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Industrial & Systems Engineering

Advancedor Academy

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.

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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.

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What's inside

Learning objectives

  • 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.

Syllabus

Introduction
Start Smart: A Guide to Mastering This Course
IE History and Evolution
What do Industrial Engineers do?
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Python Basics (Optinoal)
What is Python?
Anaconda & Jupyter & Visual Studio Code
Google Colab
Environment Setup
Python Syntax & Basic Operations
Data Structures: Lists, Tuples, Sets
Control Structures & Looping
Functions & Basic Functional Programming
Intermediate Functions
Dictionaries and Advanced Data Structures
Exception Handling & Robust Code
Modules, Packages & Importing Libraries
File Handling
Basic Object-Oriented Programming (OOP)
Data Visualization Basics
Advanced List Operations & Comprehensions
Data Preprocessing (Optinonal)
Data Quality
Data Cleaning Techniques
Handling Missing Values
Dealing With Outliers
Feature Scaling and Normalization
Standardization
Encoding Categorical Variables
Feature Engineering
Dimensionality Reduction
Linear Algebra
Introduction to Vectors and Vector Operations - Visual Explanation
Vectors and Vector Operations
Eigenvalues and Eigenvectors
Probability & Statistics
Review of Probability Theory
Probability Distributions
Hypothesis Testing
Bayes' Theorem
Descriptive Statistics
Normal Distribution
Uniform Distribution
Binomial Distribution
Inferential Statistics
Operations Research
What's OR?
Operations Research Tools
Real World Operations Research
Solver
Mathematical Modeling - Intro
Mathematical Modeling - Symbols & Notations
Mathematical Modeling - Scenario
Mathematical Modeling - LP Model
Mathematical Modeling - LP Code
Mathematical Modeling - LP Output
Information About Simplex Lessons
Linear Programming - Intro
Formulating LP Problem
Standard Form of LP
Basic Example of LP
Canonical Form of LP
Fundamentals of the Simplex Method
Steps - Simplex
Manual Example - Simplex
Integer Programming - Branch and Bound | Intro
Branch and Bound | Diagram
Branch and Bound | Knapsack
Branch and Bound | Production Planning
Mixed Integer Nonlinear Programming - Intro
Mixed Integer Nonlinear Programming - Mathematical Model
Mixed Integer Nonlinear Programming - Code
Mixed Integer Nonlinear Programming - Output
Multi-Period Portfolio Optimization with Julia
Nonlinear Programming
Nonlinear Programming - Case Study
Nonlinear Programming - Code
Nonlinear Programming - Output
Karush-Kuhn-Tucker (KKT) Conditions
Optimization
What's Optimization?
Optimization for Data Science
Numeric Optimization - Intro
Numeric Optimization - Case Study
Numeric Optimization - Mathematical Modeling
Numeric Optimization - Code
Numeric Optimization - Output
Lean Manufacturing Optimization - Code
Lean Manufacturing Optimization - Output
Robust Optimization - Intro
Robust Optimization - Mathematical Model
Robust Optimization - Code & Output
Large Scale Optimization
Supply Chain Analytics
Supply Chain Optimization - Intro
Supply Chain Optimization - Case
Supply Chain Optimization - Mathematical Model
Supply Chain Optimization - Code
Supply Chain Optimization - Output

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Industrial & Systems Engineering with these activities:
Review Probability Theory
Solidify your understanding of probability theory to better grasp concepts in operations research and supply chain analytics.
Browse courses on Probability Theory
Show steps
  • Review basic probability concepts.
  • Work through practice problems.
  • Consult online resources for clarification.
Read 'Introduction to Operations Research' by Hillier and Lieberman
Gain a deeper understanding of operations research principles with a comprehensive textbook.
View ISE Introduction to Operations Research on Amazon
Show steps
  • Read assigned chapters related to the course syllabus.
  • Work through the examples provided in the book.
  • Attempt the end-of-chapter problems.
Read 'The Goal: A Process of Ongoing Improvement'
Understand the Theory of Constraints through a narrative approach, enhancing your grasp of process improvement.
View The Goal: A Process of Ongoing Improvement -... on Amazon
Show steps
  • Read a chapter each day.
  • Reflect on the concepts presented.
  • Relate the concepts to real-world scenarios.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Linear Programming Problem Sets
Reinforce your understanding of linear programming by solving a variety of practice problems.
Show steps
  • Find a collection of linear programming problems.
  • Solve each problem using the Simplex method or software.
  • Check your answers and review the solutions.
Optimize a Local Business Process
Apply industrial engineering principles to a real-world scenario by optimizing a process at a local business.
Show steps
  • Identify a local business with a process that can be improved.
  • Analyze the current process and identify bottlenecks.
  • Develop a plan to optimize the process.
  • Implement the plan and measure the results.
Create a Video Explaining Simplex Method
Solidify your understanding of the Simplex method by creating a video tutorial explaining the process.
Show steps
  • Review the Simplex method thoroughly.
  • Prepare a script and visual aids.
  • Record and edit the video.
  • Share the video with peers for feedback.
Build a Supply Chain Simulation
Create a simulation model of a supply chain to analyze and optimize its performance.
Show steps
  • Choose a supply chain to model.
  • Gather data on the supply chain's parameters.
  • Build a simulation model using appropriate software.
  • Run simulations and analyze the results.
  • Present your findings in a report.

Career center

Learners who complete Industrial & Systems Engineering will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Industrial & Systems Engineering.
Cover image
Introduction to Operations Research ISE
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Comprehensive textbook on operations research, covering a wide range of topics including linear programming, network optimization, and queuing theory. It is commonly used in university courses and provides a strong theoretical foundation. This book is valuable as a reference for understanding the mathematical underpinnings of operations research techniques. It adds depth to the course by providing detailed explanations and numerous examples.
ISE Introduction to Operations Research
Paperback
$$$
Introduction to Operations Research: 2024 Release...
Paperback
$$$
Cover image
The Goal
Save
Introduces the Theory of Constraints, a crucial concept in industrial engineering. It presents the material in a novel-like format, making it engaging and easy to understand. It is highly recommended as a supplementary reading to understand process improvement and optimization. This book provides a practical and intuitive understanding of constraint management.
The Goal: A Process of Ongoing Improvement - 30th...
Paperback
$$
The Goal: Excellence In Manufacturing
Paperback
$$$$
(日本語) The Goal, Second Revised Edition
Tankobon Softcover
$$
The Goal: A Process of Ongoing Improvement
Paperback
$$$
The Goal: A Process of Ongoing Improvement
Kindle Edition
$$

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Effort
34.5 total hours
Via
Udemy
Instructor
Advancedor Academy
Language
English
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