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TCS Data Scientist Interview Questions and Answers

Updated 5 Apr 2025

29 Interview questions

A Data Scientist was asked
Q. What is the difference between recall and precision?
Ans. 

Recall is the ratio of correctly predicted positive observations to the all observations in actual class, while precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

  • Recall is about the actual positive instances that were correctly identified by the model.

  • Precision is about the predicted positive instances and how many of them were actually positive.

  • Recall...

A Data Scientist was asked
Q. If you have minimal data, which model would you train for categorical prediction?
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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A Data Scientist was asked
Q. What is the bias-variance tradeoff?
Ans. 

Bias-variance trade off is the balance between underfitting and overfitting in machine learning models.

  • Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.

  • Variance refers to error from sensitivity to small fluctuations in the training set, leading to overfitting.

  • The trade off involves finding the right level of model complexity to minimize both bias and variance.

  • Regul...

A Data Scientist was asked
Q. What is the difference between k-means and k-NN?
Ans. 

K-means is a clustering algorithm while KNN is a classification algorithm.

  • K-means is unsupervised learning, KNN is supervised learning

  • K-means partitions data into K clusters based on distance, KNN classifies data points based on similarity to K neighbors

  • K-means requires specifying the number of clusters (K), KNN requires specifying the number of neighbors (K)

  • Example: K-means can be used to group customers based on...

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A Data Scientist was asked
Q. How does RNN handle exploding or vanishing gradients?
Ans. 

RNN uses techniques like gradient clipping, weight initialization, and LSTM/GRU cells to handle exploding/vanishing gradients.

  • Gradient clipping limits the magnitude of gradients during backpropagation.

  • Weight initialization techniques like Xavier initialization help in preventing vanishing gradients.

  • LSTM/GRU cells have gating mechanisms that allow the network to selectively remember or forget information.

  • Batch norm...

A Data Scientist was asked
Q. Explain the difference between Faster-RCNN and YOLO v3.
Ans. 

Faster-RCNN and Yolo v3 are both object detection algorithms, but differ in their approach and performance.

  • Faster-RCNN uses a two-stage approach, first generating region proposals and then classifying them.

  • Yolo v3 uses a single-stage approach, directly predicting bounding boxes and class probabilities.

  • Faster-RCNN is generally more accurate but slower, while Yolo v3 is faster but less accurate.

  • Faster-RCNN is better...

A Data Scientist was asked 6mo ago
Q. 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.

  • ...

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A Data Scientist was asked
Q. How to remove stop words and how it works
Ans. 

Stop words are common words like 'the', 'is', 'and' that are removed from text data to improve analysis.

  • Stop words are commonly removed from text data to improve the accuracy of natural language processing tasks.

  • They are typically removed before tokenization and can be done using libraries like NLTK or spaCy.

  • Examples of stop words include 'the', 'is', 'and', 'in', 'on', etc.

A Data Scientist was asked
Q. What are Logist regression and tell me about your projects
Ans. 

Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.

  • Logistic regression is used when the dependent variable is binary (e.g., 0/1, yes/no, true/false).

  • It estimates the probability that a given observation belongs to a particular category.

  • It uses the logistic function to model the relationship between the dependent variable and indep...

A Data Scientist was asked
Q. How does decision tree algorithm work, what is cross entropy..
Ans. 

Decision tree algorithm is a tree-like model used for classification and regression. Cross entropy is a measure of the difference between two probability distributions.

  • Decision tree algorithm recursively splits the data into subsets based on the most significant attribute until a stopping criterion is met.

  • It is a popular algorithm for both classification and regression tasks.

  • Cross entropy is used as a loss functio...

TCS Data Scientist Interview Experiences

36 interviews found

Interview experience
3
Average
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Not Selected

I appeared for an interview in Oct 2024.

Round 1 - Technical 

(1 Question)

  • Q1. Project related questions from your CV
Round 2 - Technical 

(2 Questions)

  • Q1. Question on transformers
  • Q2. Comparison of transfer learning and fintuning.
  • Ans. 

    Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.

    • Transfer learning uses knowledge gained from one task to improve learning on a different task.

    • Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

    • Transfer learning is faster and requires less data compared to training a...

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
No response

I appeared for an interview in Jan 2025.

Round 1 - One-on-one 

(1 Question)

  • Q1. What is underfitting and overfitting? 2. what is the attention mechanism? 3. What is the difference between single-head and multi-head attention?
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. Supervised learning algorithms
  • Ans. 

    Supervised learning algorithms are used in machine learning to predict outcomes based on labeled training data.

    • Supervised learning algorithms require labeled training data to learn the relationship between input and output variables.

    • Common supervised learning algorithms include linear regression, logistic regression, decision trees, support vector machines, and neural networks.

    • These algorithms are used for tasks such a...

  • Answered by AI
  • Q2. Unsupervised learning algorithms
  • Ans. 

    Unsupervised learning algorithms are used to find patterns in data without labeled outcomes.

    • Unsupervised learning algorithms do not require labeled data for training.

    • They are used for clustering, dimensionality reduction, and anomaly detection.

    • Examples include K-means clustering, hierarchical clustering, and principal component analysis.

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
-
Round 1 - One-on-one 

(4 Questions)

  • Q1. Cosine similarity
  • Ans. 

    Cosine similarity measures the similarity between two non-zero vectors in an inner product space.

    • Cosine similarity ranges from -1 to 1, with 1 indicating identical vectors and -1 indicating opposite vectors.

    • It is commonly used in information retrieval, text mining, and recommendation systems.

    • Formula: cos(theta) = (A . B) / (||A|| * ||B||)

    • Example: Calculating similarity between two documents based on their word frequenc...

  • Answered by AI
  • Q2. What is difference between recall and precission
  • Ans. 

    Recall is the ratio of correctly predicted positive observations to the all observations in actual class, while precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

    • Recall is about the actual positive instances that were correctly identified by the model.

    • Precision is about the predicted positive instances and how many of them were actually positive.

    • Recall = Tr...

  • Answered by AI
  • Q3. How to remove stop words and how it works
  • Ans. 

    Stop words are common words like 'the', 'is', 'and' that are removed from text data to improve analysis.

    • Stop words are commonly removed from text data to improve the accuracy of natural language processing tasks.

    • They are typically removed before tokenization and can be done using libraries like NLTK or spaCy.

    • Examples of stop words include 'the', 'is', 'and', 'in', 'on', etc.

  • Answered by AI
  • Q4. Whats the goal of project
Round 2 - One-on-one 

(1 Question)

  • Q1. Pipeline design

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(2 Questions)

  • Q1. What is confusion matrix?
  • Ans. 

    Confusion matrix is a table used to evaluate the performance of a classification model.

    • It is a 2x2 matrix that shows the counts of true positive, true negative, false positive, and false negative predictions.

    • It is used to calculate metrics like accuracy, precision, recall, and F1 score.

    • Example: TP=100, TN=50, FP=10, FN=5.

  • Answered by AI
  • Q2. Explain similarity matrix algo?
  • Ans. 

    Similarity matrix algo is a method to quantify the similarity between data points in a dataset.

    • It calculates the similarity between each pair of data points in a dataset and represents it in a matrix form.

    • Common similarity measures used include cosine similarity, Euclidean distance, and Jaccard similarity.

    • The diagonal of the matrix usually contains 1s as each data point is perfectly similar to itself.

    • The values in the ...

  • Answered by AI
Round 2 - HR 

(2 Questions)

  • Q1. Why should we select you?
  • Ans. 

    I have a strong background in data analysis, machine learning, and problem-solving skills.

    • Extensive experience in data analysis and machine learning algorithms

    • Proven track record of solving complex problems using data-driven approaches

    • Strong communication skills to effectively convey insights and recommendations

    • Ability to work collaboratively in a team environment

    • Passion for continuous learning and staying updated with...

  • Answered by AI
  • Q2. What are your expectations from the org?
  • Ans. 

    I expect a collaborative environment, opportunities for growth, and a focus on impactful projects that leverage data for decision-making.

    • A collaborative culture where team members share knowledge and support each other, like regular brainstorming sessions.

    • Opportunities for professional development, such as workshops or courses in advanced data science techniques.

    • Engagement in meaningful projects that have a real-world ...

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. Explain ur projects in Detail
  • Ans. 

    Developed a predictive model for customer churn in a telecom company.

    • Used machine learning algorithms like logistic regression and random forest.

    • Performed feature engineering to extract relevant customer behavior patterns.

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

  • Answered by AI
  • Q2. Steps involved in Machine Learning Problem Statement
  • Ans. 

    Steps involved in Machine Learning Problem Statement

    • Define the problem statement and goals

    • Collect and preprocess data

    • Select a machine learning model

    • Train the model on the data

    • Evaluate the model's performance

    • Fine-tune the model if necessary

    • Deploy the model for predictions

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
  • Q1. Depends upon your past experience & JD
  • Q2. Depends upon JD

Interview Preparation Tips

Interview preparation tips for other job seekers - NA
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Asked from resume about RAG

Interview Preparation Tips

Interview preparation tips for other job seekers - Asked everything from my resume
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
No response

I applied via Naukri.com and was interviewed in Jan 2024. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. Retraining GEN AI model
  • Ans. 

    Retraining GEN AI model involves updating the model with new data to improve its accuracy and performance.

    • Retraining is necessary to keep the model up-to-date with new information.

    • New data is used to fine-tune the model's parameters and improve its predictions.

    • Retraining may involve adjusting hyperparameters, adding more layers, or changing the architecture.

    • Examples: retraining a language model with new text data, retr...

  • Answered by AI
  • Q2. DEployment of Model in MLFlow
  • Ans. 

    MLFlow allows for easy deployment of machine learning models.

    • MLFlow provides a simple way to deploy models using the mlflow models serve command.

    • Models can be deployed locally or to a cloud-based server for production use.

    • MLFlow also supports model versioning and tracking for easy management of deployed models.

  • Answered by AI

Skills evaluated in this interview

Data Scientist Interview Questions & Answers

user image Sravani Kota

posted on 21 Jan 2025

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

Coding test on python to test skills

Round 2 - Case Study 

Case study on statistics

TCS Interview FAQs

How many rounds are there in TCS Data Scientist interview?
TCS interview process usually has 1-2 rounds. The most common rounds in the TCS interview process are Technical, One-on-one Round and Aptitude Test.
How to prepare for TCS Data Scientist interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at TCS. The most common topics and skills that interviewers at TCS expect are Data Science, Python, Machine Learning, SQL and R.
What are the top questions asked in TCS Data Scientist interview?

Some of the top questions asked at the TCS Data Scientist interview -

  1. How does decision tree algorithm work, what is cross entrop...read more
  2. If minimal data, which would you train for categorical prediction mod...read more
  3. How RNN handles exploding/vanishing Gradie...read more
How long is the TCS Data Scientist interview process?

The duration of TCS Data Scientist interview process can vary, but typically it takes about less than 2 weeks to complete.

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Overall Interview Experience Rating

4.2/5

based on 36 interview experiences

Difficulty level

Easy 38%
Moderate 63%

Duration

Less than 2 weeks 82%
2-4 weeks 12%
4-6 weeks 6%
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TCS Data Scientist Salary
based on 2.3k salaries
₹6.9 L/yr - ₹19 L/yr
21% less than the average Data Scientist Salary in India
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2.6

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3.7

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2.5

Promotions

3.3

Work satisfaction

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