Overview

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Build practical time series forecasting skills in Microsoft Excel through real-world climate data. In Master Time Series Forecasting with Excel, you’ll begin with core forecasting concepts and learn to interpret temperature projections under low, medium, and high emission scenarios across the 21st century. Using Excel visualizations and charts, you’ll analyze trends and evaluate how projected outcomes change across scenarios. You’ll then apply weighted averages to minimum and maximum temperatures, compare results across emission scenarios, and use exponential averages to give greater emphasis to recent changes. The course also guides you through correlation analysis and simple and multiple regression models, helping you examine relationships between temperature variables and predict climate outcomes. Designed for learners seeking practical forecasting skills for academic, professional, or research applications, this course combines statistical methods, Excel tools, and multi-scenario climate datasets. Its focused use of climate projections sets it apart from a general Excel course. By the end, you’ll be able to visualize and interpret complex data, apply time series analysis techniques, construct regression models, and forecast future temperature outcomes with greater confidence.

Syllabus

Foundations of Forecasting in Excel
- This module introduces learners to the fundamentals of time series analysis and climate forecasting using Microsoft Excel. It explores low, medium, and high emission scenarios of the 21st century, helping learners build a strong foundation in interpreting climate projections through Excel tools.
Advanced Temperature Forecasting Techniques
- 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.
Correlations & Regression Models
- 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.
Foundations of Time Series in Excel
- 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.
Trend Analysis, Seasonality & Forecasting
- 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.