TCS Data Scientist Interview Questions, Process, and Tips

Python, basics OOP, ML, MLOps

Q1. What is TSA? Explain the project as well.
Ans. 
TSA stands for Transportation Security Administration. It is a government agency responsible for security at airports and other transportation hubs.
• TSA was created in response to the September 11, 2001 terrorist attacks in the United States.

• Its main goal is to ensure the security of passengers and transportation infrastructure.

• TSA agents screen passengers and luggage for prohibited items before they board flights.

• The a...read more
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Q2. What is Polymorphism?
Ans. 
Polymorphism is the ability of a function or method to behave differently based on the object it is acting upon.
• Polymorphism allows objects of different classes to be treated as objects of a common superclass.

• It enables a single interface to represent multiple data types.

• Examples include method overloading and method overriding in object-oriented programming.
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Q1. NLP techniques, word stop,
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Q2. K-nearset neighout algorithm, gaussian mixtture model
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Q1. Optimization of ML algorithms, NLP project lifecycle, MLOPS related questions, Time Series questions
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Q1. Fitment interview, Get familiar with Big data tools before joining for better hold on projects
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Q1. Identify geometric alorithm pattern
Ans. 
Geometric algorithm patterns involve solving problems related to geometric shapes and structures.
• Identifying and solving problems related to points, lines, angles, and shapes

• Utilizing geometric formulas and theorems to find solutions

• Examples include calculating area, perimeter, angles, and distances in geometric figures
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Q2. If minimal data, which would you train for categorical prediction model?
Ans. 
I would train a decision tree model as it can handle categorical data well with minimal data.
• Decision tree models are suitable for categorical prediction with minimal data

• They can handle both numerical and categorical data

• Decision trees are easy to interpret and visualize

• Examples: predicting customer churn, classifying spam emails
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