Astegic Data Analyst Interview Questions and Answers

Forecasting problem - Predict daily sku level sales

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 ...read more
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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...read more
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Easy and moderate level question

Given a relevant topic and discussion on it

Q1. Introducd yourself
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Q2. Why do you want to join us
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Q1. Mostly about projects ml, nlp, python code would be asked
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Q1. It would be focused on technical stuff in detail ml, nlp, deployments etc
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Q1. Organisation fit
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Q1. What is the full form of ETL?
Ans. 
ETL stands for Extract, Transform, Load. It is a process used in data warehousing to extract data from various sources, transform it into a consistent format, and load it into a target database.
• ETL stands for Extract, Transform, Load

• Extract: Involves extracting data from various sources such as databases, applications, and files

• Transform: Involves cleaning, filtering, and transforming the extracted data into a consiste...read more
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Q2. ETL Full form is Exact, Transform, Load.
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Reasoning and aptitude questions were asked

Q1. Interview was around python programming and team fitment
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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.

• ...read more
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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...read more
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