This module focuses on advanced techniques such as weighted averages and exponential averages for forecasting climate data. Learners will gain hands-on experience in handling multi-scenario datasets, applying statistical methods, and interpreting temperature projections with improved accuracy.

This module advances into correlation studies and regression models for predictive analytics. Learners will understand how minimum and maximum temperatures are interrelated across scenarios and how to use simple and multiple regression techniques to predict climate outcomes.

This module introduces the fundamentals of time series analysis using Microsoft Excel, focusing on employee attrition data. Learners will prepare datasets, apply essential and advanced Excel formulas, and calculate overall and quarterly attrition. The module emphasizes building strong analytical skills and preparing accurate datasets for deeper forecasting tasks.

This module advances into trend visualization, seasonality recognition, and forecasting techniques for HR attrition. Learners will use moving averages and trend lines to uncover hidden patterns, analyze recurring seasonal effects, and build Excel-based forecasting models. Additionally, they will evaluate attrition at department and organizational levels to generate actionable HR insights.