MedTourEasy Data Analyst Intern Interview Questions and Answers

The assignment involved analyzing sample data from Inshorts to extract valuable insights.

Q1. Can you elaborate on the projects that you have worked on?
Ans. 
I have worked on projects involving predictive modeling, natural language processing, and machine learning algorithms.
• Developed a predictive model to forecast customer churn for a telecommunications company

• Implemented sentiment analysis using natural language processing techniques on social media data

• Utilized machine learning algorithms to classify fraudulent transactions for a financial institution
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Q2. What were the key discussion points regarding the assignment?
Ans. 
Key discussion points regarding the assignment
• The methodology used to analyze the data

• The key findings and insights derived from the analysis

• Any challenges faced during the assignment and how they were overcome

• Recommendations for future improvements or further analysis
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Q1. Statistics, ML models, Assignment, Case Study
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The assessment consists of a dataset for which we are required to build a machine learning model and submit the results along with code and detailed documentation

Q1. What are ensemble models
Ans. 
Ensemble models are machine learning models that combine multiple individual models to improve predictive performance.
• Ensemble models work by aggregating predictions from multiple models to make a final prediction.

• Common types of ensemble models include Random Forest, Gradient Boosting, and AdaBoost.

• Ensemble models are often more accurate and robust than individual models.

• They can reduce overfitting and increase genera...read more
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Q2. Difference between bagging and boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q3. A lot of tree based questions
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Q1. About your job how much do you expect as a compensation
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Q1. Sql and power bi joins , filters and slicer
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Formulating SQL queries based on the data presented by the interviewer during a screen-sharing session.

Q1. Tell me about yourself
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Q2. Questions based on past experience
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Q1. 1st round was more of Mathematical foundation screening before selecting for 2nd round
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Assignment was similar to work you gonna do as a Data Scientist at Inshorts.

Q1. Discussion on assignment
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Q1. Data Science related
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Q1. Advanced data science related questions
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Q1. HR team on basic HR questions
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Q1. With Co-founder to introduce and check you
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