Crisil Data Science Intern Interview Questions and Answers

Q1. Can you elaborate on your work experience?
Ans. 
I have 5 years of experience in analyzing large datasets to extract valuable insights and make data-driven decisions.
• Analyzed customer behavior data to optimize marketing strategies

• Built predictive models to forecast sales trends

• Utilized machine learning algorithms to improve product recommendations

• Presented findings to stakeholders in a clear and actionable manner
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Q2. What questions were asked regarding your work experience?
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Questions related to work experience in data science field.
• Asked about previous projects worked on

• Inquired about specific data analysis techniques used

• Discussed challenges faced and how they were overcome
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Q1. Explain any ML model.
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Q2. Create Dataframe from two lists.
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Q1. Explain the RAG pipeline?
Ans. 
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
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Q2. Explain Confusion metrics
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Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
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Questions based on ML,PYTHON, DATA VISUALIZATION

Q1. What is TF-IDF IN NLP
Ans. 
TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.
• TF-IDF stands for Term Frequency-Inverse Document Frequency

• It is used in Natural Language Processing (NLP) to determine the importance of a word in a document

• TF-IDF is calculated by multiplying the term frequency (TF) by the inverse document frequency (IDF)

• It helps in identifying the most important
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Q2. Python coding questions based on list
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SQL, Python coding …

Basic mathematical and resoning questions.

Q1. Tell me about your self
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Q2. Explain the recent 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

• Evaluated model performance using metrics like accuracy and AUC-ROC curve
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Q3. What is the difference between random forest and decision tree
Ans. 
Random forest is an ensemble learning method that uses multiple decision trees to make predictions, while a decision tree is a single tree-like structure that makes decisions based on features.
• Random forest is a collection of decision trees that work together to make predictions.

• Decision tree is a single tree-like structure that makes decisions based on features.

• Random forest reduces overfitting by averaging the predic...read more
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Q4. What is cost function
Ans. 
A cost function is a mathematical formula used to measure the cost of a particular decision or set of decisions.
• Cost function helps in evaluating the performance of a model by measuring how well it is able to predict the outcomes.

• It is used in optimization problems to find the best solution that minimizes the cost.

• Examples include mean squared error in linear regression and cross-entropy loss in logistic regression.
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I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.

Q1. Explain the Project undertaken during the research and follow-up questions
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Q1. Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve t...read more
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Answering questions related to data science concepts and techniques.
• Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...read more
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