Motilal Oswal Financial Services Data Scientist Interview Questions and Answers

Assignment on credit risk

Q1. Hyperparameter tuning
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Q1. Case study for problem solving
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I was asked Python, sql, coding questions

Case study on how would you identify the total number of footfall on a airport

Q1. Explain any Data Science project
Ans. 
Developed a predictive model to forecast customer churn for a telecommunications company.
• Identified key features such as customer tenure, monthly charges, and service usage

• Collected and cleaned data from customer databases

• Built a machine learning model using logistic regression or random forest algorithms

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

• Provided actionable insights to reduce
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Q2. Types of Error in Statistics
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Types of errors in statistics include sampling error, measurement error, and non-sampling error.
• Sampling error occurs when the sample does not represent the population accurately.

• Measurement error is caused by inaccuracies in data collection or measurement instruments.

• Non-sampling error includes errors in data processing, analysis, and interpretation.

• Examples: Sampling error - selecting a biased sample, Measurement err...read more
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Q3. Types of Machine learning models
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Types of machine learning models include supervised learning, unsupervised learning, and reinforcement learning.
• Supervised learning: Models learn from labeled data, making predictions based on past examples (e.g. linear regression, support vector machines)

• Unsupervised learning: Models find patterns in unlabeled data, clustering similar data points together (e.g. k-means clustering, PCA)

• Reinforcement learning: Models le...read more
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Q4. Functions of pandas library, such as get_dummies()
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get_dummies() function in pandas library is used to convert categorical variables into dummy/indicator variables.
• get_dummies() function creates dummy variables for categorical columns in a DataFrame.

• It converts categorical variables into numerical representation for machine learning models.

• Example: df = pd.get_dummies(df, columns=['column_name'])
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Q1. What is cross validation ?
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Cross validation is a technique used to assess the performance of a predictive model by splitting the data into training and testing sets multiple times.
• Cross validation helps to evaluate how well a model generalizes to new data.

• It involves splitting the data into k subsets, training the model on k-1 subsets, and testing it on the remaining subset.

• Common types of cross validation include k-fold cross validation and lea...read more
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Python coding question and ML question

Q1. ML questions from resume + general
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Q1. Techno managerial round
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Q1. Work done in previous companty
Ans. 
Developed machine learning models to predict customer churn and optimize marketing campaigns.
• Built predictive models using Python and scikit-learn

• Utilized SQL to extract and manipulate data for analysis

• Collaborated with cross-functional teams to implement data-driven solutions
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