Part 2: Planning Ahead

This part focuses on preparing for the future and identifying irregularities, starting with an in-depth look at forecasting and time series modeling, a crucial and complex aspect of a marketing data analyst’s role. It guides you in choosing the right models, knowing when to use them, and avoiding common mistakes. Following this, the discussion turns to anomaly detection, highlighting its connection to forecasting. It underscores the analyst’s frequent task of spotting anomalies before they affect the business and outlines how to manage low-frequency data while reducing false positives.

This part contains the following chapters:

• Chapter 5, Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast
• Chapter 6, Anomaly Detection with StatsForecast and PyMC