Sia Partners Data Scientist Interview Questions and Answers

Q1. Projects and Data Science concepts
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Q1. Python and coding skills
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Q1. SQL basic questions
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Q2. Python - pandas, numpy based questions
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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?
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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 ?
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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. Basic statistics
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Q1. Project related
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Q1. How do you define model Gini?
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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
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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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Q1. Difference between CNN and MLP
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CNN is used for image recognition while MLP is used for general classification tasks.
• CNN uses convolutional layers to extract features from images while MLP uses fully connected layers.

• CNN is better suited for tasks that require spatial understanding like object detection while MLP is better for tabular data.

• CNN has fewer parameters than MLP due to weight sharing in convolutional layers.

• CNN can handle input of varying
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Q1. In depth statistics and deep learning questions.
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Q2. Machine Learning- 2 dependant and 6 independent variables are available, which algorithm we should use?
Ans. 
Use a regression algorithm like linear regression or decision tree regression.
• Consider using linear regression if the relationship between variables is linear.

• Decision tree regression can handle non-linear relationships between variables.

• Evaluate the performance of different algorithms using cross-validation.

• Consider the interpretability of the model when choosing an algorithm.
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