How to Forecast Anything
How to Forecast Anything
Mastercourse with Excel and SQL
Who has registered for this course:
Controllers | Accounting and Financial Professionals | Sales Managers
Consultants | AP/AR Data Analyst | Bookkeepers | Tax Pros
20+ Hours of Short, One-topic, On-demand, Pre-recorded videos
5-10 minute videos allowing you to fit this course into your lifestyle.
Self-Paced with No Time Limit, No Assigments and ‘Forever’ Course Access
CPE Credit
Real Transaction Data | Downloadable Excel Templates | Certificate of Completion
You get Lifetime access to the course.
Allowing you to come back to review the material at any time.
Plus Unlimited One-on-One sessions with Instructor.
What You Will Learn
Data Extraction | Data Cleansing | Data Dimensioning
Averaging Models | Statistical Models | Probabilistic Models
The Best Predictive Techniques
Statistical Concepts for Forecasting
Excel Forecasting functions
Simple Moving Average
Weighted Moving Average
Exponential and Double Smoothing
Exponential Smoothing with Trend Adjustments
Seasonality Smoothing
Excel Array functions
Bayesian Forecasting
Measuring Forecast Accuracy
Improving Forecast Accuracy
Regression Techniques
Applied to Real Business Data
Forecasting Year-over-Year Growth
Forecasting Techniques for Contract Sales
Forecast Customer Pickup and Attrition
Forecast Sales Funnels Performance
Forecast Wholesale Sales
Dynamic Forecasting for Individual Sales
Forecast Bundled Sales
Forecast Variable Costs
Forecast the 21 Dimensions of Revenue
Building a P&L Forecasting Model
Build Rolling Forecasts
Connecting to Transaction Systems for Streaming Forecasts
All the skills needed to be a Forcasting Expert
- Data Extraction and Transformation: Data extraction and transformation are vital steps in the forecasting process. Accurate and relevant data is essential for building reliable forecasting models.
- You will learn to use SQL and Excel to extract data from various sources, clean and preprocess it, and transform it into a suitable format for forecasting analysis.
- Statistical Concepts for Forecasting: Statistical concepts serve as the foundation for forecasting. This module covers key concepts such as probability, probability distributions, correlation, regression analysis, and time series analysis.
- You will learn to analyze data patterns, identify relationships, and make informed forecasts.
- Excel Forecasting Functions: Excel is a widely used tool for data analysis and forecasting. This module focuses on the specific forecasting functions available in Excel, such as FORECAST, TREND, GROWTH, and LINEST.
- You will learn how to leverage these functions to generate forecasts, analyze trends, and evaluate the performance of forecasting models. Proficiency in Excel forecasting functions enhances efficiency and productivity in forecasting tasks.
- Excel Array Functions: Array functions in Excel provide advanced capabilities for forecasting calculations. This module introduces students to the concept of array formulas and their application in forecasting.
- You will learn how to leverage Excel array functions to perform complex calculations on multiple data points simultaneously, leading to efficient and effective forecasting analysis.
- Simple Moving Average: The simple moving average is a fundamental forecasting technique that smoothes out variations in data. This module explores the concept of moving averages and its applications in forecasting.
- You will learn how to calculate and interpret moving averages, select appropriate window sizes, and utilize them for short-term forecasting. Mastering simple moving average techniques enables you to identify trends and make quick forecasts based on historical data.
- Weighted Moving Average: Weighted moving averages offer a more nuanced approach to forecasting by assigning different weights to past observations. This module delves into the calculation and interpretation of weighted moving averages.
- You will understand how to assign weights based on data significance and use this technique to capture changes in data patterns. Proficiency in weighted moving averages enhances forecasting accuracy, particularly when recent data points carry more importance.
- Exponential and Double Smoothing: Exponential smoothing techniques provide a flexible and adaptable approach to forecasting. This module explores single-exponential smoothing and double-exponential smoothing.
- You will learn how to apply exponential smoothing methods to capture trend and seasonality in data. Understanding these techniques equips you with the ability to handle time series data and generate forecasts that account for different components of variation.
- Exponential Smoothing with Trend: Exponential smoothing with trend extends the capabilities of exponential smoothing by incorporating trend analysis. This module focuses on techniques like Holt’s linear exponential smoothing to forecast data with both level and trend components.
- You will learn how to adjust smoothing parameters to capture trend changes effectively. Proficiency in exponential smoothing with trend allows for more accurate forecasting in situations where data exhibits a consistent trend over time.
- Seasonality Smoothing: Seasonality smoothing is crucial for forecasting data that exhibits recurring patterns at regular intervals. This module covers methods for seasonality decomposition and adjustment, such as seasonal indices and deseasonalization techniques.
- You will gain the ability to identify and handle seasonality in data, resulting in more accurate and reliable seasonal forecasts.
- Bayesian Forecasting: Bayesian forecasting offers a probabilistic approach that incorporates prior knowledge and observed data. This module explores Bayesian inference and its application in forecasting. Proficiency in Bayesian forecasting equips you with a valuable tool for handling uncertainty and incorporating expert opinions in forecasting analysis.
- You will understand how to update prior beliefs with data to generate posterior distributions and probabilistic forecasts.
- Regression Techniques: Regression analysis plays a crucial role in forecasting when relationships exist between the dependent variable and one or more independent variables. This module introduces students to simple linear regression, multiple linear regression, and non-linear regression models. Proficiency in regression techniques equips you with a powerful tool to forecast based on the influence of various factors on the dependent variable.
- You will learn how to identify relevant variables, estimate regression coefficients, interpret results, and use regression analysis to forecast outcomes.
- Measuring Forecast Accuracy: Measuring forecast accuracy is essential for evaluating the performance of forecasting models. This module focuses on commonly used metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE).
- You will learn how to calculate and interpret these metrics to assess the accuracy of their forecasts. Understanding forecast accuracy metrics enables students to objectively evaluate the performance of different forecasting techniques and make informed decisions.
- Improving Forecast Accuracy: Forecast accuracy improvement is a continuous process in forecasting. This module covers techniques and strategies for enhancing forecast accuracy. Mastery of techniques for improving forecast accuracy empowers you to generate more reliable and actionable forecasts.
- You will learn how to analyze forecast errors, identify patterns or biases, and diagnose the root causes of inaccuracies. You will explore methods for model selection, parameter tuning, incorporating external factors, and leveraging expert judgment to refine their forecasting models.
Dozens of Real-World Case Studies
- Forecasting Year-over-Year Growth: Forecasting year-over-year growth is crucial for businesses to anticipate and plan for future revenue performance. This case study focuses on understanding historical trends, identifying factors influencing growth, and developing forecasting models that project revenue growth for the upcoming periods.
- You will learn how to predict revenue growth accurately, enabling effective resource allocation and strategic decision-making.
- Dynamic Pricing and Forecasting Sales: This case study emphasizes dynamic forecasting techniques tailored to product sales, considering factors such as market demand, pricing strategies, and purchase patterns.
- You will learn how to develop flexible and responsive forecasting models, enabling businesses to maximize revenue potential, optimize pricing decisions, and efficiently manage inventory.
- Building a Forecasting Model: This case study focuses on the practical aspects of building a forecasting model specifically tailored for revenue and expense forecasting. Students will learn the step-by-step process of constructing a forecasting model, including data collection, preprocessing, feature selection, model selection, and model evaluation. Emphasis will be placed on incorporating relevant variables and industry-specific factors into the model.
- You will learn the skills to develop customized forecasting models that accurately predict revenue, enabling informed decision-making and effective profit management.
- Build Rolling Forecasts: Rolling forecasts allow analysts to continuously update and refine their forecasts based on the most recent data and market conditions. This case study focuses on the concept of rolling forecasts and the techniques used to build and maintain them.
- You will learn how to incorporate new data, adjust forecast models, and analyze forecast variances to ensure the accuracy and relevance of rolling forecasts. By mastering this case study, students will be equipped to develop agile forecasting processes that reflect the dynamic nature of most industries.
- Forecast Automation: Automating the forecasting process can significantly enhance efficiency and accuracy in forecasting. This exercise explores techniques and tools for automating the forecasting workflow, including data extraction, data transformation, model selection, and report generation.
- You will gain hands-on experience with forecasting models and learn how to leverage automation to streamline the forecasting process, reduce manual effort, and improve overall forecasting quality. Mastery of this exercise empowers students to build efficient and reliable automated forecasting tools for strategic planning
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Course material released November 29, 2024.
Pre-register now and save 40%.
No-nonsense, Immediate-Impact Training
Most data analysis courses focus an a specific software or platform, thereby limiting your ability to transfer your knowledge to other systems. Not this one.
THIS COURSE IS VENDOR AGNOSTIC. WE DO NOT PROMOTE OR LIMIT THE COURSE TO ANY SPECIFIC STATISTICS PACKAGE OR ANY OTHER SOFTWARE. WE ALSO DO NOT PROMOTE ANY THIRD PARTY COMPANIES OR CONSULTING SERVICES INCLUDING OWL. THIS COURSE IS NOT AFFILIATED WITH ANY UNIVERSITY, ASSOCIATION OR THIRD-PARTY COMPANY.
The Instructor
Hi, I am Robert Hernandez and I am an expert in the field of Mathematical Profit Optimization and Analytics. I have a degree in mathematical optimization and I have spent my entire career building data-driven forecasting and revenue optimization models for companies in over 20 different industries, from tech to tourism. I want to save you a lot of time and frustration by teaching exactly what you need to know to be a top Data Analyst.