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GE Aerospace Data Science Intern Interview Questions, Process, and Tips
Top GE Aerospace Data Science Intern Interview Questions and Answers
- Q1. How to Choose K value in K means? if there are any techniques, name them and explain.
- Q2. Formulas for Precision, Recall, accuracy, F1 Score.
- Q3. OverFitting and underfitting conditions and example
GE Aerospace Data Science Intern Interview Experiences
2 interviews found
Anonymous
Selected
I applied via Company Website and was interviewed in Jul 2024. There were 4 interview rounds.
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.
(2 Questions)
- Q1. Project Related
- Q2. Scenario based questions
(2 Questions)
- Q1. Introduces yourself
- Ans.
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
- Q2. Scenario based question
I was interviewed in May 2024.
DSA Question - Trees
(9 Questions)
- Q1. What are Regularization Techniques ?
- Ans.
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 ...
- Q2. When all the performance matrices have the high difference, on which performance matrix you reply?
- Q3. Scenario-based question - what parameters are important for deciding whether a man can reach the bus stop?
- Q4. Formulas for Precision, Recall, accuracy, F1 Score.
- Ans.
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)
- Q5. How to Choose K value in K means? if there are any techniques, name them and explain.
- Ans.
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...
- Q6. What is Population and Sample
- Ans.
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...
- Q7. What is Hypothesis testing
- Ans.
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...
- Q8. OverFitting and underfitting conditions and example
- Ans.
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...
- Q9. Name the project you worked on which belongs to supervised learning and explain
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
Selected
I applied via Campus Placement and was interviewed in Nov 2024. There were 3 interview rounds.
There were verbal, non verbal, reasoning , English and maths questions
(2 Questions)
- Q1. Tell me about your project.
- Ans.
I worked on a project analyzing customer behavior using machine learning algorithms.
Used Python for data preprocessing and analysis
Implemented machine learning models such as decision trees and logistic regression
Performed feature engineering to improve model performance
- Q2. What programming knowledge you have ?
- Ans.
Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.
Proficient in Python for data analysis and machine learning tasks
Experience with R for statistical analysis and visualization
Knowledge of SQL for querying databases and extracting data
Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
(2 Questions)
- Q1. Where do you stay ?
- Ans.
I currently stay in an apartment in downtown area.
I stay in an apartment in downtown area
My current residence is in a city
I live close to my workplace
- Q2. Tell me about you
- Ans.
I am a data science enthusiast with a strong 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
I applied via Campus Placement
(1 Question)
- Q1. ML and deep learning questions
(2 Questions)
- Q1. Projects discussion
- Q2. Chatgpt architecture
Basic DSA questions will be asked Leetcode Easy to medium
(2 Questions)
- Q1. BERT vs LSTM and their speed
- Ans.
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.
BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.
For exa...
- Q2. What is multinomial Naive Bayes theorem
- Ans.
Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
It is commonly used in text classification tasks, such as spam detection or sentiment analysis.
It is suitable for features that represent counts or frequencies, like word counts in text data.
It calculates the probability of each class given the input features and selects the class with the
Skills evaluated in this interview
Selected
I applied via campus placement at Chennai Mathematical Institute, Chennai and was interviewed in Dec 2023. There was 1 interview round.
(3 Questions)
- Q1. What are Large Language Models?
- Ans.
Large Language Models are advanced AI models that can generate human-like text based on input data.
Large Language Models use deep learning techniques to understand and generate text.
Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).
They are trained on vast amounts of text data to improve their language generation capabilities.
- Q2. Do you know about RAGs?
- Ans.
RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
RAGs is commonly used in project management to quickly communicate the status of tasks or projects.
Red typically indicates tasks or projects that are behind schedule or at risk.
Amber signifies tasks or projects that are on track but may require attention.
Green represents tasks or projects that ar...
- Q3. Which is the best clustering algorithm?
- Ans.
There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.
K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.
DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...
Skills evaluated in this interview
Selected
I applied via Campus Placement and was interviewed in Jun 2024. There were 2 interview rounds.
Some basic aptitude questions were asked , but had to be solved in 20 minutes
Medium level 2 leet code questions were asked and i cleared both
Selected
I was interviewed in Dec 2023.
English, number system, grammar
Python , data science, machine learning
Python machine learning, natural language precossing
(4 Questions)
- Q1. Python , Data Science, SQL
- Q2. What is python basics, libraries
- Ans.
Python basics include syntax, data types, and control structures. Libraries like NumPy, Pandas, and Matplotlib enhance data analysis and visualization.
Python basics cover syntax, variables, data types, and control structures.
NumPy is a library for numerical computing, providing powerful array operations.
Pandas is a library for data manipulation and analysis, offering data structures like DataFrames.
Matplotlib is a libr...
- Q3. Data science algorithms , and theory
- Q4. Sql querys, cluases
Indian environment, village, college days
(1 Question)
- Q1. Python, data science
Arbaz Ali
Selected
I applied via Approached by Company and was interviewed in Dec 2023. There was 1 interview round.
(3 Questions)
- Q1. How many working days
- Q2. Company give another benefit
- Q3. Work load is high or low
(1 Question)
- Q1. Basic python and machine learning questions
(1 Question)
- Q1. Salary expectation and working location related questions and why you want to join wipro
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