KavyaSahithi Books Lead Data Analyst Interview Questions and Answers

Q1. What about your self?
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Q2. Family background
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Q3. Power BI test and advanced excel
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Q4. Microsoft access test
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Q5. Python test and One to one discussion with super boss
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Q1. Tell me about yourself?
Ans. 
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.
Ans. 
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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The first round was easy which consist of basic aptitude questions including java,c,cpp,dbms,sql,reasoning,etc

Q1. Explain Data Analysis Life Cycle
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Q1. Deep questions about Machine learning, Deep learning, Generative AI, the working of transformers etc.
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Q1. Deep questions about Machine learning and deep learning with projects done. This was a client round.
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Q1. Salary discussion, project discussion, why change? Why Wipro
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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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Basic DSA questions will be asked Leetcode Easy to medium

Q1. BERT vs LSTM and their speed
Ans. 
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
• BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.

• BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.

• For exa...read more
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Q2. What is multinomial Naive Bayes theorem
Ans. 
Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
• It is commonly used in text classification tasks, such as spam detection or sentiment analysis.

• It is suitable for features that represent counts or frequencies, like word counts in text data.

• It calculates the probability of each class given the input features and selects the class with the
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Numpy,pandas,data analysis

Q1. Statistical concepts, Machine learning concepts, project related questions
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Q2. Confusion matrix
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Q3. What is standardization
Ans. 
Standardization is the process of rescaling the features so that they have the properties of a standard normal distribution with a mean of 0 and a standard deviation of 1.
• Standardization helps in comparing different features on a common scale.

• It is useful when the features have different units or scales.

• Commonly used in machine learning algorithms like support vector machines and k-nearest neighbors.

• Example: If one fea...read more
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Q4. What is normalization
Ans. 
Normalization is the process of scaling and standardizing data to a common range.
• Normalization helps in comparing different features on the same scale.

• Common techniques include Min-Max scaling and Z-score normalization.

• Example: Scaling age and income variables to a range of 0 to 1.
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Q1. Machine learning concepts, share screen and write code in python
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