Build Your Custom AI Forecast Analyst

Go beyond loading an Excel file into an AI chat tool.

You already know AI can answer a question about a file you upload. That isn't what this is. This is how you build a forecasting system that stays wired to the tools your business actually runs on, so the analysis happens whenever you ask, against live data, in a workflow you built and control.

Self-paced with no time limit and permanent access to all materials.

Join a community of students from leading global brands

20+ hoursShort, one-topic, on-demand videos

5-10 minutesLessons designed to fit into your lifestyle

Forever accessSelf-paced with no time limit

Real transaction dataWork from practical business examples

Unlimited course updatesEvery new module and improvement included for life

For analysts who have hit the ceiling with AI chat platforms

Your forecast should not start over every Monday.

You use AI chat platforms for analysis. You get useful answers. But the workflow is manual every single time: export a file, paste it in, explain your business again, prompt again when it loses direction, get a number, close the window. Next week you do all of it again. This course is for analysts who are done with that ceiling and ready to build a connected forecasting system that runs on their real data without the manual reset.

What you build

Control your entire forecasting ecosystem.

Not a one-time answer you paste into a chat window. A connected system that runs against your real data, on demand, whenever you need it.

connected_tools

Your business tools wired to an AI assistant through MCP, so it reads your real data instead of a copy you exported this morning.

data_ontology

A definition of what your fields actually mean, encoded once, so the assistant stops guessing what revenue, active, or churn refer to inside your business.

forecast_workflow

Forecasting that runs end to end across your tools, without you stitching the steps together by hand each time you need it.

defensible_output

Results you can take into a meeting, because you know how the model reached them and where it would break.

A connected system gives you what chat alone cannot:

Grounded answers. AI works from your actual business data instead of generating an answer from incomplete context.

Consistent analysis. Your workflow is codified, so the same analytical process runs every time.

No repeated explanations. Your ontology preserves your fields, definitions, metrics and business relationships.

A structured path from data to decision. The LLM remains guided through each stage of the forecasting workflow instead of losing direction during a long conversation.

Less wasted token use. Your workflow retrieves only the relevant context instead of repeatedly sending long instructions and entire datasets.

A skill that advances your career. Analysts who can build and direct connected AI systems are rare. This moves you from task executor to the person who owns the forecasting infrastructure.

The curriculum

Know every forecasting method.Know how to direct AI to apply them.

Data Extraction and Preparation

Forecasting starts before any model runs. Data rarely sits in one place in a usable form. It lives in source systems, CRMs, ERPs, databases, and exports that need to be extracted, moved through staging areas, cleaned, and structured before an AI agent can work with it reliably. This module covers how to pull data from real business systems, handle the transformation step that most courses skip entirely, and arrive at a dataset that is clean, consistent, and ready for analysis. Getting this step wrong quietly ruins every model downstream, and most forecasters never trace the error back to the source.

You will build a repeatable data preparation pipeline on real business data, identify and correct the quality issues that produce misleading forecasts, and understand how staging areas work so your workflow is not dependent on manual exports.

Your Data Ontology and MCP Setup

An AI agent does not know what your data means. It knows what the words say, but not what revenue means in your business versus a competitor's, what constitutes an active customer versus an inactive one, or how your metrics relate to each other. A data ontology solves this by encoding your field definitions, business logic, and entity relationships once, permanently, so every forecast the agent produces is reasoned in your terms rather than generic ones. MCP then wires your tools directly to the agent, so instead of pasting exported files into a chat window, the agent reads your live data and runs your defined workflow on demand.

You will build a working data ontology for your own business and configure an MCP connection to at least one of your real data sources, so the forecasting workflow you build in later modules runs against live data from day one.

Statistics Foundation

Every forecasting model produces statistical output. If you cannot read that output, you cannot evaluate whether the model is giving you a reliable answer or a confident-sounding wrong one. This module covers the statistical concepts that every serious forecaster needs: what a distribution tells you about your data, why correlation is not causation, what variance and standard deviation reveal about forecast reliability, how time series data behaves differently from a cross-sectional table, and what stationarity means and why it matters. None of this requires a statistics degree. It requires enough fluency to judge what the agent hands you.

You will learn to read and interpret the statistical outputs of every forecasting model in this course, so you can evaluate results with judgment instead of trusting a number the agent generated.

Statistical Forecasting Methods

Statistical forecasting methods have driven business decisions for decades because they work, they are interpretable, and for many real business problems they outperform far more complex algorithms. This module covers moving averages, exponential smoothing, seasonality and trend decomposition, regression-based forecasting, and Bayesian methods. For each one, you will understand how the method works mathematically, what type of data it is designed for, when it outperforms and when it breaks down, and how to direct an AI agent to apply it correctly to your specific business data rather than a generic dataset.

You will run each statistical method on real business data and build the judgment to know which one belongs on a given forecasting problem before the agent runs a single calculation.

Foundational Forecasting Algorithms

ARIMA, SARIMA, and Prophet are the foundational algorithmic methods of time series forecasting. Unlike the statistical methods in the previous module, these algorithms are specifically designed to detect hidden patterns in sequential data: cycles the human eye misses, seasonal rhythms that repeat across years, and trends that accelerate or decelerate gradually. ARIMA identifies autoregressive and moving average structure in the data. SARIMA extends this to handle seasonality. Prophet, built by Meta's data science team, handles business-specific seasonality, holidays, and trend changes with minimal configuration. Until recently, running these required fluency in Python or R. You will direct an AI agent to implement, tune, and interpret all three on your own data.

You will implement all three algorithms on real business time series, understand the parameters the agent is tuning and why, and translate the output into a management-ready forecast.

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Gradient and Ensemble Algorithms

XGBoost and Random Forest are the algorithms that dominate machine learning competitions and production forecasting systems at scale. Random Forest builds hundreds of decision trees simultaneously and takes a vote on the answer, producing forecasts that are highly robust to noise and outliers in real business data. XGBoost goes further with gradient boosting, building each new tree to correct the errors of the previous one, which is why it outperforms almost every other algorithm family on structured business data. Both reveal which variables in your data are actually driving your forecast outcomes, a capability that simple statistical methods cannot match. Understanding how these algorithms think is what separates an analyst who uses them from one who can direct an agent to apply them correctly.

You will build both algorithms on real business data, identify the predictors actually driving your outcomes, and develop the judgment to know when gradient and ensemble methods belong in your forecasting stack.

Deep Learning and Driver-Based Algorithms

LSTM networks are the deep learning architecture built for sequential data. Unlike every other algorithm in this course, LSTM models have memory. They learn which patterns from the distant past matter, how long their influence persists, and what sequences predict what comes next, including patterns that unfold over months rather than weeks. Driver-based machine learning takes a different approach, systematically identifying the variables that cause your business outcomes to move and modeling exactly how changes in those drivers ripple forward into your forecast. These are the methods used inside financial institutions, demand planning teams at large enterprises, and revenue optimization systems. You will direct an AI agent to run both, compare their performance against simpler methods, and understand when the added complexity is genuinely worth it.

You will implement LSTM and driver-based models on real business time series, benchmark them against the algorithms from previous modules, and build the criteria for when to escalate to deep learning versus staying with a simpler method.

Running the Live Workflow

A forecasting model that runs once is a project. A forecasting workflow that runs on live data whenever you need it is a system. This module covers rolling forecasts that update automatically as new data arrives, automation pipelines that remove the manual steps between data and output, and connections to transaction systems for streaming forecasts that reflect what is happening right now rather than what happened last month. With MCP already configured from Module 02, this module turns your individual models into a connected workflow: the agent pulls fresh data, selects and runs the appropriate model, and delivers output without you stitching the steps together by hand.

You will build a rolling forecast that updates on new data, automate the pipeline from extraction to output, and run the complete workflow end to end on live data.

Directing the AI Agent

Knowing how every forecasting algorithm works is the foundation. Knowing how to direct an AI agent to apply the right one, at the right moment, on the right data is the skill that separates a great forecaster from someone who runs models. This module covers how to guide model selection for a given problem, how to structure the prompt and workflow so the agent stays on track through a multi-step analysis, how to recognize when the agent is producing a confident-sounding wrong answer, and how to intervene and correct it. The agent does not have your business judgment. You do. This module is about how those two things work together.

You will develop a personal framework for directing AI agents through complex forecasting problems, including the checkpoints, correction prompts, and validation steps that keep the agent's output aligned with your business reality.

Validation and Defensibility

A forecast without a measure of its own accuracy is a guess with a spreadsheet attached. This module covers the accuracy metrics professional forecasters use: MAE, RMSE, and MAPE, explained in plain language with no statistical notation required. More importantly, it covers how to distinguish random error from systematic bias, how to diagnose why a forecast is consistently wrong in a particular direction, and how to apply targeted fixes that improve accuracy across future cycles. It also covers how to present a forecast credibly to people who will push back on it, including how to communicate uncertainty, show your model's limitations honestly, and defend the output under pressure without overstating your confidence.

You will calculate accuracy metrics on your own forecasts, identify and correct systematic bias, and build a repeatable validation process that makes your forecasting more reliable with every new cycle of data.

The distinction

Loading a file into an AI chat tool is not a forecasting ecosystem.

It is a one-time export. You get an answer. You close the window. Next week you do it again. Nothing is connected, nothing persists, nothing runs without you.

This course builds the other thing: a forecasting system wired to the tools your business actually runs on, so the analysis is always there, always current, and always yours to control.

Chat-based — every session

YOU Export data from your system

YOU Paste it into the chat window

YOU Re-explain your business context

YOU Re-prompt when the model loses direction

→ Answer. Discarded. Start over next week.

Connected — set up once

YOU ONCE Wire your tools via MCP

YOU ONCE Build your data ontology

YOU ONCE Define your forecasting workflow

AUTO Reads live data. Runs the workflow. Delivers output.

↻ Runs again on live data, any time you ask.

Every piece of the right diagram is something you assemble yourself, on your own data, with no code required.

What Makes This Course Different

4 Sessions Included

This is not a course where you watch videos and hope the pieces fall into place. Every student gets 4 private 60-minute sessions with Robert to work through the material on their own data, apply every technique to their own forecasting system, and get real answers to real business questions. Additional sessions are available at $200/hour.

Stuck on a concept?

Book a session and Robert walks you through it on your own data until it clicks. No time limits, no ticketing system.

Not sure which model fits your problem?

Bring your actual business situation. Robert helps you select the right algorithm, run it, and interpret the output.

Want to apply this to your own data?

That is the entire point. Every technique in this course gets applied to your specific business data in your personal sessions.

Sessions cover course concepts and applying the workflow to your own data. Not done-for-you consulting.

Student results

6,000+ analysts. Here is what they say.

★★★★★

"This course was a great learning experience — well-structured, short videos, easy to study at your own pace. The SQL and Excel content are extremely useful and I will definitely be applying much of this in my work in business intelligence."

JBJulia BaunemannBusiness Intelligence

★★★★★

"Robert took me step-by-step into understanding the how and the why of every process. Now I can understand my demand patterns and optimize prices based on real information. I discovered the beauty of Data Science through these courses."

YEYassir EL BASSIMAnalytics Professional

★★★★★

"I wish I had done this many years ago — it would have helped so much during my career as an analytics leader. This course provides a great tool to learn analytics from how to retrieve data all the way to making pricing decisions and creating forecasts."

RDRozsa DimandoAnalytics Leader

★★★★★

"I cannot begin to tell you the value of this course. It provided the tools to be mathematically specific in what I ask our reporting departments to do. Understanding the SQL reporting capabilities was golden."

LCLisa CullyAnalytics Director

★★★★★

"Robert was able to break complex material into small, easy-to-consume explanations. I know the value of myself and my company have vastly improved with the completion of this course."

BGBryan GatzemeyerBusiness Professional

★★★★★

"These courses have shed new light on how I can improve my Excel workbooks. It is quite obvious you are what analytics needs to be and I look forward to modeling myself in these practices."

MGMark GrossmanIndependent Hotel Manager

★★★★★

"Having been through a number of analytics courses including one from Cornell, yours really explains and shows the calculations behind the systems. Your concept of looking at individual transactions opened a whole new world for me."

RHRonald HopmanAnalytics Professional

★★★★★

"Though I have finished the Cornell Advanced Analytics certification, I felt your course is something I should have learned earlier."

APAbhijeet P.kCertified Analytics Professional

★★★★★

"The presentations are expertly done, presenting difficult mathematical concepts in simple language. Your concept of looking at individual transactions opened a whole new world for me."

IDIvan R. DrechslerAnalytics Professional

★★★★★

"The body of knowledge is the true science of analytics. These skills will equip you with the confidence and wisdom you need to apply analytics concepts."

RARonald AlliAnalytics Professional

★★★★★

"I've taken many other analytics courses but... if you are serious about analytics, you have to take this course."

CPConstantino PapadopulosAnalytics Professional

★★★★★

"This is the best analytics course I have ever taken."

NTNikolaos TsitsoulisAnalytics Professional

Enrollment

Learn forecasting as a serious business analytics discipline.

100% Money-back Guarantee.
Real business data, reusable forecasting models, templates, and Certificate of Completion.
Lifetime access and all future course updates.

Meet Your Instructor

Robert Hernandez

I am Robert Hernandez, and I am committed to helping you learn everything I know, without wasting your time on purely academic skills that do not translate into real-world value. I bring a rare combination of cross-disciplinary experience, advanced mathematical and analytics skills, technical competency, multi-industry knowledge, and business process improvement expertise. I started my career in FP&A and am an energetic and dedicated analytics professional with strong business acumen and more than 20 years of experience creating and managing data-driven models and processes for diverse organizations, both on-site and remotely. I have worked at Walt Disney and HP/Compaq, and have consulted for dozens of companies across industries, from e-commerce to entertainment to energy.

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Have a Question about this course? Email me.

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