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Data Analytics Interview Questions | Day 2

Welcome to the 2nd Day of Data Analytics Interview Questions Quiz.
You can go through the question and answer for Day 1 Here
The best answer to the quiz was provided by Raahul
Data Analytics Interview Questions

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-analytics 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.

Data Analytics Interview Questions


  1. Write the sequence of the commands in which it is executed in the backend of SQL. (Most asked interview question)
  2. What is the difference between HAVING and WHERE in SQL?
  3. What is the use of Coalesce() function in SQL?


  1. Create a list of a list
  2. Create a dictionary of list
  3. Is string mutable or immutable? Give an example

Case study

  1. KFC wants to open its first branch in India, what data points should be considered before it entering in a new market?

Machine Learning

  1. Define precision and recall using a simple example
  2. Can we use linear regression for a classification problem?

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

About TheDataMonkGrand Master

I am the Co-Founder of The Data Monk. I have a total of 6+ years of analytics experience 3+ years at Mu Sigma 2 years at OYO 1 year and counting at The Data Monk I am an active trader and a logically sarcastic idiot :)

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Comments ( 2 )

  1. SQL
    2. HAVING is used when using a GROUP BY in SQL. It is used to filter based on the aggregated column. For example, if we want to see number of customers occurring more than once, following is the query-
    select cust, count(*)
    from table
    group by cust having count(*)>1
    WHERE is used to filter based simply on conditions (without any aggregation necessary).
    3. COALESCE() function is used to output the first non-null value within it. for eg select coalesce (col,0) will output 0 if col is null.

    1. a = [1,2,3]
    b = [2,4,5]
    c = [a,b]
    print(c) – [[1, 2, 3], [2, 4, 5]]
    2. a = [1,2,3]
    b = [2,4,5]
    c = {1:a,2:b}
    print(c[1]) – [1,2,3]
    3. A string is immutable in Python. We cannot append, extend or pop characters from strings.

    Case Study
    Factors –
    1. External – Existing competition performance and macro-economic conditions of India (inflation, raw items prices, consumer spending sentiment)
    2. Customer – Taste, existing preferences and preferences/openness related to similar food items.
    3. Internal – Small scale or large scale? (one outlet or multiple outlets launched simultaneously?)
    4. Location – Population density, income groups and city development/growth factors.

    Machine Learning
    1. Precision is the ratio between true positives and the all the positives predicted by the model and recall is the ratio between true positives and the true positives and false negatives.
    2. No. linear regression deals with a continuous dependent variable, whereas we deal with discrete values in classification problems.

    • Nice answer Raahul, considering the exit strategy will also get some points as a big company will always have a exit strategy before entering a new market. Overall very good answers