Capgemini Data Scientist Interview Questions, Process, and Tips

Q1. Question on satatistics, python
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Q2. Questions on sql and machine learning
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Very easy and but selection is basis

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Q1. What is overfitting and underfitting
Ans. 
Overfitting occurs when a model learns the training data too well, leading to poor performance on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.
• Overfitting: Model is too complex, fits noise in the training data, performs poorly on new data

• Underfitting: Model is too simple, fails to capture underlying patterns in the data, performs poorly on both training and new...read more
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Q2. What are LLM Models
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LLM models, or Language Model Models, are a type of machine learning model that focuses on predicting the next word in a sequence of words.
• LLM models are commonly used in natural language processing tasks such as text generation, machine translation, and speech recognition.

• They are trained on large amounts of text data to learn the relationships between words and predict the most likely next word in a given context.

• Exa...read more
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Q1. Precision recall Gen AI etc.
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Q1. Project Related Questions
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Q1. Overall Experience and reasons for Job change
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Q1. 2 round technical interview, both of them about ML and last one concerning statistics and ML.
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Q2. What is linear regression?
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Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.
• It assumes a linear relationship between the variables

• It is used to predict the value of the dependent variable based on the independent variable(s)

• It can be simple linear regression (one independent variable) or multiple linear regression (more than one independent variable)

• It is commo...read more
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Q3. Question regarding confusion matrix?
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Q4. Name various ML algorithm?
Ans. 
ML algorithms are used to train models on data to make predictions or decisions. Some popular ones are SVM, KNN, and Random Forest.
• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Random Forest

• Naive Bayes

• Decision Trees

• Linear Regression

• Logistic Regression

• Neural Networks

• Gradient Boosting

• Clustering Algorithms (K-Means, Hierarchical)

• Association Rule Learning (Apriori)

• Dimensionality Reduction Algorithms (PCA, LDA)

• Reinf
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Q1. How do you handle outliers? How to handle imbalance dataset? Feature engineering techniques?
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Outliers can be handled by removing, transforming or imputing them. Imbalanced datasets can be handled by resampling techniques. Feature engineering involves creating new features from existing ones.
• Outliers can be removed using statistical methods like z-score or IQR.

• Outliers can be transformed using techniques like log transformation or box-cox transformation.

• Outliers can be imputed using techniques like mean imputat...read more
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Q1. 1. Explain the project in detail 2. Explain me your 5 favourite models 3. Questions on probability
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Q1. It was a HR round and HR has asked me what's your salary expectations.
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