Best Gemini prompts for Management 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 2 hours to run, and I need to optimize it to reduce the runtime to under 30 minutes. Provide a step-by-step analysis of the current pipeline, including recommendations for optimizing the SQL queries, improving the Pandas transformation efficiency, and reducing the data load time into Tableau. Assume that the data volume is 100GB and the pipeline runs daily.

I've built a regression model using Python and Scikit-learn to predict customer churn based on demographic and behavioral data. The model is currently achieving an R-squared value of 0.6, but I need to improve it to 0.8. Provide a detailed evaluation of the current model, including recommendations for feature engineering, hyperparameter tuning, and model selection. Assume that the dataset contains 100,000 rows and 20 features, and that the target variable is binary.

I've been tasked with conducting a data quality and accuracy audit on a dataset containing customer information, including names, addresses, and phone numbers. The dataset contains 500,000 rows and is stored in a Snowflake database. Provide a step-by-step guide on how to perform the audit, including recommendations for data profiling, data validation, and data cleansing. Assume that the data is collected from multiple sources, including web forms, CRM systems, and external data providers.

I have a dataset containing sales data for a retail company, including information on product categories, sales channels, and customer demographics. The dataset contains 1 million rows and is stored in a Tableau dashboard. Provide a statistical summary of the data, including recommendations for identifying trends, patterns, and correlations. Also, generate insights on how to improve sales performance, including suggestions for pricing strategies, product bundling, and marketing campaigns. Assume that the data is collected from multiple sources, including POS systems, e-commerce platforms, and customer surveys.