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AI, Machine Learning & Data ScienceUdemy2026-7 Edition100% Free Video Course

Simulation Modeling for Data Science with Python and AI

Simulation Modeling for Data Science with Python and AI is a course on how to use simulation techniques, Python programming, and AI to model and analyze complex systems, published by Udemy Online Academy. Designed for data scientists, Python developers, engineers, researchers, and aspiring AI professionals, this course explores how simulation can be used to understand system behavior, test scenarios, estimate outcomes, and support data-driven decision making. Learners will work with probability, random variables, statistical distributions, Monte Carlo simulation, discrete event modeling, data analysis, and simulation workflows with the help of AI.In this valuable course, instead of learning patterns from the past, we model how the system itself works and then run thousands of possible scenarios to understand a wide range of possible outcomes. Simulation modeling complements the analytics, machine learning, and simulation you already know, allowing you to answer questions about new situations. If you can read Python and have ever provided analytics, you have the prerequisites. You won’t be writing code from scratch, but you will choose modeling parameters, validate the generated code using structured techniques, and communicate the results in a way that managers and executives can act on.

59 Video Lessons
15.5 Hours On-Demand
Created by Senior Industry Specialist
Uploaded Sep 2026
English
Simulation Modeling for Data Science with Python and AI
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Course Features:
15.5 hours on-demand video
59 complete lectures
Streamable on mobile, tablet & desktop
Self-paced curriculum with progress tracking
Direct MP4 downloads & offline video access
Verified course archives hosted on cloud infrastructure.

What You'll Master in this Course

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
and …

Course Curriculum59 Lectures

7 sections • 15.5 hours total length

Prefer offline learning? Download all 59 video lectures and project files for free.
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Requirements

  • Basic enthusiasm to learn and follow along with lessons
  • A computer or mobile device with a modern internet connection

Description

Simulation Modeling for Data Science with Python and AI is a course on how to use simulation techniques, Python programming, and AI to model and analyze complex systems, published by Udemy Online Academy. Designed for data scientists, Python developers, engineers, researchers, and aspiring AI professionals, this course explores how simulation can be used to understand system behavior, test scenarios, estimate outcomes, and support data-driven decision making. Learners will work with probability, random variables, statistical distributions, Monte Carlo simulation, discrete event modeling, data analysis, and simulation workflows with the help of AI.In this valuable course, instead of learning patterns from the past, we model how the system itself works and then run thousands of possible scenarios to understand a wide range of possible outcomes. Simulation modeling complements the analytics, machine learning, and simulation you already know, allowing you to answer questions about new situations. If you can read Python and have ever provided analytics, you have the prerequisites. You won’t be writing code from scratch, but you will choose modeling parameters, validate the generated code using structured techniques, and communicate the results in a way that managers and executives can act on.

Instructor

S

Senior Industry Specialist

Specialist in AI, Machine Learning & Data Science

Passionate educator focused on real-world practical skills, modern frameworks, and production-ready engineering practices. Delivering step-by-step masterclasses accessible to learners globally on MJ Accedemy.

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