analyze data use visualizations and data science skills for data modelling

The lifetime value (LTV) of users are accounted by the revenue they generate over their lifetime after they have installed. The data you has the revenue events for each user up until the first 14 days after each user has installed the app. However, users continue to generate revenue until their lifetime is reached. In the dataset, the revenue events from the 14th day until the lifetime after the install for each user are removed on purpose. Form a regression function using ARPI values to estimate the average lifetime value (LTV). Skills and Experience Needed: - Proficiency in data analysis, - Visualization - Recommendation Writing - Regression Analysis - Data Modeling - Time Series Analysis - Google Sheet Skills - Writing SQL queries to combine datasets and generate a summary table. - Calculating metrics such as ARPI_D1, ARPI_D14, ROAS_D14, and CPI. Preferred Tools and Software: - Python - Jupyter - Google sheet