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Indiafilings Data Scientist Interview Questions and Answers
Indiafilings Data Scientist Interview Experiences
1 interview found
Anonymous
I applied via Approached by Company and was interviewed in May 2023. There were 3 interview rounds.
(2 Questions)
- Q1. What is the difference between sigmoid and softmax activation function?
- Ans.
Sigmoid is used for binary classification while softmax is used for multi-class classification.
Sigmoid function outputs values between 0 and 1, suitable for binary classification tasks.
Softmax function outputs a probability distribution over multiple classes, summing up to 1.
Sigmoid is used in the output layer for binary classification, while softmax is used for multi-class classification.
Softmax is the generalization
- Q2. What is an activation function ?
- Ans.
An activation function is a mathematical function that determines the output of a neural network.
Activation functions introduce non-linearity to the neural network, allowing it to learn complex patterns in the data.
Common activation functions include sigmoid, tanh, ReLU, and softmax.
The choice of activation function can impact the performance and training speed of the neural network.
Build an NLP model on their dataset
Interview Preparation Tips
Skills evaluated in this interview
Interview questions from similar companies
I applied via Recruitment Consulltant and was interviewed before Aug 2021. There was 1 interview round.
(1 Question)
- Q1. Difference between CNN and MLP
- Ans.
CNN is used for image recognition while MLP is used for general classification tasks.
CNN uses convolutional layers to extract features from images while MLP uses fully connected layers.
CNN is better suited for tasks that require spatial understanding like object detection while MLP is better for tabular data.
CNN has fewer parameters than MLP due to weight sharing in convolutional layers.
CNN can handle input of varying
Interview Preparation Tips
Skills evaluated in this interview
I appeared for an interview in May 2022.
Round duration - 60 Minutes
Round difficulty - Easy
(1 Question)
Round duration - 60 Minutes
Round difficulty - Easy
There were 10 MCQs ranging from Aptitude to Programming MCQs to basics of Data Science.
The coding question only the optimized solution was accepted
- Q1.
Special Sum of Array Problem Statement
Given an array 'arr' containing single-digit integers, your task is to calculate the total sum of all its elements. However, the resulting sum must also be a single-...
- Ans.
Calculate the total sum of array elements until a single-digit number is obtained by repeatedly summing digits.
Iterate through the array and calculate the sum of all elements.
If the sum is a single-digit number, return it. Otherwise, repeat the process of summing digits until a single-digit number is obtained.
Return the final single-digit sum.
(1 Question)
Round duration - 45 minutes
Round difficulty - Easy
The interview happened in the evening. It was an online video call.
The interviewer was very cooperative. I would say it was rather a discussion session between us.
- Q1.
Clone a Linked List with Random Pointers
Given a linked list where each node contains two pointers: one pointing to the next node and another random pointer that can point to any node within the list (or ...
- Ans.
Create a deep copy of a linked list with random pointers.
Iterate through the original linked list and create a new node for each node in the list.
Store the mapping of original nodes to new nodes in a hashmap to handle random pointers.
Update the random pointers of new nodes based on the mapping stored in the hashmap.
Return the head of the copied linked list.
Round duration - 10 Minutes
Round difficulty - Easy
It was late night
It was a telephonic call
Interview Preparation Tips
Tip 1 : Start your preparation early. Start from the very basics before directly moving onto DSA. Get a grasp of the basics in each topic. Practice different varieties of questions from each topic. I would recommend at least 200 questions of DSA.
Tip 2 : Revise your projects before you attend any interview. This is extremely important. You must be able to clearly explain your project along with your role in the project in layman terms to the interviewer.
Tip 3 : Grind hard to achieve your goals but don't take much stress. There's a long way to go.
Tip 1 : Never, I say never put false things or your friends project in your resume
Tip 2 : Make a 1 page resume. Make your resume in such a way that the interviewer must be able to see the things you want him to see in the very first scan.
Skills evaluated in this interview
(1 Question)
- Q1. How to extract numbers pre decimal point from a long list of decimalnumbers with efficiency
- Ans.
Use string manipulation to efficiently extract numbers before the decimal point from a list of decimal numbers.
Split each decimal number by the decimal point and extract the number before it
Use regular expressions to match and extract numbers before the decimal point
Iterate through the list and extract numbers using string manipulation functions
Skills evaluated in this interview
I applied via Walk-in and was interviewed in Mar 2020. There was 1 interview round.
Interview Questionnaire
10 Questions
- Q1. What is R square and how R square is different from Adjusted R square
- Ans.
R square is a statistical measure that represents the proportion of the variance in the dependent variable explained by the independent variables.
R square is a value between 0 and 1, where 0 indicates that the independent variables do not explain any of the variance in the dependent variable, and 1 indicates that they explain all of it.
It is used to evaluate the goodness of fit of a regression model.
Adjusted R square t...
- Q2. Explain what do u understand by the team WOE and IV. What's the importance. Advantages and disadvantages
- Q3. What are variable reducing techniques
- Ans.
Variable reducing techniques are methods used to identify and select the most relevant variables in a dataset.
Variable reducing techniques help in reducing the number of variables in a dataset.
These techniques aim to identify the most important variables that contribute significantly to the outcome.
Some common variable reducing techniques include feature selection, dimensionality reduction, and correlation analysis.
Fea...
- Q4. Which test is used in logistic regression to check the significance of the variable
- Ans.
The Wald test is used in logistic regression to check the significance of the variable.
The Wald test calculates the ratio of the estimated coefficient to its standard error.
It follows a chi-square distribution with one degree of freedom.
A small p-value indicates that the variable is significant.
For example, in Python, the statsmodels library provides the Wald test in the summary of a logistic regression model.
- Q5. How to check multicollinearity in Logistic regression
- Ans.
Multicollinearity in logistic regression can be checked using correlation matrix and variance inflation factor (VIF).
Calculate the correlation matrix of the independent variables and check for high correlation coefficients.
Calculate the VIF for each independent variable and check for values greater than 5 or 10.
Consider removing one of the highly correlated variables or variables with high VIF to address multicollinear...
- Q6. Difference between bagging and boosting
- Ans.
Bagging and boosting are ensemble methods used in machine learning to improve model performance.
Bagging involves training multiple models on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves iteratively training models on the same dataset, with each subsequent model focusing on the samples that were misclassified by the previous model.
Bagging reduc...
- Q7. Explain the logistics regression process
- Ans.
Logistic regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.
It is a type of regression analysis used for predicting the outcome of a categorical dependent variable based on one or more predictor variables.
It uses a logistic function to model the probability of the dependent variable taking a particular value.
It is commo...
- Q8. Explain Gini coefficient
- Ans.
Gini coefficient measures the inequality among values of a frequency distribution.
Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.
It is commonly used to measure income inequality in a population.
A Gini coefficient of 0.4 or higher is considered to be a high level of inequality.
Gini coefficient can be calculated using the Lorenz curve, which plots the cumulati...
- Q9. Difference between chair and cart
- Ans.
A chair is a piece of furniture used for sitting, while a cart is a vehicle used for transporting goods.
A chair typically has a backrest and armrests, while a cart does not.
A chair is designed for one person to sit on, while a cart can carry multiple items or people.
A chair is usually stationary, while a cart is mobile and can be pushed or pulled.
A chair is commonly found in homes, offices, and public spaces, while a c...
- Q10. How to check outliers in a variable, what treatment should you use to remove such outliers
- Ans.
Outliers can be detected using statistical methods like box plots, z-score, and IQR. Treatment can be removal or transformation.
Use box plots to visualize outliers
Calculate z-score and remove data points with z-score greater than 3
Calculate IQR and remove data points outside 1.5*IQR
Transform data using log or square root to reduce the impact of outliers
Interview Preparation Tips
Skills evaluated in this interview
Data Scientist Interview Questions & Answers
Bajaj Finserv
Vaibhav Diwakar Gavli
posted on 6 Jan 2025
SQL & aptitude question
1 coding question for 45 min
(1 Question)
- Q1. Detailed questing for machine learning model's.
laltaki
I applied via Company Website and was interviewed before Feb 2023. There was 1 interview round.
(1 Question)
- Q1. ML concepts , regression, regularization etc
I applied via Approached by Company and was interviewed before Sep 2021. There were 3 interview rounds.
(1 Question)
- Q1. Projects and Data Science concepts
(1 Question)
- Q1. Python and coding skills
Interview Preparation Tips
I applied via Referral and was interviewed in Nov 2024. There were 2 interview rounds.
(4 Questions)
- Q1. Types of Chunking in data preparation in RAG
- Q2. How Embedding works in Vector Databases
- Q3. Explain ARIMA model
- Q4. How can we decide to choose Linear Regression for a business problem
(4 Questions)
- Q1. What is token and it's limit for Open Source LLMs
- Q2. Difference of a Regression and Time Series problem
- Q3. Advantage of LSTM over RNN
- Q4. Performance Metrics for Logistic Regression
DEVANG RATHOD
(1 Question)
- Q1. Central Limit Theorem
- Ans.
Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
The Central Limit Theorem is essential in statistics as it allows us to make inferences about a population based on a sample.
It states that regardless of the shape of the population distribution, the sampling distribution of the sample mean will be approximately normally distribut...
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