Analytics Interview Questions
In this series we will try to solve as many questions as possible in 4 areas, SQL, Python, Machine Learning Algorithms, and Miscellaneous. All you need to do is to comment your answers below and the best answer will get an extra e-book for free 😛

You need to attempt these questions, put everything in one comment, we will evaluate and will announce the winner in the next day’s post

Analytics Interview Questions

We will follow the below pattern:
– SQL (3 Questions)
– Python (2 Questions)
– Case Study (1 Question)
– Machine Learning and Statistics (2 Questions)

SQL

1) There are two tables X and Y, X has a column A that contains 4 rows -1,-1,-1,-1. Y has a column B that contains -1,-1,-1 (Most asked interview question)
How many rows will be there if we do X left join Y

Given:

• Table X has column A with values (-1, -1, -1, -1) → 4 rows
• Table Y has column B with values (-1, -1, -1) → 3 rows

When we perform LEFT JOIN (X LEFT JOIN Y ON X.A = Y.B):

• Each -1 in X.A will match with all -1s in Y.B (3 matches per row).
• Since there are 4 rows in X, and each matches 3 rows in Y, we get 4 × 3 = 12 rows in the output.

2) What is the use of partition in a table?

Partitioning is used to divide a large table into smaller, manageable sections based on specific criteria (e.g., date, region).

Benefits of Partitioning:

• Improves query performance by scanning only relevant partitions.
• Enhances data management by allowing partition-wise operations.
• Speeds up deletion as old partitions can be dropped instead of row-wise deletion.

Example:
A sales table partitioned by year (sales_2023, sales_2024) allows queries to run faster for a specific year.

3) What is the difference between the Drop and Delete table? (Meesho, OYO rooms)

• DROP TABLE: Removes the entire table structure and data permanently. Cannot be rolled back.
• DELETE FROM TABLE: Removes only the records but keeps the table structure. Can be rolled back if inside a transaction.

Example:

DROP TABLE employees;  -- Deletes table completely
DELETE FROM employees WHERE department = 'HR'; -- Deletes only HR employees

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Python

1) What is a mutable and immutable data type? (Most Asked Questions)

• Mutable Data Types: Can be modified after creation (e.g., lists, dictionaries, sets).
• Immutable Data Types: Cannot be modified after creation (e.g., strings, tuples, integers).

Example:

# Mutable List

lst = [1, 2, 3]
lst[0] = 10  # Allowed

# Immutable String

s = "hello"
s[0] = "H"  # Error: Strings cannot be changed

2) How do you read a CSV file in Python?

import pandas as pd

df = pd.read_csv("data.csv")
print(df.head())  # Displays first 5 rows

3) What is the syntax of group by in Python ?

import pandas as pd

df = pd.DataFrame({
    'Category': ['A', 'B', 'A', 'B'],

    'Sales': [100, 200, 150, 250]
})
grouped = df.groupby('Category').sum()
print(grouped)

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Case Study

1) For a particular e-commerce company, there has been a decline in the number and value of items stored in the cart. Tell all the possible reasons for this decline?

1. Increased Pricing: Customers may abandon carts due to higher product or shipping costs.
2. Website Performance Issues: Slow page loads or crashes can discourage users.
3. Complex Checkout Process: Lengthy forms or too many steps can lead to drop-offs.
4. Limited Payment Options: Lack of preferred payment methods may reduce conversions.
5. Discount Expiry: Users may have added products expecting a discount that expired.
6. Better Competitor Pricing: Customers may compare prices and buy from competitors.

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Machine Learning Concepts

1) What is the p-value? Explain in a very layman term with simple example

• The p-value tells us how likely we got our results by random chance.
• If p-value < 0.05, results are significant (not due to chance).

Example:

• Suppose we test a new diet pill.
• If the p-value = 0.02, it means there’s a 2% chance the results are random.
• Since 2% is less than 5%, we conclude the pill works.

2) What is correlation? Explain with simple example

• Correlation measures how strongly two variables move together.
• Positive Correlation: When one increases, the other increases (e.g., height vs weight).
• Negative Correlation: When one increases, the other decreases (e.g., price vs demand).
• No Correlation: No relationship between them.

Example:

• More hours of study → Higher exam scores (Positive Correlation).
• More ice cream sales → More people at the beach (Positive Correlation).
• More gym workouts → Lower body fat (Negative Correlation).

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