AlgoAnalytics Financial Consultancy Data Science Intern Interview Questions and Answers

Q1. Types of Chunking in data preparation in RAG
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Q2. How Embedding works in Vector Databases
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Q3. Explain ARIMA model
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Q4. How can we decide to choose Linear Regression for a business problem
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Q1. What is token and it's limit for Open Source LLMs
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Q2. Difference of a Regression and Time Series problem
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Q3. Advantage of LSTM over RNN
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Q4. Performance Metrics for Logistic Regression
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Q1. Given a variable, how to do Linear Regression?
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Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Collect data on the variables of interest

• Plot the data to visualize the relationship between the variables

• Choose a suitable linear regression model (simple or multiple)

• Fit the model to the data using a regression algorithm (e.g. least squares)

• Evaluate the model's performance using ...read more
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Q2. How Linear Regression handles noise?
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Linear Regression minimizes noise by fitting a line that best represents the relationship between variables.
• Linear Regression minimizes the sum of squared errors between the actual data points and the predicted values on the line.

• It assumes that the noise in the data is normally distributed with a mean of zero.

• Outliers in the data can significantly impact the regression line and its accuracy.

• Regularization techniques l...read more
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Q3. Solve two equations to find coefficients?
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Use linear algebra to solve for coefficients in two equations.
• Set up the two equations with unknown coefficients

• Solve the equations simultaneously using methods like substitution or elimination

• Example: 2x + 3y = 10 and 4x - y = 5, solve for x and y
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Q4. Probability question on picking a red ball from Red, blue, black ball bag with replacement.
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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. How much experience do you have?
Ans. 
I have 8 years of experience in data science, with a focus on machine learning and predictive modeling.
• 8 years of experience in data science

• Specialize in machine learning and predictive modeling

• Worked on various projects involving big data analysis

• Experience with programming languages such as Python and R
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Q2. What is the tech stake you ahve worked on?
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I have worked on developing machine learning models for predictive maintenance in the manufacturing industry.
• Developed machine learning algorithms to predict equipment failures in advance

• Utilized sensor data and historical maintenance records to train models

• Implemented predictive maintenance solutions to reduce downtime and maintenance costs
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Q1. What is Gradient Descents?
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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. What is cross validation ?
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Cross validation is a technique used to assess the performance of a predictive model by splitting the data into training and testing sets multiple times.
• Cross validation helps to evaluate how well a model generalizes to new data.

• It involves splitting the data into k subsets, training the model on k-1 subsets, and testing it on the remaining subset.

• Common types of cross validation include k-fold cross validation and lea...read more
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Q1. What is Bert and transformer
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Bert and transformer are models used in natural language processing for tasks like text classification and language generation.
• Bert (Bidirectional Encoder Representations from Transformers) is a transformer-based model developed by Google for NLP tasks.

• Transformer is a deep learning model architecture that uses self-attention mechanisms to process sequential data like text.

• Both Bert and transformer have been widely use...read more
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Q2. NLP pre processing techniques
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NLP pre processing techniques involve cleaning and preparing text data for analysis.
• Tokenization: breaking text into words or sentences

• Stopword removal: removing common words that do not add meaning

• Lemmatization: reducing words to their base form

• Normalization: converting text to lowercase

• Removing special characters and punctuation
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Q1. Basic questions
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Q2. Strength weakness
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