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Imarticus Learning Data Scientist Interview Questions and Answers
Imarticus Learning Data Scientist Interview Experiences
1 interview found
Akash Anadure
Interview Questionnaire
3 Questions
- Q1. Tell me something about yourself?
- Q2. What is your strengths and weaknesses?
- Q3. What is your expected salary from our company?
Interview Preparation Tips
Top trending discussions
Interview questions from similar companies
Anonymous
I was interviewed in Sep 2024.
(2 Questions)
- Q1. Greatset number from an array
- Ans.
Find the greatest number from an array of strings.
Convert the array of strings to an array of integers.
Use a sorting algorithm to sort the array in descending order.
Return the first element of the sorted array as the greatest number.
- Q2. Questions related to Hypothesis testing
Skills evaluated in this interview
I applied via Monster and was interviewed in Oct 2023. There were 5 interview rounds.
Python Coding Test to test general knowledge on progamming
(1 Question)
- Q1. Explain Bias-Variance Tradeoff
- Ans.
Bias-variance tradeoff is the balance between model complexity and generalization error.
Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.
Variance refers to error from sensitivity to fluctuations in the training data, leading to overfitting.
Increasing model complexity reduces bias but increases variance, while decreasing complexity increases bias but reduces variance.
The...
(1 Question)
- Q1. Why should we hire you
(1 Question)
- Q1. Final round discussion on package and benefits
Skills evaluated in this interview
Selected
I applied via Referral and was interviewed before Mar 2023. There were 2 interview rounds.
(4 Questions)
- Q1. Interpretation of classification metrics like accuracy, precision, recall
- Ans.
Classification metrics like accuracy, precision, and recall are used to evaluate the performance of a classification model.
Accuracy measures the overall correctness of the model's predictions.
Precision measures the proportion of true positive predictions out of all positive predictions.
Recall measures the proportion of true positive predictions out of all actual positive instances.
- Q2. Difference between bagging and boosting
- Ans.
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, where each subsequent model c...
- Q3. Difference between R-squared and Adjusted R-squared
- Ans.
R-squared measures the proportion of variance explained by the model, while Adjusted R-squared adjusts for the number of predictors in the model.
R-squared is the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with 1 indicating a perfect fit.
Adjusted R-squared penalizes the addition of unnecessary predictors to the model, providing a more accur...
- Q4. Feature Selection Techniques
- Ans.
Feature selection techniques help in selecting the most relevant features for building predictive models.
Filter methods: Select features based on statistical measures like correlation, chi-squared test, etc.
Wrapper methods: Use a specific model to evaluate the importance of features by adding or removing them iteratively.
Embedded methods: Feature selection is integrated into the model training process, like LASSO regre...
(4 Questions)
- Q1. Various types of joins in SQL
- Ans.
Various types of joins in SQL include inner join, outer join, left join, right join, and full join.
Inner join: Returns rows when there is a match in both tables.
Outer join: Returns all rows when there is a match in one of the tables.
Left join: Returns all rows from the left table and the matched rows from the right table.
Right join: Returns all rows from the right table and the matched rows from the left table.
Full joi...
- Q2. SQL query on self join
- Q3. Bias and Variance Tradeoff
- Q4. Model interpretability
Skills evaluated in this interview
I applied via Company Website and was interviewed before Sep 2021. There were 6 interview rounds.
The coding test was a Hackerank test with 3 python and 2 SQL questions.
(3 Questions)
- Q1. This is a technical test with questions from Machine Learning and statistics.
- Q2. What is a Central Limit Theorem?
- Ans.
Central Limit Theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal.
The theorem applies to large sample sizes.
It is a fundamental concept in statistics.
It is used to estimate population parameters from sample statistics.
It is important in hypothesis testing and confidence intervals.
Example: If we take a large number of samples of the same size from...
- Q3. Can you explain gradient descent?
- Ans.
Gradient descent is an iterative optimization algorithm used to minimize a cost function by adjusting model parameters.
Gradient descent is used in machine learning to optimize models.
It works by iteratively adjusting model parameters to minimize a cost function.
The algorithm calculates the gradient of the cost function and moves in the direction of steepest descent.
There are different variants of gradient descent, such...
(4 Questions)
- Q1. This was related to Projects, what projects did you work on and domain-related questions
- Q2. What is Image segmentation?
- Ans.
Image segmentation is the process of dividing an image into multiple segments or regions.
It is used in computer vision to identify and separate objects or regions of interest in an image.
It can be done using various techniques such as thresholding, clustering, edge detection, and region growing.
Applications include object recognition, medical imaging, and autonomous vehicles.
Examples include separating the foreground a...
- Q3. Questions about output shape when an convolution operation is performed using a filter.
- Q4. How is object detection done using CNN?
- Ans.
Object detection using CNN involves training a neural network to identify and locate objects within an image.
CNNs use convolutional layers to extract features from images
These features are then passed through fully connected layers to classify and locate objects
Common architectures for object detection include YOLO, SSD, and Faster R-CNN
Analyze a scenario for the reduce in sales of a product in the end of the month.
(1 Question)
- Q1. One to One round with Manager, less technical but some case study and work culture related.
Interview Preparation Tips
- Machine Learning
- Deep Learning
- SQL
- Python
- Statistics
- Case study
- Scenario based questions
Always be prepared with the basics and understand your project completely.
Skills evaluated in this interview
I was interviewed in Sep 2024.
(2 Questions)
- Q1. Greatset number from an array
- Ans.
Find the greatest number from an array of strings.
Convert the array of strings to an array of integers.
Use a sorting algorithm to sort the array in descending order.
Return the first element of the sorted array as the greatest number.
- Q2. Questions related to Hypothesis testing
Skills evaluated in this interview
I applied via Monster and was interviewed in Oct 2023. There were 5 interview rounds.
Python Coding Test to test general knowledge on progamming
(1 Question)
- Q1. Explain Bias-Variance Tradeoff
- Ans.
Bias-variance tradeoff is the balance between model complexity and generalization error.
Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.
Variance refers to error from sensitivity to fluctuations in the training data, leading to overfitting.
Increasing model complexity reduces bias but increases variance, while decreasing complexity increases bias but reduces variance.
The...
(1 Question)
- Q1. Why should we hire you
(1 Question)
- Q1. Final round discussion on package and benefits
Skills evaluated in this interview
Selected
I applied via Referral and was interviewed before Mar 2023. There were 2 interview rounds.
(4 Questions)
- Q1. Interpretation of classification metrics like accuracy, precision, recall
- Ans.
Classification metrics like accuracy, precision, and recall are used to evaluate the performance of a classification model.
Accuracy measures the overall correctness of the model's predictions.
Precision measures the proportion of true positive predictions out of all positive predictions.
Recall measures the proportion of true positive predictions out of all actual positive instances.
- Q2. Difference between bagging and boosting
- Ans.
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, where each subsequent model c...
- Q3. Difference between R-squared and Adjusted R-squared
- Ans.
R-squared measures the proportion of variance explained by the model, while Adjusted R-squared adjusts for the number of predictors in the model.
R-squared is the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with 1 indicating a perfect fit.
Adjusted R-squared penalizes the addition of unnecessary predictors to the model, providing a more accur...
- Q4. Feature Selection Techniques
- Ans.
Feature selection techniques help in selecting the most relevant features for building predictive models.
Filter methods: Select features based on statistical measures like correlation, chi-squared test, etc.
Wrapper methods: Use a specific model to evaluate the importance of features by adding or removing them iteratively.
Embedded methods: Feature selection is integrated into the model training process, like LASSO regre...
(4 Questions)
- Q1. Various types of joins in SQL
- Ans.
Various types of joins in SQL include inner join, outer join, left join, right join, and full join.
Inner join: Returns rows when there is a match in both tables.
Outer join: Returns all rows when there is a match in one of the tables.
Left join: Returns all rows from the left table and the matched rows from the right table.
Right join: Returns all rows from the right table and the matched rows from the left table.
Full joi...
- Q2. SQL query on self join
- Q3. Bias and Variance Tradeoff
- Q4. Model interpretability
Skills evaluated in this interview
I applied via Company Website and was interviewed before Sep 2021. There were 6 interview rounds.
The coding test was a Hackerank test with 3 python and 2 SQL questions.
(3 Questions)
- Q1. This is a technical test with questions from Machine Learning and statistics.
- Q2. What is a Central Limit Theorem?
- Ans.
Central Limit Theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal.
The theorem applies to large sample sizes.
It is a fundamental concept in statistics.
It is used to estimate population parameters from sample statistics.
It is important in hypothesis testing and confidence intervals.
Example: If we take a large number of samples of the same size from...
- Q3. Can you explain gradient descent?
- Ans.
Gradient descent is an iterative optimization algorithm used to minimize a cost function by adjusting model parameters.
Gradient descent is used in machine learning to optimize models.
It works by iteratively adjusting model parameters to minimize a cost function.
The algorithm calculates the gradient of the cost function and moves in the direction of steepest descent.
There are different variants of gradient descent, such...
(4 Questions)
- Q1. This was related to Projects, what projects did you work on and domain-related questions
- Q2. What is Image segmentation?
- Ans.
Image segmentation is the process of dividing an image into multiple segments or regions.
It is used in computer vision to identify and separate objects or regions of interest in an image.
It can be done using various techniques such as thresholding, clustering, edge detection, and region growing.
Applications include object recognition, medical imaging, and autonomous vehicles.
Examples include separating the foreground a...
- Q3. Questions about output shape when an convolution operation is performed using a filter.
- Q4. How is object detection done using CNN?
- Ans.
Object detection using CNN involves training a neural network to identify and locate objects within an image.
CNNs use convolutional layers to extract features from images
These features are then passed through fully connected layers to classify and locate objects
Common architectures for object detection include YOLO, SSD, and Faster R-CNN
Analyze a scenario for the reduce in sales of a product in the end of the month.
(1 Question)
- Q1. One to One round with Manager, less technical but some case study and work culture related.
Interview Preparation Tips
- Machine Learning
- Deep Learning
- SQL
- Python
- Statistics
- Case study
- Scenario based questions
Always be prepared with the basics and understand your project completely.
Skills evaluated in this interview
I applied via Referral and was interviewed in Oct 2021. There were 5 interview rounds.
Interview Questionnaire
9 Questions
- Q1. About the pervious Project?
- Q2. How ensemble techniques works?
- Ans.
Ensemble techniques combine multiple models to improve prediction accuracy.
Ensemble techniques can be used with various types of models, such as decision trees, neural networks, and support vector machines.
Common ensemble techniques include bagging, boosting, and stacking.
Bagging involves training multiple models on different subsets of the data and combining their predictions through averaging or voting.
Boosting invol...
- Q3. Types of ensemble techniques?
- Ans.
Ensemble techniques combine multiple models to improve prediction accuracy.
Bagging: Bootstrap Aggregating
Boosting: AdaBoost, Gradient Boosting
Stacking: Meta-model combines predictions of base models
Voting: Combining predictions of multiple models by majority voting
- Q4. Explain bagging
- Ans.
Bagging is a technique used in machine learning to improve the stability and accuracy of a model by combining multiple models.
Bagging stands for Bootstrap Aggregating.
It involves creating multiple subsets of the original dataset by randomly sampling with replacement.
Each subset is used to train a separate model, and the final prediction is the average of all the predictions made by each model.
Bagging reduces overfittin...
- Q5. Explain bosting?
- Ans.
Boosting is an ensemble learning technique that combines multiple weak models to create a strong model.
Boosting iteratively trains weak models on different subsets of data
Each subsequent model focuses on the misclassified data points of the previous model
Final prediction is made by weighted combination of all models
Examples include AdaBoost, Gradient Boosting, XGBoost
- Q6. Difference between bias and variance
- Ans.
Bias is error due to erroneous assumptions in the learning algorithm. Variance is error due to sensitivity to small fluctuations in the training set.
Bias is the difference between the expected prediction of the model and the correct value that we are trying to predict.
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data.
High bias can cause an algorithm to m...
- Q7. Classification techniques?
- Ans.
Classification techniques are used to categorize data into different classes or groups based on certain features or attributes.
Common classification techniques include decision trees, logistic regression, k-nearest neighbors, and support vector machines.
Classification can be binary (two classes) or multi-class (more than two classes).
Evaluation metrics for classification include accuracy, precision, recall, and F1 scor...
- Q8. Explain about random forest
- Ans.
Random forest is an ensemble learning method for classification, regression and other tasks.
Random forest builds multiple decision trees and combines their predictions to improve accuracy.
It uses bagging technique to create multiple subsets of data and features for each tree.
Random forest reduces overfitting and is robust to outliers and missing values.
It can handle high-dimensional data and is easy to interpret featur...
- Q9. Many question on SQL and Python
Interview Preparation Tips
Skills evaluated in this interview
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