Industrial & Systems Engineering

Learn core concepts, decision analytics, process optimization, and modern tools like reinforcement learning

What you'll learn

Understand the core principles and methodologies of Industrial and Systems Engineering, including systems thinking, optimization, and process improvement.

Apply practical tools and techniques, such as decision analytics, operations research, and simulation, to solve real-world problems effectively.

Analyze and optimize processes in manufacturing, logistics, and operations to improve efficiency and performance.

Learn to integrate modern concepts like reinforcement learning and data-driven decision-making into traditional Industrial Engineering practices.

Requirements

No prior experience or specialized tools are required for this course. A basic understanding of high school mathematics and an interest in problem-solving will be helpful, but everything you need to know will be explained step by step.

Description

This course offers a comprehensive introduction to Industrial and Systems Engineering, blending traditional principles with modern tools and techniques. Whether you’re just starting in the field or looking to expand your skillset, this course is designed to help you build a solid foundation and gain practical knowledge to address real-world challenges in various industries.Industrial and Systems Engineering is about finding better ways to get things done. It’s about improving processes, making smarter decisions, and designing systems that work efficiently. Throughout this course, you’ll explore essential topics like systems thinking, process optimization, and quality control, while also diving into more advanced areas like decision analytics and reinforcement learning.You’ll learn how to break down complex problems, analyze them systematically, and apply proven methods to develop effective solutions. From optimizing production lines to designing efficient supply chains, this course covers practical applications that are relevant across manufacturing, logistics, and operations.In addition to the technical content, the course will also highlight how these methods are being applied in modern industries to adapt to technological advancements. We’ll discuss real-world case studies and provide hands-on examples to ensure that you can confidently put your knowledge to use.By the end of the course, you’ll have a well-rounded understanding of Industrial and Systems Engineering, the ability to tackle challenges effectively, and the skills to create real impact in your field. No prior experience is required—just an interest in learning how to solve problems and improve systems.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Python Basics (Optinoal)

Lecture 2 What is Python?

Lecture 3 Anaconda & Jupyter & Visual Studio Code

Lecture 4 Google Colab

Lecture 5 Environment Setup

Lecture 6 Python Syntax & Basic Operations

Lecture 7 Data Structures: Lists, Tuples, Sets

Lecture 8 Control Structures & Looping

Lecture 9 Functions & Basic Functional Programming

Lecture 10 Intermediate Functions

Lecture 11 Dictionaries and Advanced Data Structures

Lecture 12 Exception Handling & Robust Code

Lecture 13 Modules, Packages & Importing Libraries

Lecture 14 File Handling

Lecture 15 Basic Object-Oriented Programming (OOP)

Lecture 16 Data Visualization Basics

Lecture 17 Advanced List Operations & Comprehensions

Section 3: Data Preprocessing (Optinonal)

Lecture 18 Data Quality

Lecture 19 Data Cleaning Techniques

Lecture 20 Handling Missing Values

Lecture 21 Dealing With Outliers

Lecture 22 Feature Scaling and Normalization

Lecture 23 Standardization

Lecture 24 Encoding Categorical Variables

Lecture 25 Feature Engineering

Lecture 26 Dimensionality Reduction

Section 4: Operations Research

Lecture 27 What's OR?

Lecture 28 Operations Research Tools

Lecture 29 Real World Operations Research

Lecture 30 Solver

Lecture 31 Mathematical Modeling - Intro

Lecture 32 Mathematical Modeling - Symbols & Notations

Lecture 33 Mathematical Modeling - Scenario

Lecture 34 Mathematical Modeling - LP Model

Lecture 35 Mathematical Modeling - LP Code

Lecture 36 Mathematical Modeling - LP Output

Section 5: Optimization

Lecture 37 What's Optimization?

Lecture 38 Optimization for Data Science

Section 6: Supply Chain Analytics

Lecture 39 Supply Chain Optimization - Intro

Lecture 40 Supply Chain Optimization - Case

Lecture 41 Supply Chain Optimization - Mathematical Model

Lecture 42 Supply Chain Optimization - Code

Lecture 43 Supply Chain Optimization - Output

Lecture 44 Facility Location Optimization - Intro

Lecture 45 Facility Location - Case

Lecture 46 Facility Location - Mathematical Model

Lecture 47 Facility Location - Code

Lecture 48 Facility Location - Output

Lecture 49 Facility Capacity Optimization - Intro

Lecture 50 Facility Capacity Optimization - Case

Lecture 51 Facility Capacity Optimization - Math Model

Lecture 52 Facility Capacity - Code

Lecture 53 Facility Capacity - Output

Lecture 54 Route Scheduling Optimization - Intro

Lecture 55 Route Scheduling Optimization - Case

Lecture 56 Route Scheduling Optimization - Math Model

Lecture 57 Route Scheduling Optimization - Code

Section 7: Sequantial Decision Making

Lecture 58 SDA - Intro

Lecture 59 Portfolio Management

Lecture 60 Dynamic Inventory Model

Lecture 61 Adaptive Market Planning

Section 8: System Simulation

Lecture 62 Decision-Making Workflow in Simulation

Lecture 63 Simulation Modeling Terminology

Lecture 64 Comparing Modeling and Simulation

Lecture 65 Classifying Simulation Models

Lecture 66 Setting Up the Simulation Model

Lecture 67 Exploring Discrete Event Simulation (DES)

Lecture 68 Bank Teller Simulation with Simpy

Lecture 69 Coffee Shop Queue with Simpy

Lecture 70 Car Wash Simulation with Simpy

Lecture 71 Restaurant Drive-Through Simulation with Simpy

Lecture 72 Traffic Light Simulation with Simpy

Section 9: Rockwell Arena Modules

Lecture 73 Create

Lecture 74 Dispose

Lecture 75 Process

Lecture 76 Decide

Lecture 77 Batch

Lecture 78 Seperate

Lecture 79 Assign

Lecture 80 Record

Lecture 81 Attribute

Lecture 82 Entity

Lecture 83 Queue

Lecture 84 Resource

Lecture 85 Variable

Lecture 86 Schedule

Lecture 87 Set

Section 10: Introduction to Finance

Lecture 88 Basic Finance Concepts

Lecture 89 Mathematical Foundations for Finance

Lecture 90 Introduction to Financial Markets

Lecture 91 Introduction to Financial Instruments

Lecture 92 Time Value of Money

Lecture 93 Basics of Forex Markets

Lecture 94 Introduction to Behavioral Finance

Lecture 95 Introduction to Risk and Return

Lecture 96 Fundamental Analysis

Lecture 97 Technical Analysis Basics

Lecture 98 Introduction to Portfolio Management

Lecture 99 Introduction to Corporate Finance

Lecture 100 Basics of Macroeconomics

Lecture 101 Introduction to Bonds and Fixed Income Securities

Lecture 102 Introduction to Derivatives

Students or professionals interested in Industrial and Systems Engineering who want to build a strong foundation in both traditional and modern practices,Anyone curious about decision analytics, process optimization, and the integration of technology like reinforcement learning into engineering systems.,Beginner learners looking to enter the field of Industrial Engineering or enhance their skills for practical, real-world applications.,Experienced professionals who want to expand their knowledge by exploring advanced topics and modern tools used in the field today.