Tiger Corporation Senior Data Science Analyst Interview Questions and Answers

Q1. What is bias v variance tradeoff
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Q2. When should bagging v boosting techniques be used
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Q1. Difference between fact and figure.
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Fact is a statement that can be proven true or false, while figure is a numerical value or statistic.
• Fact is a statement that can be verified or proven true or false.

• Figure is a numerical value or statistic.

• Facts are objective and can be verified through evidence or research.

• Figures are quantitative data used to represent information.

• Example: 'The sky is blue' is a fact, while 'The average temperature is 25 degrees Cel
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Q2. Explain data modelling
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Data modelling is the process of creating a visual representation of data to understand its structure, relationships, and patterns.
• Data modelling involves identifying entities, attributes, and relationships in a dataset.

• It helps in organizing data in a way that is easy to understand and analyze.

• Common data modelling techniques include Entity-Relationship (ER) diagrams and UML diagrams.

• Data modelling is essential for da...read more
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Q1. Design a dwmand planning system
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Design a demand planning system for efficient forecasting and inventory management.
• Utilize historical sales data to identify trends and seasonality

• Incorporate external factors like market trends, promotions, and competitor activities

• Implement machine learning algorithms for accurate demand forecasting

• Integrate with inventory management systems for optimized stock levels

• Regularly review and adjust the system based on pe
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Q2. Pick one point from resume and explain
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Implemented machine learning model to predict customer churn for a telecom company
• Developed and trained a machine learning model using Python and scikit-learn

• Utilized historical customer data to identify patterns and factors leading to churn

• Evaluated model performance using metrics such as accuracy, precision, and recall

• Provided actionable insights to the telecom company based on the model's predictions
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Q1. ML and deep learning questions
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Q1. Projects discussion
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Q2. Chatgpt architecture
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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
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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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(51+52+53+......+100) =

Q1. M.s excel , red hat ,
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Q2. Introdution M.s word
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Q3. Linux , git , mavel , nexus.
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Q4. Sonar cube , aws , super putty.
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Q1. What are Large Language Models?
Ans. 
Large Language Models are advanced AI models that can generate human-like text based on input data.
• Large Language Models use deep learning techniques to understand and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

• They are trained on vast amounts of text data to improve their language generation capabilities.
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Q2. Do you know about RAGs?
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RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
• RAGs is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates tasks or projects that are behind schedule or at risk.

• Amber signifies tasks or projects that are on track but may require attention.

• Green represents tasks or projects that ar...read more
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Q3. Which is the best clustering algorithm?
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There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
• The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.

• K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.

• DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...read more
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