Impressico Business Solutions Jr. Data Scientist Interview Questions and Answers

Q1. Asked about coalese in sql and its syntax.
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Q2. Sql from leetcode on windows function and joins and cte
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Q1. Questions based on my work experience, basic level of python coding (return counts for duplicate integers in a list), theory questions on basic DL (activation layers, time series)
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Q1. Explain end to end ML project
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End to end ML project involves data collection, preprocessing, model training, evaluation, and deployment.
• 1. Data collection: Gather relevant data from various sources.

• 2. Data preprocessing: Clean, transform, and prepare the data for modeling.

• 3. Model training: Develop and train machine learning models using the processed data.

• 4. Model evaluation: Assess the performance of the models using metrics like accuracy, precis...read more
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Q1. About CNN architecture and how it is relevant for textual data
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Q2. Questions related to probability therom
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Good questions. 3 SQL case studies at end

Q1. Power BI, SQL, Excel technical questions
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I was given assigment on a simple problem where task was to analyse and create a working solution for a problem statement

Q1. What is bert algorithm? How does it work.
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BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained natural language processing model.
• BERT is a transformer-based machine learning algorithm developed by Google.

• It is designed to understand the context of words in a sentence by considering both the left and right context simultaneously.

• BERT has been pre-trained on a large corpus of text data and can be fine-tuned for specific NLP tasks like ...read more
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Q2. Can you explain regression in logistic regression?
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Logistic regression is a type of regression analysis used to predict the probability of a binary outcome.
• Logistic regression is used when the dependent variable is binary (e.g. 0 or 1, yes or no).

• It estimates the probability that a given input belongs to a certain category.

• The output of logistic regression is transformed using a sigmoid function to ensure it falls between 0 and 1.

• It uses the logistic function to model ...read more
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Q1. What is r squared value
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R-squared value is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
• R-squared value ranges from 0 to 1, with 1 indicating a perfect fit.

• It is used to evaluate the goodness of fit of a regression model.

• A higher R-squared value indicates that the model explains a larger proportion of the variance in the dependent variable.

• F...read more
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