Best Gemini prompts for Operations Research Analysts

I have an ETL pipeline built using Python and SQL that extracts data from a Snowflake database, transforms it using Pandas, and loads it into a Tableau dashboard. The pipeline is currently taking around 2 hours to run, but I need to reduce the runtime to under 30 minutes. Analyze the pipeline's current architecture and provide a step-by-step plan to optimize its performance, including any necessary code refactoring, data partitioning, or parallel processing techniques. Assume the pipeline is processing around 10 million rows of data and the current hardware configuration is a 16-core CPU with 64GB of RAM.

I've built a regression model using Python's scikit-learn library to predict customer churn based on a set of demographic and behavioral features. The model is producing an R-squared value of 0.8, but I'm having trouble interpreting the coefficients and understanding which features are driving the predictions. Provide a detailed explanation of the model's results, including the significance of each feature, the direction of the relationships, and any potential issues with multicollinearity or heteroscedasticity. Also, suggest ways to refine the model and improve its precision, such as feature engineering or hyperparameter tuning.

I'm working with a large dataset that contains a mix of structured and unstructured data, including customer feedback, transactional records, and social media posts. The data is currently stored in a Snowflake database, but I'm concerned about the accuracy and consistency of the data. Design a data quality control process that includes data profiling, data validation, and data cleansing steps. The process should be able to handle a large volume of data and provide real-time feedback on data quality issues. Also, suggest ways to implement data governance policies and ensure that the data is properly documented and metadata is maintained.

I need to create a statistical summary report that provides an overview of customer behavior and preferences based on a large dataset of transactional records and survey responses. The report should include summary statistics such as means, medians, and standard deviations, as well as data visualizations such as histograms and scatter plots. The report should also include a section on data quality and any limitations or biases in the data. Provide a template for the report and suggest ways to use Python's Pandas and Matplotlib libraries to generate the summary statistics and data visualizations. Also, suggest ways to refine the report and make it more engaging and insightful for stakeholders.