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

Company: Reliance Jio
Designation: Data Scientist
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: 12 LPA – 30 LPA

Reliance Jio, a subsidiary of Jio Platforms, is one of India’s leading telecommunications companies. Headquartered in Mumbai, Jio revolutionized the Indian telecom industry with its affordable 4G services and nationwide LTE network. If you’re preparing for a Data Science role at Jio, here’s a detailed breakdown of their interview process and the types of questions you can expect.

Jio Data Science Interview Questions

The following article contains Jio Data Science Interview Questions for young aspirers.

Interview Process

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

Round 1 – Telephonic Screening

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

SQL – 9/10

1) Write a SQL query to get the second highest query using sub query.

SELECT
Name, MAX(salary) AS salary
FROMTable_name
WHEREsalary < (SELECT MAX(salary) FROM Table_name); 

2) Write a SQL query to find all the student names Nitin in a table

select name
from student
where lower(name) like ‘%nitin%’

3) Write a query to get all the student with name length 10, starting with K and ending
with z.

select name
from student
where length(name)=10 and lower(name) like ‘k%z’

4) Write a SQL query to get the second highest query using Ranking

Note: Dense_rank() has been used to handle duplicate salaries if there are any.

With result as
{
19
selectsalary,
dense_rank()over(orderbysalarydesc)assalaryrank
fromemployees
}
select top1salary
fromresult
wheresalaryrank=2

5) Can you use HAVING command without any aggregate function in SQL?

No it’s not necessary for having to use aggregate functions and even without group by having
can exist.
Eg: This query works well in PostgreSql
select 1 having 1 = 1;

R/Python – 7/10 

1) Write a Python function that takes two lists as input and returns a new list containing only the elements that are common to both lists.

2) Write a Python function that takes a list as input and returns a new list with all duplicate elements removed.

3) Write a Python function that calculates the factorial of a given non-negative integer.

4) Write a Python function to check if two strings are anagrams of each other (contain the same characters in a different order).

5) Write a Python function to find the second largest number in a given list of numbers.

Statistics/ML

1) Is random weight assignment better than assigning the same weights to the units in the hidden layer?

Yes, random weight assignment is better than assigning the same weights to all hidden layer units. If all weights are initialized with the same value, each neuron will receive the same gradients during backpropagation and update in the same way, making them function identically. This prevents the network from learning useful patterns. Random initialization breaks symmetry, ensuring diverse feature learning.

2) When using the Gaussian mixture model, how do you know it’s applicable?

The Gaussian Mixture Model (GMM) is applicable when:

• The data exhibits multimodal distributions (multiple clusters).
• You suspect the data comes from a mixture of Gaussian distributions.
• The dataset has soft clusters rather than hard, well-separated clusters.
• Techniques like the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) help confirm if GMM is a good fit by comparing model performances.

3)  How will you tune hyperparameters in your model? Also, how will you test and know if they actually worked or not?

Hyperparameter tuning can be done using:

• Grid Search: Trying all possible combinations of hyperparameters.
• Random Search: Randomly selecting hyperparameters within a range.
• Bayesian Optimization: Using probability-based search to find the best parameters.
• Automated Methods: Such as Hyperopt or Optuna.
• To test if the tuning worked, compare performance metrics (accuracy, F1-score, RMSE, etc.) on a validation set before and after tuning. Cross-validation ensures the results are generalizable.

4) Why is it important to have information about the bias-variance trade-off while modeling?

The bias-variance trade-off helps balance underfitting and overfitting:

• Underfitting: The model is too simple and fails to capture patterns.
• Overfitting: The model is too complex and memorizes training data, failing on new data.
• A good model finds the right balance to generalize well to unseen data.

5) If you’re attempting to predict a customer’s gender, and you only have 100 data points, what problems could arise?

• Small sample size: Leads to unreliable patterns and overfitting.
• Class imbalance: If one gender is more common, the model may be biased.
• Lack of generalization: The model might not perform well on new data.
• Feature sparsity: If features are insufficient, predictions will be inaccurate.
• Statistical insignificance: Results may not be meaningful due to a lack of diverse data.

Case Study 

Problem Statement:

Jio has noticed that a significant percentage of users are discontinuing their services. As a data scientist, you are provided with a dataset containing customer information, usage patterns, and service details. Your goal is to build a churn prediction model, identify key factors influencing churn, and recommend strategies to reduce it.

Dataset Overview:

You are given a dataset with the following columns:

• Customer_ID – Unique identifier for each customer
• Tenure – Number of months the customer has been with Jio
• Monthly_Charges – The monthly bill amount
• Total_Charges – Total amount paid by the customer
• Internet_Service – Type of internet service (Fiber, Mobile Data, No Internet)
• Call_Drop_Rate – Percentage of dropped calls
• Data_Usage_GB – Total data usage in GB per month
• Customer_Complaints – Number of complaints filed
• Subscription_Type – Prepaid or Postpaid
• Churn – Whether the customer has churned (1 = Yes, 0 = No)

Key Questions to Answer:

1. What percentage of customers have churned?

• What is the overall churn rate?
• Is churn higher in specific customer segments?

2. What factors are most correlated with churn?

• Do high monthly charges lead to higher churn?
• Is there a link between call drop rate and churn?
• How does internet service type impact churn?

3. Can we build a predictive model to identify customers likely to churn?

• What machine learning algorithms can be used?
• How can we improve model accuracy?

4. What strategies can Jio implement to reduce churn?

• Should Jio offer personalized discounts to high-risk customers?
• Can improving network quality reduce churn?

Key Insights & Business Recommendations

1. Identify High-Risk Customers

Customers with high monthly charges, frequent complaints, and poor network experience are more likely to churn. Jio should prioritize these customers for retention efforts.

2. Offer Personalized Retention Strategies

For high-churn-risk customers, Jio can offer:

• Discounts on plans
• Free additional data
• Improved customer support

3. Improve Network Quality

A high call drop rate and poor internet experience contribute to churn. Investing in better network infrastructure can significantly reduce churn.

4. Introduce Loyalty Programs

A loyalty program rewarding long-term customers with exclusive benefits can improve customer retention.

Basic, you can practice a lot of case studies and other statistics topics here – https://thedatamonk.com/data-science-resources/

For any information related to courses or e-books, please send an email to [email protected]