Poonawalla Fincorp Data Scientist Interview Questions and Answers

I am very familiar with Python, SQL, and ML models.

• I have extensive experience using Python for data analysis and machine learning tasks.

• I am proficient in writing SQL queries to extract data from databases.

• I have worked with a variety of ML models, including regression, classification, and clustering.

• I am familiar with popular ML libraries such as scikit-learn, TensorFlow, and Keras.

• I have experience with data preproc...read more

ML classification models use various metrics to evaluate performance.

• Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC.

• Accuracy measures the proportion of correct predictions.

• Precision measures the proportion of true positives among all positive predictions.

• Recall measures the proportion of true positives among all actual positives.

• F1 score is the harmonic mean of precision and recall.

• AUC-ROC me...read more

Outliers can significantly impact analysis. It's important to identify and handle them appropriately.

• Visualize the data to identify outliers

• Consider the source of the outliers and whether they are valid data points

• Remove outliers if they are invalid or use robust statistical methods that are less sensitive to outliers

• Document any handling of outliers in the analysis report

The organization used descriptive and inferential statistics to analyze and build models.

• Descriptive statistics were used to summarize and describe the data, such as mean, median, and standard deviation.

• Inferential statistics were used to make predictions and draw conclusions about the population based on the sample data, such as hypothesis testing and regression analysis.

• The organization may have also used time series...read more

I am very familiar with Python, SQL, and ML models.

• I have extensive experience using Python for data analysis and machine learning tasks.

• I am proficient in writing SQL queries to extract data from databases.

• I have worked with a variety of ML models, including regression, classification, and clustering.

• I am familiar with popular ML libraries such as scikit-learn, TensorFlow, and Keras.

• I have experience with data preproc...read more

ML classification models use various metrics to evaluate performance.

• Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC.

• Accuracy measures the proportion of correct predictions.

• Precision measures the proportion of true positives among all positive predictions.

• Recall measures the proportion of true positives among all actual positives.

• F1 score is the harmonic mean of precision and recall.

• AUC-ROC me...read more

Outliers can significantly impact analysis. It's important to identify and handle them appropriately.

• Visualize the data to identify outliers

• Consider the source of the outliers and whether they are valid data points

• Remove outliers if they are invalid or use robust statistical methods that are less sensitive to outliers

• Document any handling of outliers in the analysis report

The organization used descriptive and inferential statistics to analyze and build models.

• Descriptive statistics were used to summarize and describe the data, such as mean, median, and standard deviation.

• Inferential statistics were used to make predictions and draw conclusions about the population based on the sample data, such as hypothesis testing and regression analysis.

• The organization may have also used time series...read more