HSBC Group Data Scientist Interview Questions and Answers for Freshers

Q1. How to extract numbers pre decimal point from a long list of decimalnumbers with efficiency
Ans. 
Use string manipulation to efficiently extract numbers before the decimal point from a list of decimal numbers.
• Split each decimal number by the decimal point and extract the number before it

• Use regular expressions to match and extract numbers before the decimal point

• Iterate through the list and extract numbers using string manipulation functions
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Q1. How do you define model Gini?
Ans. 
Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
• Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).

• It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.

• A Gini coefficient of 0.5 indicates a model that is no better than random guessing.

• Commonly used in credit
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Q2. How to you train XG boost model
Ans. 
XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
• Specify parameters for XGBoost model such as learning rate, max depth, and number of trees

• Split data into training and validation sets using train_test_split function

• Fit the XGBoost model on training data using fit method

• Tune hyperparameters using techniques like grid search
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Assignment on credit risk

Q1. Hyperparameter tuning
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Q1. Case study for problem solving
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Q1. What is Gradient Descents?
Ans. 
Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent.
• Gradient descent is used to find the minimum of a function by taking steps proportional to the negative of the gradient at the current point.

• It is commonly used in machine learning to optimize the parameters of a model by minimizing the loss function.

• There are different variants of gradie...read more
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Q2. What is LSTM?, and what are the gates in it?
Ans. 
LSTM (Long Short-Term Memory) is a type of recurrent neural network designed to handle long-term dependencies.
• LSTM has three gates: input gate, forget gate, and output gate.

• Input gate controls the flow of information into the cell state.

• Forget gate decides what information to discard from the cell state.

• Output gate determines the output based on the cell state.
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Q3. They gave me a link to dataset and started saying the operations to apply on that. E.g, value_counts, null_values, fill the values with mean,etc.
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Q4. What is t-test? What is Mean, Median and Mode and where to use these?
Ans. 
T-test is a statistical test used to determine if there is a significant difference between the means of two groups.
• Mean is the average of a set of numbers, median is the middle value when the numbers are ordered, and mode is the most frequently occurring value.

• Mean is sensitive to outliers, median is robust to outliers, and mode is useful for categorical data.

• T-test is used to compare means of two groups, mean is used...read more
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Q5. What is RANDOM FOREST ?
Ans. 
Random Forest is an ensemble learning method used for classification and regression tasks.
• Random Forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the forest makes a prediction, and the final prediction is the average (regression) or majority vote (classification) of all trees.

• Random Forest helps reduce overfitting and improve accuracy compared to a single decision tre...read more
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Q1. SQL basic questions
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Q2. Python - pandas, numpy based questions
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Medium General Aptitude questions and technical(Big Data, Python etc.)

Q1. ML Algorithms (SVM, Random forest, bagging boosting, ridge, etc)
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Q1. Deep equations and understading of DL and ML Algorithms
Ans. 
Understanding deep equations and algorithms in DL and ML is crucial for a data scientist.
• Deep learning involves complex neural network architectures like CNNs and RNNs.

• Machine learning algorithms include decision trees, SVM, k-means clustering, etc.

• Understanding the math behind algorithms helps in optimizing model performance.

• Equations like gradient descent, backpropagation, and loss functions are key concepts.

• Practica...read more
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