Best Jasper prompts for Operations Research Analysts

Given a Snowflake database with 10 tables, each containing 1 million rows, and an ETL pipeline written in Python that takes 2 hours to run, using the pandas library and the Snowflake Connector, write a modified version of the pipeline that utilizes parallel processing and query optimization techniques to reduce the runtime to under 30 minutes, and provide a step-by-step explanation of the changes made, including any necessary modifications to the SQL queries and the Python code.

Using a dataset of 100,000 samples, each with 10 features, and a target variable that is a continuous outcome, write a Python script that uses the scikit-learn library to train and evaluate the performance of 5 different regression models, including linear regression, decision tree regression, random forest regression, support vector regression, and gradient boosting regression, and uses a grid search approach to tune the hyperparameters of each model, providing a comparison of the models' performance metrics, including mean squared error, mean absolute error, and R-squared, and recommend the best model based on the results.

Given a dataset of 1 million rows, with 20 columns, and a set of data quality rules defined in a separate CSV file, write a SQL script that uses the Tableau data validation toolkit to check the data against the rules, and generates a report that highlights any data quality issues, including missing values, inconsistent formatting, and invalid data, and provides recommendations for data cleaning and normalization, including the use of data profiling and data visualization techniques to identify patterns and trends in the data.

Using a dataset of 500,000 samples, each with 15 features, and a set of business questions defined in a separate text file, write a Python script that uses the statsmodels library to generate a statistical summary of the data, including means, medians, modes, and standard deviations, and uses a combination of data visualization and machine learning techniques to generate insights and recommendations based on the data, including the identification of trends, patterns, and correlations, and the creation of predictive models to forecast future outcomes, providing a written report that summarizes the key findings and implications for business decision-making.