the Situation ? ML model with a time crunch
    T – What was the Task at hand? Had to work on with limited resource to predict XYZ with ABC accuracy
    A – What Action did you take? After I missed out on this opportunity, we did rigorous EDA and identified many good variables
    R – What was the Result ? Very stable and robust model with better accuracy
  2. Ownership
    How good are you at taking ownership, are you a ‘minimum guy’ like Srikant 😛

    Tell us one time when you take ownership of a task and completed it.

    I once identified a problem which the client was facing to identify fraudulent behavior and then approached with a scalable solution
  3. Invent and Simplify
    How innovative are you?
    Have you ever took up a complex problem and solved it with a fresh and simple approach?
    Well, There was a situation when we were trying to build an application to check if two migrated reports are identical or not, we tried to build some pipeline but it was taking a lot of time. There was a freely available software which used to break PDF in screenshots and another application which can match two pictures. Build a simple program to automate these two processes. Simple solution
  4. Are Right, A lot
    Leaders have a good judgment and they are mostly right.
    In your previous role did you ever face an issue which you solved using your judgement or past experience?
    There was a time when my colleague were trying to build a time series model but they were unable to figure out the reason for low accuracy. I asked them to do EDA and figure out some variables which might be a reason for the dip and hiccups. Then I suggested to use a model like ARIMAX which can take both time series and Regression !!
  5. Learn and Be Curious
    Are you curious enough to bring more ideas to the table?
    Tell me an instance when you asked a lot of questions to the data to finally figure out the root cause of an issue.
  6. Hire and Develop the Best
    Do you hire the best or do you hire and develop the best?
    Tell me one instance when you realize that a person in your team is not the best fit for the role. What did you do?
    I once had a person to whom I was giving KT in my last organization, he was not able to grasp some concepts. What I did? I made extensive documentation and videos of the complete process of KT, advised him a couple of courses on internet. The output – He was able to pick up things very quickly and was able to deliver everything at a much better speed.
  7. Insist on the Highest Standard
    Do you believe in having high standard delivery of task?
    Tell me one instance when you insisted on quality of the deliverable and created an impact despite being late on the task.

    No Sir, I am always prepared, I always provide best solution 😛
  8. Think Big
    Tell me one instance when you thought of a big opportunity, pressed for it and delivered something beautifully
    I once had the courage to talk the client of a BIG MNC about the fault in their web home page. There was an issue of cannibalization of lower pages. I worked individually on the project to justify the issue and presented with a different design
  9. Bias For Action
    Speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk taking.

    When was the last time you had to take up a decision with limited time or resource. What actions did you take?

  10. Frugality
    Tell me one time when you tried something out of your own personal time and solved a very small but regular problem.
    In my last organization people used to misplace their Key fobs once they were in their notice period. So, i created a simple tracking Excel sheet which resulted in less number of Key fobs misplacement
  11. Earn Trust
    Tell me the last time you earned trust with your last client. Why was there a trust issue and how did you resolve?
    There was some trust issue with the Client, they were not sure of our analytical capabilities and we were monitored by their Analytics consultant. We took this as an opportunity, did cross team sessions and hands-on exercises to build capability
  12. Dive Deep
    One time when you actually dig deep into the issue to find out solution to an important issue
    Talk about EDA in any project
  13. Have Backbone; Disagree and Commit
    Have you ever said NO to a project or work and delivered something of a superior quality?
    I was once working on one of the important queries, legacy query, though there were some issues but it was there for a while or something. I showed up, fixed that in a month
  14. Deliver Results
    What is the most difficult situation you ever faced in your life? How did you deal with it?
    Created a dashboard where a client can plug and play around the UI so that we can conclude on the design of the dashboard. Thus saved a lot of time for the team

There are some good questions here as well – Link

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Amazon Interview Questions

Company Name – Amazon
Location – Bangalore
Position – Business Analyst

Number of Rounds –  3
Round 1 – Telephonic Round on Technical Capabilities
Round 2 – SQL, Excel, and Statistics
Round 3 – Project discussion and HR


The Hiring Manager will ask about your technical proficiency in the different tools and technologies. Once you met the requirement of their team then you will be asked further questions.
My proficiency was in

SQL – 8/10
Tableau – 8/10
Python – 9/10
Statistics – 8/10

I was asked questions mainly on SQL, which included that of Joins, Group By, etc.
Following are the few questions:-

1. What is the difference between HAVING and WHERE condition?
2. How to calculate Mode, Median, and Mean from a given number?
3. What is the relation between Mean, mode, and median in a normal distribution?
4. What percentage of value lies between Mean and one Standard deviation(both positive and negative)
5. Give the relation of Mean, median, and mode in a positively skewed distribution

6. Negatively skewed distribution?
7. What is sum of squared deviation?
8. Why do we need to square the terms?
9. What is the order of execution of a SQL query?
10.How to find Third highest salary in Employee table using self-join?
11.Why data cleaning plays a vital role in analysis?
12. Classification vs regression?

13. What are the predictor and target variable?
14. How does knn works?

Other than these, there were simple questions on the use of Order by, Between, Like commands

Round 2 – SQL, Excel, and Statistics

1. What is the difference among COUNT, COUNTA, COUNTIF and COUNTBLANK in Ms-Excel?
2. What is the order of sequence of operating mathematical operation in Excel?
3. What is the syntax of Vlookup? How does VLookup works?
4. What is the difference between heap table and temporary table?
5. What is the usage of regular expressions in MySQL?
6. What is the difference between primary key and candidate key?
7. What are the absolute measures of dispersion?

8. What are the measures of spread?
9. What is the use of Kurtosis?
10. What are Leptokurtic and Platykurtic?

You can find the SQL questions here

You can find Statistics Interview Questions here

Round 3 – Project Discussion and HR

The last round was a mixture of Project Discussion and HR

My project was on analyzing the performance of store and product for a leading Multinational Retailer. The performance of a few category of clothes were below par and some stores were not performing good with respect to revenue. We were analyzing the worst performing product and stores.

There were few products which were not performing well in some of the stores, whereas these were performing good in other stores.

Few questions asked in the interview were?
1. What is shelf-life?
2. What is supply chain analytics?
3. Project Description

4. What was the final recommendation?
5. Suppose you were the owner of the company, will you directly shut these stores and products? Is it fair?

6. What is footfall?
7. If you have to recommend a product to a customer who has already filled his cart, then what data will you look for? Basically, how will you recommend a product to an e-commerce customer?

This was it 🙂

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
5. 100 Questions to Crack Data Science Interview
6. 100 Puzzles and Case Studies To Crack Data Science Interview
7. 100 Questions To Crack Big Data Interview
8. 100 Questions to Learn R in 6 Hours
9. Complete Analytical Project before Data Science interview
10. 112 Questions To Crack Business Analyst Interview Using SQL
11. 100 Questions To Crack Business Analyst Interview
12. A to Z of Machine Learning in 6 hours
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

Keep Learning 🙂

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com