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TechVantage Systems Data Scientist Interview Questions and Answers
TechVantage Systems Data Scientist Interview Experiences
1 interview found
Anonymous
Selected
I applied via LinkedIn and was interviewed before Jul 2023. There were 4 interview rounds.
Basic stat, sql, python, ML related questions
Give a group project and discuss about how you would do that
(2 Questions)
- Q1. Explain favourite ml algorithm
- Ans.
My favorite ML algorithm is Random Forest, as it is versatile, easy to use, and provides high accuracy.
Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.
It can handle both regression and classification tasks.
Random Forest is less prone to overfitting compared to individual decision trees.
It can handle large data sets wi...
- Q2. Intuition behind neural networks
- Ans.
Neural networks are a type of machine learning model inspired by the human brain, consisting of interconnected nodes that process information.
Neural networks consist of layers of interconnected nodes, each performing a specific function.
They use activation functions to introduce non-linearity into the model.
Neural networks learn by adjusting the weights of connections between nodes through a process called backpropagat...
(1 Question)
- Q1. How would you see yourself in 5 years.
Skills evaluated in this interview
Data Scientist Jobs at TechVantage Systems
Top trending discussions
Interview questions from similar companies
(2 Questions)
- Q1. What is overfitting in machine learning?
- Ans.
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
- Q2. Overfitting accurs when a model learns the details.......etc
- Ans.
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
Interview Preparation Tips
Forecasting problem - Predict daily sku level sales
(2 Questions)
- Q1. What is difference between bias and variance
- Ans.
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
- Q2. Parametric vs non parametruc model
- Ans.
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
Skills evaluated in this interview
Data Scientist Interview Questions & Answers
Thinkitive Technologies
Ganesh Patil
posted on 29 Feb 2024
Selected
I applied via Approached by Company and was interviewed before Mar 2023. There were 3 interview rounds.
(1 Question)
- Q1. Mostly about projects ml, nlp, python code would be asked
(1 Question)
- Q1. It would be focused on technical stuff in detail ml, nlp, deployments etc
(1 Question)
- Q1. Organisation fit
(2 Questions)
- Q1. What is overfitting in machine learning?
- Ans.
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
- Q2. Overfitting accurs when a model learns the details.......etc
- Ans.
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
Interview Preparation Tips
Forecasting problem - Predict daily sku level sales
(2 Questions)
- Q1. What is difference between bias and variance
- Ans.
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
- Q2. Parametric vs non parametruc model
- Ans.
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
Skills evaluated in this interview
I applied via Naukri.com and was interviewed before Mar 2023. There were 3 interview rounds.
(1 Question)
- Q1. Machine Learning
(1 Question)
- Q1. Machine Learning
Approach check for multiple case studies
Interview Preparation Tips
Data Scientist Interview Questions & Answers
Thinkitive Technologies
Ganesh Patil
posted on 29 Feb 2024
Selected
I applied via Approached by Company and was interviewed before Mar 2023. There were 3 interview rounds.
(1 Question)
- Q1. Mostly about projects ml, nlp, python code would be asked
(1 Question)
- Q1. It would be focused on technical stuff in detail ml, nlp, deployments etc
(1 Question)
- Q1. Organisation fit
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Rating in categories
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Data Scientist
15
salaries
|  ₹5 L/yr - ₹9 L/yr |
|
Associate Data Scientist
13
salaries
| ₹2.9 L/yr - ₹6 L/yr |
|
Senior Software Engineer
7
salaries
| ₹6.3 L/yr - ₹17 L/yr |
|
Software Engineer
5
salaries
| ₹3 L/yr - ₹8 L/yr |
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Software Developer
4
salaries
| ₹4.2 L/yr - ₹10 L/yr |
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