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

Our best seller:

✅ Become a Full Stack Analytics Professional with The Data Monk’s master e-book with 2200+ interview questions covering 23 topics – 2200 Most Asked Interview Questions

Machine Learning e-book

✅ Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions

Domain wise interview e-books

✅ Data Analyst and Product Analyst Interview Preparation -> 1100+ Most Asked Interview Questions
✅ Business Analyst Interview Preparation -> 1250+ Most Asked Interview Questions

The Data Monk – 30 Days Mentorship program

We are a group of 30+ people with ~8 years of Analytics experience in product-based companies. We take interviews on a daily basis for our organization and we very well know what is asked in the interviews.
Other skill enhancer websites charge 2lakh+ GST for courses ranging from 10 to 15 months.

We only focus on making you a clear interview with ease. We have released our Become a Full Stack Analytics Professional for anyone in 2nd year of graduation to 8-10 YOE. This book contains 23 topics and each topic is divided into 50/100/200/250 questions and answers. Pick the book and read it thrice, learn it, and appear in the interview.
We also have a complete Analytics interview package
– 2200 questions ebook (Rs.1999) + 23 ebook bundle for Data Science and Analyst role (Rs.1999)
– 4 one-hour mock interviews, every Saturday (top mate – Rs.1000 per interview)
– 4 career guidance sessions, 30 mins each on every Sunday (top mate – Rs.500 per session)
– Resume review and improvement (Top mate – Rs.500 per review)

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

Snapdeal Data Science Interview Questions

Company: Snapdeal
Designation: Data Scientist
Experience Required: 0 to 4 years
Topics to Prepare: SQL, Python/R, Statistics, Machine Learning, Case Studies
Difficulty Level: High

Snapdeal, founded in 2010 by Kunal Bahl and Rohit Bansal, is one of India’s leading e-commerce platforms. Known for its innovative approach and customer-centric services, Snapdeal is a great place to work for aspiring data scientists. If you’re preparing for a Data Science role at Snapdeal, here’s a detailed breakdown of their interview process and the types of questions you can expect.

Snapdeal Data Science Interview Questions

Interview Process

The Snapdeal Data Science interview process typically consists of 5 rounds, each designed to evaluate different aspects of your technical and analytical skills:

Round 1 – Technical round

Focus: Basic understanding of Data Science concepts, SQL, and Python/R.
Format: You’ll be asked to explain your projects and solve a few coding or SQL problems.

Round 2 – Walk-in/Face-to-Face Technical Round

Focus: Advanced SQL, coding, and problem-solving.
Format: You’ll solve problems on a whiteboard or shared document.

Round 3 – Project Analysis

Focus: Deep dive into your past projects.
Format: You’ll be asked to explain your approach, tools used, and the impact of your work.

Round 4 – Case Studies

Focus: Business problem-solving and data-driven decision-making.
Format: You’ll be given a real-world scenario and asked to propose solutions.

Round 5 – Hiring Manager Round

Focus: Cultural fit, communication skills, and long-term career goals.
Format: Behavioral questions and high-level discussions about your experience.

Difficulty of Questions

Here’s a breakdown of the difficulty level for each topic:

SQL: 8/10

1. How can you get the top three highest salaries from the employees table?

SELECT DISTINCT salary
FROM employees
ORDER BY salary DESC
LIMIT 3;

2. How do you find employees who share the same salary?

SELECT salary, COUNT(*) AS count 
FROM employees 
GROUP BY salary 
HAVING COUNT(*) > 1;

3. How can you find employees who joined in the last six months?

SELECT name, joining_date
FROM employees
WHERE joining_date >= DATE_SUB(CURDATE(), INTERVAL 6 MONTH);

4. How do you find the employee with the longest name?

SELECT name
FROM employees
ORDER BY LENGTH(name) DESC
LIMIT 1;

5. How can you find departments that have more than 10 employees?

SELECT department_id, COUNT(*) AS total_employees 
FROM employees 
GROUP BY department_id 
HAVING COUNT(*) > 10;

Python/R: 9/10

1. Given two lists, keys and values, write Python code to create a dictionary where the elements of keys are the keys and the elements of values are the corresponding values, using the zip() function.

2. Write a Python function that takes two numbers as input and returns their division. Handle both ZeroDivisionError and TypeError exceptions.

3. Given a list of numbers, write Python code to check if any number is greater than 10 and if all numbers are positive.

4. Create a namedtuple called Point with fields x and y, and create an instance of it.

5. Write a Python function that calculates the sum of the elements in a list recursively.

Statistics/ML

1. If you are having 3GB RAM in your machine and you want to train your model on an 8GB dataset. How would you go about this problem?

Answer:

To handle this situation, you can use the following techniques:

Batch Processing: Split the dataset into smaller batches and train the model incrementally.

Out-of-Core Learning: Use libraries like Dask or Vaex that process data in chunks without loading the entire dataset into memory.

Data Sampling: Train the model on a representative sample of the dataset.

Cloud Computing: Use cloud platforms (e.g., AWS, Google Cloud) with higher memory capacity.

Feature Reduction: Remove irrelevant features to reduce dataset size.

2. How can you tell if a given coin is biased?

Answer:

We can use hypothesis testing to determine if a coin is biased:

Null Hypothesis (H₀): The coin is fair (probability of heads p=0.5).

Alternative Hypothesis (H₁): The coin is biased ( p ≠ 0.5 ).

Experiment: Flip the coin n times and record the number of heads.

Test Statistic: Use the binomial test or chi-square test to calculate the p-value.

Conclusion: If the p-value < significance level (e.g., 0.05), reject H₀ and conclude the coin is biased.

3. Why does L1 regularization cause parameter sparsity whereas L2 regularization does not?

Answer:

L1 Regularization: Adds the absolute value of coefficients to the loss function. It tends to shrink less important features to exactly zero, resulting in sparse models.

L2 Regularization: Adds the squared value of coefficients to the loss function. It shrinks coefficients but rarely reduces them to zero, preserving all features.

Example:

L1: Useful for feature selection (e.g., identifying key customer attributes).

L2: Useful when all features are relevant (e.g., predicting house prices).

4. What is the importance of Markov Chains in Data Science?

Answer:

Definition: A stochastic model describing a sequence of events where the probability of each event depends only on the previous state.

Applications:

Customer Journey Analysis: Predict transitions between states (e.g., browsing → cart → purchase).

Recommendation Systems: Model user behavior patterns.

Fraud Detection: Identify unusual sequences of transactions.

5. If the model isn’t perfect, how would you select the threshold so that the model outputs 1 or 0 for a label?

Answer:

ROC Curve: Plot True Positive Rate (TPR) vs. False Positive Rate (FPR) for different thresholds.

Optimal Threshold: Choose the threshold that maximizes TPR while minimizing FPR.

Business Context: Adjust the threshold based on the cost of false positives/negatives.

Example: In fraud detection, a lower threshold may be preferred to catch more frauds, even if it increases false positives.

Case Study

Problem Statement:

Snapdeal wants to improve its recommendation system to display more relevant products to users. Your task as a data scientist is to analyze customer behavior, identify patterns, and suggest strategies to enhance the recommendation model.

Dataset Overview:

You have access to a dataset containing past user interactions with the Snapdeal platform. The dataset includes the following attributes:

• User_ID – Unique identifier for each user
• Product_ID – Unique identifier for each product
• Category – Category of the product (Electronics, Clothing, Home Decor, etc.)
• Brand – Brand name of the product
• Price – Price of the product
• User_Clicks – Number of times the user has clicked on a product
• User_Views – Number of times the user has viewed a product
• Purchase_Flag – Whether the user purchased the product (1 = Yes, 0 = No)
• Time_Spent_on_Product – Duration (in seconds) spent by the user on the product page
• Previous_Purchases – List of past product purchases by the user
• Ratings_Given – User’s rating for a purchased product (1 to 5)

Key Questions to Answer:

1. What are the key factors influencing product recommendations?

• Do users prefer recommendations based on their past purchases?
• How do product views and clicks impact the likelihood of purchase?
• Does price range influence recommendation success?

2. How can Snapdeal improve its recommendation system?

• Should recommendations be personalized based on browsing history?
• Can product similarity (based on brand, category, or price) improve recommendations?
• How can Snapdeal reduce irrelevant product suggestions?

3. What strategies can Snapdeal use to increase conversions?

• Should Snapdeal prioritize frequently purchased or highly rated products?
• Can real-time browsing behavior help refine recommendations?
• How can Snapdeal balance diversity and relevance in product recommendations?

Key Insights & Business Recommendations

1. Understanding User Behavior and Purchase Intent

• Clickstream Analysis: Users who spend more time viewing a product but do not purchase it might need price-based discounts or alternative recommendations.
• Category Preferences: Users often buy from the same category multiple times (e.g., a user purchasing a smartphone might also buy accessories).
• Ratings and Reviews Influence: Products with higher ratings and better reviews are more likely to be purchased. Snapdeal should prioritize them in recommendations.

2. Enhancing the Recommendation Model

• Personalized Recommendations: Using past purchases, browsing history, and click behavior to suggest relevant products.
• Collaborative Filtering: Recommending products based on what similar users have purchased.
• Content-Based Filtering: Suggesting products similar in category, brand, or price range to previously viewed items.
• Real-Time Recommendation Updates: If a user is actively browsing home decor, Snapdeal should prioritize similar products instantly.

3. Optimizing Product Discovery for Higher Conversions

• Trending Products Section: Featuring best-selling and high-rated products in recommendations.
• Limited-Time Discounts: If a user views a product multiple times but does not purchase, Snapdeal should offer a personalized discount.
• User Engagement Metrics: Analyzing click-through rates and time spent on product pages to refine recommendations dynamically.

4. Improving Customer Retention Through Smart Recommendations

• Post-Purchase Recommendations: After a user buys a smartphone, suggest accessories like phone cases and chargers.
• Cart Abandonment Alerts: If a user adds a product to the cart but does not buy it, send personalized recommendations or discounts.
• Cross-Selling Strategies: Users who purchase electronics might also be interested in extended warranties or related gadgets.

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