BCG Data Scientist Interview Questions and Answers for Experienced

Q1. Share details of projects done. But interviewer had his own bias in understanding.
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1 hour test with 3 python programming questions.

Q1. Are all the decision trees same in a random forest ?
Ans. 
No, decision trees in a random forest are different due to the use of bootstrapping and feature randomization.
• Decision trees in a random forest are trained on different subsets of the data through bootstrapping.

• Each decision tree in a random forest also considers only a random subset of features at each split.

• The final prediction in a random forest is made by aggregating the predictions of all individual decision trees
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Q2. How will you handle class imbalanced dataset to increase the f1 score ?
Ans. 
Handling class imbalanced dataset involves techniques like resampling, using different algorithms, adjusting class weights, and using ensemble methods.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

• Adjust class weights in the model to give more importance to the...read more
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Q1. What is Linear regression
Ans. 
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that minimi...read more
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Q2. What li list and tuple
Ans. 
List and tuple are both data structures in Python, but list is mutable while tuple is immutable.
• List is denoted by square brackets [] while tuple is denoted by parentheses ().

• Elements in a list can be changed, added, or removed, while elements in a tuple cannot be changed once defined.

• Lists are used when you need a collection of items that may change, while tuples are used for fixed collections of items.

• Example: list =
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Q3. Previous company project experience
Ans. 
Developed a predictive analytics model to forecast customer churn for a telecom company.
• Utilized machine learning algorithms such as logistic regression and random forests

• Cleaned and preprocessed large datasets to improve model accuracy

• Collaborated with cross-functional teams to gather domain knowledge and insights
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Coding Interview on Python for 15 minutes. Straight away to coding. Did one basic mistake first then others attended well. Basically from List operations, Pandas DF merging,concat etc.One simple pattern program etc.But no confimation yet from them

Q1. List compehension example,List multiplications,Pandas merge,join,concat,some pattern program general;
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Q2. List comprehension example, Pandas merging code etc
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Q1. What are optimizers
Ans. 
Optimizers are algorithms used to adjust the parameters of a model to minimize the error between predicted and actual values.
• Optimizers are used in machine learning to improve the accuracy of models.

• They work by adjusting the weights and biases of a model during training.

• Common optimizers include Gradient Descent, Adam, and RMSprop.

• The choice of optimizer depends on the type of problem and the characteristics of the da...read more
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Q2. What is RELU and activation functions
Ans. 
RELU is an activation function used in neural networks to introduce non-linearity.
• RELU stands for Rectified Linear Unit.

• It is a simple function that returns the input if it is positive, and 0 otherwise.

• It is commonly used in deep learning models due to its simplicity and effectiveness.

• Other activation functions include sigmoid, tanh, and softmax.
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Q1. What is homoscedasticity
Ans. 
Homoscedasticity refers to the assumption that the variance of errors is constant across all levels of the independent variable.
• Homoscedasticity is a key assumption in linear regression analysis.

• It indicates that the residuals (errors) have constant variance.

• If the residuals exhibit a pattern where the spread of points increases or decreases as the predicted values increase, it violates the assumption of homoscedastici...read more
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Q2. What is biasing and what is overfitting and underfitting
Ans. 
Biasing is the error due to overly simplistic assumptions in the learning algorithm. Overfitting is when a model is too complex and fits the training data too closely, leading to poor generalization. Underfitting is when a model is too simple to capture the underlying structure of the data.
• Biasing occurs when a model has high error on both training and test data due to oversimplified assumptions.

• Overfitting happens whe...read more
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Q1. Tell me about your project and your role into it
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Q2. What do you understand by Deep learning neural networks
Ans. 
Deep learning neural networks are a type of artificial neural network with multiple layers, used for complex pattern recognition.
• Deep learning neural networks consist of multiple layers of interconnected nodes, allowing for more complex patterns to be learned.

• They are capable of automatically learning features from data, eliminating the need for manual feature engineering.

• Examples include Convolutional Neural Networks ...read more
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Q1. Speaking in English
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Q2. Reading Writing
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Q1. Where do you want to join?
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Q2. What is your expected ctc?
Ans. 
I am looking for a competitive salary based on industry standards and my experience.
• Research industry standards for Data Scientist salaries

• Consider my level of experience and skills when determining salary expectations

• Be open to negotiation based on the overall compensation package offered
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Q1. What you can do as a Data Scientist
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Q2. Tell me your strengths and weaknesses
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Simple Data Science Case Study

Q1. Python knowledge related questions
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Data Science Case Study