Best ChatGPT prompts for Operations Research Analysts

Given a Snowflake database with 100 million rows of customer data, and an ETL pipeline written in Python that takes 10 hours to run, identify the top 3 bottlenecks in the pipeline and provide a rewritten version of the pipeline that reduces the runtime by at least 50%, using techniques such as parallel processing, data partitioning, and query optimization, and including a detailed comparison of the original and optimized pipelines using metrics such as data throughput, memory usage, and CPU utilization.

Using a dataset of 10,000 samples with 20 features, and a target variable that is a mix of categorical and numerical values, develop a Python script that uses cross-validation to compare the performance of 3 different regression models (linear, decision tree, and random forest), and then uses a grid search to tune the hyperparameters of the best-performing model, including the number of trees, maximum depth, and learning rate, and provide a detailed analysis of the results, including plots of the model's performance on the training and testing sets, and a discussion of the implications of the results for the business problem at hand.

Given a dataset of 1 million rows with 50 columns, and a set of data quality rules that include checks for missing values, outliers, and data type inconsistencies, develop a data cleaning script in R that applies these rules to the dataset, and then provides a detailed report on the number and percentage of rows that failed each rule, including a summary of the distribution of values for each column, and a set of recommendations for how to address the data quality issues, including data imputation, data transformation, and data validation.

Using a Tableau dashboard that connects to a SQL database with 10 million rows of sales data, and a set of queries that are currently taking 30 seconds to execute, develop a rewritten version of the queries that uses techniques such as indexing, caching, and query rewriting to reduce the execution time by at least 75%, and provide a detailed analysis of the results, including a comparison of the original and optimized queries using metrics such as query execution time, data transfer size, and CPU utilization, and a discussion of the implications of the results for the dashboard's performance and user experience.