GE Aerospace Data Science Intern Interview Questions, Process, and Tips for Freshers

This round majorly consisted of verbal, non verbal, and reasoning related question with a difficulty level ranging from easy to moderate.

Consisted of 2 coding question. One question of a easy level and one medium level question.

Q1. Project Related
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Q2. Scenario based questions
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Q1. Introduces yourself
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I am a data science enthusiast with a background in statistics and machine learning.
• Background in statistics and machine learning

• Passionate about data science

• Experience with data analysis tools like Python and R
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Q2. Scenario based question
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DSA Question - Trees

Q1. What are Regularization Techniques ?
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Regularization techniques are methods used to prevent overfitting in machine learning models by adding a penalty term to the loss function.
• Regularization techniques help in reducing the complexity of the model by penalizing large coefficients.

• Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net regularization.

• Regularization helps in improving the generalization ...read more
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Q2. When all the performance matrices have the high difference, on which performance matrix you reply?
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Q3. Scenario-based question - what parameters are important for deciding whether a man can reach the bus stop?
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Q4. Formulas for Precision, Recall, accuracy, F1 Score.
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Formulas for Precision, Recall, Accuracy, F1 Score in data science.
• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• Accuracy = (TP + TN) / (TP + TN + FP + FN)

• F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
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Q5. How to Choose K value in K means? if there are any techniques, name them and explain.
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Choosing the optimal K value in K-means clustering is crucial for accurate results.
• Elbow method: Plotting the sum of squared distances vs. K and selecting the K value where the curve bends like an elbow.

• Silhouette method: Calculating the average silhouette score for different K values and choosing the one with the highest score.

• Gap statistic method: Comparing the within-cluster dispersion to a reference null distributi...read more
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Q6. What is Population and Sample
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Population refers to the entire group of individuals or items that we are interested in studying, while a sample is a subset of the population.
• Population is the larger group that we want to draw conclusions about.

• Sample is a smaller group selected from the population to represent it.

• Population parameters are characteristics of the entire group, while sample statistics are characteristics of the sample.

• Example: Populati...read more
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Q7. What is Hypothesis testing
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Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• It involves formulating a hypothesis about a population parameter, collecting data, and using statistical tests to determine if the data supports or rejects the hypothesis.

• There are two types of hypotheses: null hypothesis (H0) and alternative hypothesis (H1).

• Common statistical tests for hypothesis testing include...read more
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Q8. OverFitting and underfitting conditions and example
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Overfitting and underfitting are common issues in machine learning where the model either learns the noise in the training data or fails to capture the underlying patterns.
• Overfitting occurs when a model learns the training data too well, including noise and outliers, leading to poor generalization on new data.

• Underfitting happens when a model is too simple to capture the underlying patterns in the data, resulting in h...read more
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Q9. Name the project you worked on which belongs to supervised learning and explain
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