Dell Data Science Engineer Interview Questions and Answers

Q1. Exptected salary
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Q2. What do you expect for work
Ans. 
I expect challenging projects, opportunities for growth, collaborative team environment, and work-life balance.
• Challenging projects that allow me to apply my data science skills and learn new techniques

• Opportunities for growth and advancement within the company

• Collaborative team environment where I can share ideas and work together towards common goals

• Work-life balance to ensure I can perform at my best both profession
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Topic was joining and calculating

Basic python questions

Q1. Explain Projects worked on?
Ans. 
Developed predictive models for customer churn and sales forecasting using machine learning algorithms.
• Built a customer churn prediction model using logistic regression and random forest algorithms.

• Implemented a time series forecasting model for sales prediction using ARIMA and LSTM neural networks.

• Utilized Python libraries such as pandas, scikit-learn, and TensorFlow for data preprocessing and model building.
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Q2. Explain what are GANs?
Ans. 
GANs are Generative Adversarial Networks, a type of deep learning model consisting of two neural networks - a generator and a discriminator.
• GANs are used to generate new data samples that resemble a given dataset.

• The generator network creates fake data samples, while the discriminator network tries to distinguish between real and fake samples.

• The two networks are trained simultaneously in a competitive manner, improvin...read more
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Q3. Any Research Paper published?
Ans. 
Yes, I have published a research paper on the topic of machine learning algorithms for predictive analytics.
• Published research paper on machine learning algorithms

• Focused on predictive analytics

• Presented findings at a data science conference
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Q4. Explain working of Neural networks?
Ans. 
Neural networks are a type of machine learning algorithm inspired by the human brain's neural structure.
• Neural networks consist of layers of interconnected nodes (neurons) that process input data and pass it through activation functions.

• They use weights to adjust the strength of connections between neurons during training.

• Neural networks are capable of learning complex patterns and relationships in data, making them su...read more
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Q1. Explain xgboost
Ans. 
XGBoost is a popular machine learning algorithm known for its speed and performance in gradient boosting.
• XGBoost stands for eXtreme Gradient Boosting.

• It is an implementation of gradient boosted decision trees designed for speed and performance.

• XGBoost is widely used in machine learning competitions and has become a popular choice for data scientists.

• It can handle missing data and is optimized for parallel processing.

• XG...read more
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Q2. Explain decision tree
Ans. 
Decision tree is a predictive modeling tool that uses a tree-like graph of decisions and their possible consequences.
• Decision tree is a supervised learning algorithm used for classification and regression tasks.

• It breaks down a dataset into smaller subsets based on different attributes.

• Each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome...read more
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Ml algorithms ,deeplearning, nlp questioons and projects

Python basic codeing

Q1. 1. diff between acf pacf
Ans. 
ACF measures the linear relationship between an observation and its lagged values. PACF measures the direct relationship.
• ACF (Autocorrelation Function) measures the correlation between an observation and its lagged values.

• PACF (Partial Autocorrelation Function) measures the correlation between an observation and its lagged values, while removing the indirect effects of intermediate lags.

• ACF is used to identify the orde...read more
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15 statistical and logical questions

2 easy to medium coding problmes. e.g. swapping the array.

Q1. What is regression ? how it works
Ans. 
Regression is a statistical method used to analyze the relationship between variables and predict outcomes.
• Regression models the relationship between a dependent variable and one or more independent variables.

• It works by finding the best-fit line that minimizes the sum of squared differences between the actual and predicted values.

• Examples include linear regression, polynomial regression, and logistic regression.
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Q2. Questions on recent projects
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Q1. Basic back ground check
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I was asked to solve various problems (your typical algorithm and data structure subjects), as well as explain the various projects I worked on in my most recent position.

Divide candidates in a group of around 15 people, and put you through different activities such as role play exercises to measure your communication and team working skills.