该课程专为零编程和统计背景的商业决策者设计，核心在于将 AI（如 Claude 或 ChatGPT）转化为战略分析的超级杠杆。学员将掌握“先规格、后生成”的 AI 工作流，无需亲自动手编写代码，即可指导 AI 构建出蒙特卡洛组合风险和离散事件运营流等高级模拟模型，从而对资本准备、人员排班或新产品发布等缺乏历史数据的未来场景，进行严谨的“What-if”情境预测与置信区间分析。
为了确保模型结果能够经受住高管层的质疑，课程独创了“阅读、断言、压力测试”三层验证法，教你如何有效识别并纠正 AI 生成代码中的逻辑漏洞。最终，学员不仅能熟练运用系统动力学或智能体建模等不同范式来拆解复杂的现实问题，还将学会如何将深奥的模拟结果浓缩进一张高度可视化的“一页纸报告”中，用商业语言说服怀疑的利益相关者，真正赢得战略决策的主导权。
Published 7/2026
Created by Dan Sullivan
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 59 Lectures ( 4h 57m ) | Size: 2 GB
Answer business questions under uncertainty: spec, generate, and validate simulation models with AI
What you’ll learn
⚡ Use a spec-first AI workflow: write a structured model spec, generate working Python with Claude or ChatGPT, and iterate without coding from scratch
⚡ Answer real business questions under uncertainty, from capital reserves to staffing plans to product launches, with models you build yourself
⚡ Validate AI-generated code in three layers (read, assert, stress-test) so you can defend your results to a skeptical stakeholder
⚡ Run the what-if scenarios analytics and ML can’t: simulate futures with no historical precedent and report them with confidence intervals
⚡ Build a Monte Carlo loan portfolio risk model with correlated defaults, policy levers, and recession stress scenarios
⚡ Build a discrete-event clinic patient-flow model with realistic arrivals, peak-aware staffing, and an ops manager dashboard
⚡ Choose the right paradigm for a new problem (Monte Carlo, discrete-event, system dynamics, agent-based) and the right distribution for each uncertain input
⚡ Communicate simulation results in a one-page deliverable that earns the model the right to be acted on
Requirements
❗ No programming or statistics background required. You’ll read and verify code rather than write it, and every math concept is taught picture-first, with no calculus needed.
❗ A free account with an AI chat assistant such as Claude or ChatGPT, for generating code from your specs.
❗ A web browser. Labs run in Udemy’s built-in Jupyter environment, so there’s nothing to install; the final section covers optional local setup.
Description
AI can write working code in seconds. Depending on what you do next, that’s either a threat to your career or the biggest lever you’ve ever been handed. The difference comes down to one skill: knowing exactly what to ask for, and how to verify the answer before anyone acts on it. This course teaches that skill through simulation modeling, the craft of answering business questions under uncertainty with models you can defend.
Those questions are everywhere once you look
✨ How much capital should a bank hold against next year’s loan losses?
✨ How many doctors does the clinic need on Monday morning?
✨ Should the launch budget go to ads or to influencers?
Five years ago, answering them meant hiring specialists. Generative AI changed the economics: writing simulation code used to take a senior engineer a week, and for a well-specified problem it now takes an afternoon. The bottleneck moved from typing to thinking, and thinking is where the value always lived. This course puts you on the valuable side of that line.
Already work in analytics or machine learning? Simulation fills the gap those tools leave open
✨ Analytics and ML learn from the past. They’re the right tools when yesterday’s data describes tomorrow’s world.
✨ They struggle exactly where the stakes run highest: a decision no one has made before, a shock with no precedent in the training data, a what-if question about a policy that’s never been tried.
✨ Simulation works from the other direction. You model how the system itself works (the arrivals, the queues, the defaults, the feedback loops), then run the futures that haven’t happened yet.
✨ When a stakeholder asks “what if demand doubles?” or “how bad could a severe recession get?”, a model trained on history can only extrapolate. A simulation answers, complete with confidence intervals.
That’s why simulation belongs in every analyst’s and data scientist’s toolkit, right alongside the skills you already have.
One workflow, every model. You’ll practice the same five steps on everything you build
✨Spec it. Write a structured, plain-language description of the business question, the assumptions with sources, the process, and the checks that prove the model is right.
✨Generate it. Paste your spec into Claude or ChatGPT and get working Python back.
✨Run it. Execute in a Jupyter notebook in your browser. Udemy provides the environment, so there’s nothing to install.
✨Validate it. Three layers: read the code, run the assertions, stress-test with extreme inputs.
✨Iterate. When the results raise a new question, edit the spec and regenerate.
That rhythm is the most transferable skill the course teaches, and it works the same whether the model is a risk analysis or anything else you’ll ask AI to build for you.
What you’ll build. The course covers the four major simulation paradigms, two in depth and two at awareness level
✨Monte Carlo (deep practice): a community bank’s loan portfolio risk model, built across five labs from a 1,000-loan base model through correlated defaults, policy levers, and recession stress scenarios, ending with a one-page summary a treasurer could take to the board.
✨Discrete-event (deep practice): a clinic patient-flow model with realistic time-of-day arrivals, peak-aware staffing, and an ops manager dashboard.
✨System dynamics (awareness): why feedback loops and delays produce boom and bust.
✨Agent-based (awareness): how product adoption emerges from thousands of individual choices.
✨Capstone: pick one of three open-ended business problems with no paradigm label attached, choose the lens, write the spec from scratch, validate the model, and deliver a recommendation a stakeholder could act on.
Built for accessibility. You don’t need a stats degree or a coding background
✨ Math is taught picture-first with formulas optional; nothing requires calculus.
✨ You’ll learn the handful of probability distributions that do most of the work in business modeling.
✨ You won’t write Python from scratch. You’ll learn to read it, question it, and verify it, which is exactly the skill the age of AI rewards.
The workflow you’ll practice here also compounds. The specs you write today will generate better code as the AI tools improve, without the specs needing to change, and the verification habit only grows more valuable as more of the world’s code comes from tools that can be subtly wrong. Enroll, and start building the models your decisions have been waiting for.
What you’ll learn
✨ Answer real business questions under uncertainty: capital reserves, staffing plans, and product launches, with models you build yourself
✨ Use a spec-first AI workflow: write a structured model spec, generate Python with Claude or ChatGPT, and iterate without writing code from scratch
✨ Validate AI-generated code in three layers (read, assert, stress-test) so you can defend the results to a skeptical stakeholder
✨ Build a Monte Carlo loan portfolio risk model with confidence intervals, correlated defaults, and recession stress scenarios
✨ Build a discrete-event clinic patient-flow model with realistic arrivals, peak-aware staffing, and an ops dashboard
✨ Recognize when system dynamics or agent-based modeling fits a problem, and choose the right paradigm before you build
✨ Pick the right probability distribution for an uncertain input, and avoid the traps that quietly corrupt business models
✨ Communicate simulation results in a one-page deliverable that earns the model the right to be acted on
Requirements
✨ No programming or statistics background required; you’ll read and verify code rather than write it from scratch, and every concept is taught picture-first
✨ A free account with an AI chat assistant such as Claude or ChatGPT for generating code from your specs
✨ A browser; labs run in Udemy’s built-in Jupyter environment, so there’s nothing to install (the final section covers local setup for those who want it)
Who this course is for
⭐ Business analysts, finance professionals, operations leads, and marketing managers who face decisions under uncertainty and are tired of waiting on an engineering queue.
⭐ Data analysts and data scientists who want to answer the what-if questions their historical data can’t, and add simulation to their analytics and ML toolkit.
⭐ Professionals who want a durable, defensible way to work with AI: specifying problems and verifying answers rather than competing with it at writing code.
⭐ This course deliberately does not cover machine learning, time-series forecasting, or optimization.
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