Mobikwik Data Analyst Interview Questions and Answers

Q1. Basics of Logistic regression
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Q2. What is Logistic Regression? What is Recall
Ans. 
Logistic Regression is a statistical method used to model the probability of a certain event occurring based on one or more predictor variables. Recall is a metric used to evaluate the performance of a classification model.
• Logistic Regression is used for binary classification tasks, where the outcome variable is categorical and has two classes.

• It estimates the probability that a given input belongs to a certain categor...read more
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Q3. What is Recall?
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Recall is a metric used in classification models to measure the ability of the model to find all relevant cases within a dataset.
• Recall is calculated as the number of true positive predictions divided by the sum of true positive and false negative predictions.

• It is also known as sensitivity or true positive rate.

• A high recall indicates that the model is good at identifying all relevant cases, even if it means more fals...read more
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Q4. What is the difference between linear and logistic reg.?
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Linear regression is used for predicting continuous values, while logistic regression is used for predicting binary outcomes.
• Linear regression is used when the target variable is continuous and has a linear relationship with the independent variables.

• Logistic regression is used when the target variable is binary or categorical and the relationship between independent variables and the target is non-linear.

• Linear regres...read more
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Q5. What are the performance metrics of Regression
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Performance metrics of Regression include Mean Squared Error, R-squared, Mean Absolute Error, etc.
• Mean Squared Error (MSE)

• R-squared (R^2)

• Mean Absolute Error (MAE)

• Root Mean Squared Error (RMSE)

• Adjusted R-squared
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Q1. Fibonacci sequence
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Q2. Databricks cluster questions
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Q3. Intermediate SQL questions from datalemur
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Q4. Python questions from geeksforgeeks
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Q5. ETL information
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Basic technology function of MODEM and it's working via SM to master .

Doubt in honesty while e and eco of functional programming.

Technical SQL Coding Round of 1 hour

Began with 2 easy generic question -
1. No. of record on different types of joins.
2. Employees who have higher salaries than their managers,

Then moved to medium questions -
1. Implement Rolling sum with & without window functions.
2. Other questions were on CTEs, Joins & other complex queries.

Bar Raiser Round of 1 hour involving -

1. Case study: How can we leverage product knowledge & analytics to increase Amazon Prime Revenue from 10% to 12%?

2. Metrics awareness: Common metrics discussion like DAU, PDAU, MAU, Retention, Churn, etc.

3. Resume based work exp discussion

Q1. Quick culture fitment round with HR discussing about past work experience, my expectations & why I want to join Jupiter.
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Statistics assignment on given dataset

Q1. Credit Risk, loan disbursement process, PD model , machine learning
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45 min , 45 questions

Q1. Technical question( PowerBI, data science)
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Q2. Behaviourial question
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