Nucleus Software Exports Data Scientist Interview Questions and Answers

Machine Learning is a subset of Artificial Intelligence that uses algorithms to learn from data and make predictions.

• AI (Artificial Intelligence) is the broader concept of machines being able to carry out tasks in a way that we would consider 'smart'.

• ML (Machine Learning) is a subset of AI that focuses on the development of computer programs that can access data and use it to learn for themselves.

• DL (Deep Learning) is ...read more

Evaluation metrics are used to assess the performance of classification models.

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

• Accuracy measures the overall correctness of the model's predictions.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positives.

• F1 sco...read more

Window functions in SQL allow you to perform calculations across a set of table rows related to the current row.

• Window functions are used to perform calculations on a set of rows related to the current row.

• They do not reduce the number of rows returned by the query.

• Common window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE().

Indexes are used to improve the performance of SQL queries by allowing faster data retrieval. Procedures are reusable SQL code blocks.

• Indexes are used to quickly locate data without having to scan the entire table.

• Procedures are stored SQL code blocks that can be executed multiple times.

• Indexes can be created on columns to speed up search queries.

• Procedures can accept input parameters and return results.

Precision and Recall are evaluation metrics for classification models. Feature selection is important for model performance. P-value is a statistical measure.

• Precision is the ratio of true positives to the total predicted positives. Recall is the ratio of true positives to the total actual positives.

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• Feature...read more

Machine Learning is a subset of Artificial Intelligence that uses algorithms to learn from data and make predictions.

• AI (Artificial Intelligence) is the broader concept of machines being able to carry out tasks in a way that we would consider 'smart'.

• ML (Machine Learning) is a subset of AI that focuses on the development of computer programs that can access data and use it to learn for themselves.

• DL (Deep Learning) is ...read more

Evaluation metrics are used to assess the performance of classification models.

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

• Accuracy measures the overall correctness of the model's predictions.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positives.

• F1 sco...read more

Window functions in SQL allow you to perform calculations across a set of table rows related to the current row.

• Window functions are used to perform calculations on a set of rows related to the current row.

• They do not reduce the number of rows returned by the query.

• Common window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE().

Indexes are used to improve the performance of SQL queries by allowing faster data retrieval. Procedures are reusable SQL code blocks.

• Indexes are used to quickly locate data without having to scan the entire table.

• Procedures are stored SQL code blocks that can be executed multiple times.

• Indexes can be created on columns to speed up search queries.

• Procedures can accept input parameters and return results.

Precision and Recall are evaluation metrics for classification models. Feature selection is important for model performance. P-value is a statistical measure.

• Precision is the ratio of true positives to the total predicted positives. Recall is the ratio of true positives to the total actual positives.

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• Feature...read more