Data Containers Consulting BI Analyst Interview Questions and Answers

Q1. SQL and pandas coding
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Q2. Resume projects deep dive
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Q1. Tell me about yourself?
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I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
• Background in statistics and machine learning

• Experience in solving complex problems using data-driven approaches

• Passionate about leveraging data to drive insights and decision-making
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Q2. Describe in detail about one of my main project.
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Developed a predictive model for customer churn in a telecom company.
• Collected and cleaned customer data including usage patterns and demographics.

• Used machine learning algorithms such as logistic regression and random forest to build the model.

• Evaluated model performance using metrics like accuracy, precision, and recall.

• Implemented the model into the company's CRM system for real-time predictions.
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Q3. Few questions related to my projects.
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Q1. Questions on Basics python(Since i am fresher)
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Q1. About Machine learning basics, activation functions linear regression, cnn, all basics..
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Q2. About project questions, about sdlc basic 3 questions
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Q3. About Why not used another model for training?
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Q1. Complex sql question based on lead lag
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Q2. Question based on data index
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Q3. Question based on data engineer
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Q1. Pandas basics and SQL joins nlp
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Q1. What is stemming and lematization
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Stemming and lemmatization are techniques used in natural language processing to reduce words to their base or root form.
• Stemming is a process of reducing words to their base form by removing suffixes.

• Lemmatization is a process of reducing words to their base form by considering the context and part of speech.

• Stemming is faster but may not always produce a valid word, while lemmatization is slower but produces valid wo...read more
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Q2. Python coding about default values in class and funtions
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Q3. Kmeans clustering evaluation
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Q4. How to measure multicollinearity
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Multicollinearity can be measured using correlation matrix, variance inflation factor (VIF), or eigenvalues.
• Calculate the correlation matrix to identify highly correlated variables.

• Use the variance inflation factor (VIF) to quantify the extent of multicollinearity.

• Check for high eigenvalues in the correlation matrix, indicating multicollinearity.

• Consider using dimensionality reduction techniques like principal componen
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Numpy,pandas,data analysis

Q1. Difference between supervised and unsupervised learning
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Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data.
• Supervised learning requires a target variable for training the model.

• Examples of supervised learning include classification and regression.

• Unsupervised learning finds patterns and relationships in data without a target variable.

• Examples of unsupervised learning include clustering and dimensionality reduction.
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Q2. What is sigmoid function
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Sigmoid function is a mathematical function that maps any real value to a value between 0 and 1.
• Used in machine learning for binary classification problems to produce probabilities

• Commonly used in logistic regression

• Has an S-shaped curve

• Equation: f(x) = 1 / (1 + e^(-x))
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