Best Grok prompts for Management Analysts

Given a dataset of customer transactions with fields for date, customer ID, purchase amount, and product category, write a Python script using the Isolation Forest algorithm to identify anomalous purchases that may indicate fraudulent activity, and then use SQL to insert the results into a Snowflake database for further analysis. Assume the data is stored in a CSV file named 'customer_transactions.csv' and the database connection details are stored in a separate file named 'db_credentials.json'. Use the Tableau API to generate a visualization of the anomalous transactions.

Using a dataset of employee demographic and performance data with fields for age, tenure, job satisfaction, and turnover status, develop a logistic regression model in R to predict the likelihood of employee turnover, and then use the model to generate predictions for a new dataset of employees. Assume the data is stored in a data frame named 'employee_data' and the new dataset is stored in a data frame named 'new_employees'. Use the 'dplyr' library to preprocess the data and the 'caret' library to tune the model hyperparameters. Write the results to a CSV file named 'turnover_predictions.csv'.

Given an ETL pipeline that extracts data from a PostgreSQL database, transforms the data using Python, and loads the data into a Snowflake database, write a data quality assessment script that checks for missing values, data type inconsistencies, and data duplication, and then generates a report detailing the results. Assume the ETL pipeline is defined in a Python script named 'etl_pipeline.py' and the database connection details are stored in a separate file named 'db_credentials.json'. Use the 'pandas' library to read and manipulate the data and the 'matplotlib' library to generate visualizations of the results.

Using a dataset of sales data with fields for date, sales amount, and product category, develop a time series model in Python using the ARIMA algorithm to forecast future sales, and then use the model to generate predictions for the next quarter. Assume the data is stored in a CSV file named 'sales_data.csv' and the model hyperparameters are tuned using the 'pmdarima' library. Write the results to a CSV file named 'sales_forecast.csv'. Use the Tableau API to generate a visualization of the forecasted sales.