Instructor-led 4-days

Course Description:

This 4-Day immersive course is designed to provide participants with a solid foundation in data modeling and simulation techniques used for analytical decision-making, forecasting, and system design. Learners will explore conceptual, logical, and physical data models and perform simulations using statistical and computational tools.

Through a combination of theory, case studies, and interactive labs, participants will gain hands-on experience building models, validating assumptions, running simulations, and interpreting results using Python, Excel, and specialized modeling libraries like SimPy and @Risk.

Prerequisites:

• Basic proficiency in Python or Excel
• Understanding of basic statistics and probability
• Familiarity with databases or data structures is helpful

Key Takeaways:

• Understand the types and stages of data modeling (conceptual, logical, physical)
• Build entity-relationship diagrams and translate them into data schemas
• Apply simulation techniques to model uncertainty and randomness
• Use Monte Carlo simulation to support risk-based decision-making
• Model systems and processes using discrete-event simulation
• Gain hands-on experience with tools like SimPy, Excel, and Python
• Learn how to validate and calibrate models using real-world data

Module 1: Foundations of Data Modeling

Topics:

• Introduction to Data Modeling
• Conceptual vs Logical vs Physical Models
• Entity-Relationship (ER) Modeling
• Normalization and Schema Design

Hands-On Labs:

• Lab 1: Create an ER diagram using a case study (e.g., Hospital Database)
• Lab 2: Translate ER diagram into a normalized relational schema
• Lab 3: Model a database using SQL (CREATE TABLE statements)

🟨 Module 2: Simulation Basics & Statistical Modeling

Topics:

• Introduction to Simulation and its Applications
• Random Variables, Distributions, and Probabilistic Thinking
• Monte Carlo Simulation Basics
• Modeling in Excel vs Python

Hands-On Labs:

• Lab 4: Simulate inventory demand with Excel’s RAND and NORMINV
• Lab 5: Write a basic Monte Carlo simulation in Python to estimate Pi
• Lab 6: Run a financial forecast simulation for a small business

Module 3: Discrete-Event Simulation & Process Modeling

Topics:

• Discrete-Event Simulation (DES) Concepts
• Modeling Queues, Events, and Resources
• Introduction to SimPy (Python Simulation Library)
• Process Modeling in Operations Research

Hands-On Labs:

• Lab 7: Model a checkout line with SimPy (multi-server queue)
• Lab 8: Simulate patient flow in a clinic using DES
• Lab 9: Build a simple traffic simulation using SimPy

🟥 Module 4: Advanced Simulation Techniques & Capstone

Topics:

• Sensitivity Analysis and Scenario Testing
• Model Calibration and Validation
• Communicating Results through Visualization
• Capstone Project and Presentations

Hands-On Labs:

• Lab 10: Use matplotlib/Seaborn to visualize simulation output
• Lab 11: Perform sensitivity analysis with parameter variation