KCS GROUP Data Scientist Interview Questions and Answers

Q1. What is LSTM and how is different with RNN
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Q2. Tell me the NLP technique you used in past and explain
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Q1. Tell me about Yourself.
Ans. 
I am a data scientist with a background in statistics and machine learning.
• Background in statistics and machine learning

• Experience with data analysis and visualization tools like Python, R, and Tableau

• Strong problem-solving skills

• Ability to communicate complex technical concepts to non-technical stakeholders
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Q2. Explain your Final Year Project
Ans. 
Developed a predictive model for customer churn in a telecom company using machine learning algorithms.
• Used Python for data preprocessing and model building

• Implemented algorithms like Random Forest and Logistic Regression

• Evaluated model performance using metrics like accuracy and AUC-ROC curve
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Q1. Tell me about yourself
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Q2. Questions from projects which was mentioned on resume
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Q1. Fitment based questions
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Q2. Explain a project
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Collected and cleaned customer data including usage patterns and demographics

• Used classification algorithms like Random Forest and Logistic Regression to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall
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Math, English, reasoning

Q1. Asked me about expected salary
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Q1. What is difference between lstm and rnn
Ans. 
LSTM is a type of RNN that addresses the vanishing gradient problem by using memory cells.
• RNN stands for Recurrent Neural Network, a type of neural network that processes sequential data.

• LSTM stands for Long Short-Term Memory, a type of RNN that includes memory cells to retain information over long sequences.

• LSTM is designed to overcome the vanishing gradient problem, which occurs when training RNNs on long sequences.

• L...read more
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Q1. What are different evaluation matrix ?
Ans. 
Evaluation matrices are used to assess the performance of models in data science.
• Confusion matrix: used to evaluate the performance of classification models.

• Precision, recall, and F1 score: measures for binary classification models.

• Mean squared error (MSE): evaluates the performance of regression models.

• R-squared: assesses the goodness of fit for regression models.

• Area under the ROC curve (AUC-ROC): evaluates the perfo...read more
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Data Science MCQ questions

Building a baseline ML model with EDA etc.

Q1. How do you analyse outliers?
Ans. 
Outliers can be analyzed using statistical methods like Z-score, IQR, or visualization techniques like box plots.
• Calculate Z-score and identify data points with Z-score greater than a certain threshold as outliers.

• Use Interquartile Range (IQR) to detect outliers by identifying data points outside 1.5 * IQR range.

• Visualize data using box plots to identify any data points that fall outside the whiskers.

• Consider domain kn...read more
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Q2. Tell me about a time when what you found did not match expectation with what you thought analysing the dataset.
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Q1. Why Unilever now?
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Q2. Where do you see yourself in 5 years?
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Q1. Pandas basics and SQL joins nlp
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Q1. DFA Focus :Sorting ,Searching ,Stacks,Queues, HashMaps
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Q2. Os & cn: Process scheduling, TCP/IP, HTTP basics
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Q1. Regularization ,when to select l1 and l2
Ans. 
L1 regularization is used for feature selection and L2 regularization is used for preventing overfitting.
• Use L1 regularization when you want to perform feature selection as it tends to produce sparse feature vectors.

• Use L2 regularization when you want to prevent overfitting by penalizing large weights.

• A combination of L1 and L2 regularization (Elastic Net) can be used for a balance between feature selection and prevent
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