Apply and Predict: Time Series Forecasting in Excel

Overview

By the end of this course, learners will be able to apply forecasting concepts, analyze real-world datasets, detect seasonal trends, construct regression models, and predict future outcomes with Microsoft Excel. They will practice using weighted and exponential averages to capture data trends, explore correlations and regression analysis for deeper insights, and forecast scenarios such as climate projections and workforce attrition. Unlike generic Excel training, this course uniquely blends statistical forecasting techniques with practical applications across two high-impact domains: climate analysis and human resources (HR) analytics. Learners will work hands-on with authentic datasets, from climate emission scenarios to employee attrition records, ensuring they gain transferable, industry-relevant skills. Completing this course empowers participants to confidently visualize, interpret, and forecast time series data, enabling stronger decision-making for academic, professional, and research purposes. With Excel as the primary tool, learners gain accessible yet powerful forecasting expertise—no programming or advanced statistical software required.

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.