Lean Six Sigma Green Belt Online Course with Python

Course Curriculum

Chapter 1: Welcome

Lecture 1: Let's get started

Lecture 2: Why use Python for Lean Six Sigma Data Analysis

Lecture 3: Six Sigma Data Analysis covered in Python in this Course

Lecture 4: Is Lean Six Sigma Relevant in the age of AI and Industry 4.0?

Chapter 2: Getting Started With Six Sigma

Lecture 1: What is Six Sigma

Lecture 2: What is '6' & what is 'Sigma' in Six Sigma

Lecture 3: How different is Six Sigma (99.9996%) from 99% good?

Lecture 4: How is Six Sigma used by Organizations?

Lecture 5: Six Sigma Benefits & Goals

Lecture 6: Introduction to Lean

Lecture 7: Lean and Six Sigma – How the combination is more powerful!

Lecture 8: Introduction to ClearCalls Case Study

Lecture 9: Important Terms in Six Sigma

Lecture 10: Six Sigma Roles

Lecture 11: Why Six Sigma

Chapter 3: Six Sigma Problem Solving Approach

Lecture 1: Introduction to Problem Solving

Lecture 2: Six Sigma Problem Solving Approaches

Lecture 3: Six Sigma DMAIC Projects

Lecture 4: Define Phase Deliverables

Lecture 5: Measure Phase Deliverables

Lecture 6: Analyze Phase Deliverables

Lecture 7: Improve Phase Deliverables

Lecture 8: Control Phase Deliverables

Lecture 9: Design for Six Sigma (DFSS) Overview

Lecture 10: Six Sigma Project Selection Methods

Chapter 4: Listening to Customers

Lecture 1: Types of Customers

Lecture 2: Voice of Customers Basics & why it matters?

Lecture 3: VOC Capturing Methods

Lecture 4: Plan, Execute and Analyze VOC

Lecture 5: Affinity Diagram Scenario

Lecture 6: Affinity Diagram

Lecture 7: Prioritizing Customer Needs using Kano Model

Lecture 8: Customer Satisfaction and Loyalty Measurement

Lecture 9: Activity: Create Affinity Diagram for Clear Calls Case Study

Chapter 5: Define Phase : Completing a Project Charter

Lecture 1: Project Charter Overview

Lecture 2: Completing Project Charter – Part 1

Lecture 3: Completing Project Charter – Part 2

Lecture 4: Completing Project Charter – Part 3

Lecture 5: Project Scoping using In-frame/Out-frame Tool

Lecture 6: Six Sigma Project Routines

Lecture 7: Activity: Create a Project Charter for ClearCalls Six Sigma Project Case Study

Chapter 6: Define Phase : Process Mapping Tools

Lecture 1: Process Mapping Tools – An overview

Lecture 2: SIPOC

Lecture 3: Process Flow Diagram

Lecture 4: Scenario where Deployment Flow Chart is useful

Lecture 5: Deployment Flow Charts

Lecture 6: Benefits of Deployment Flow Charts

Lecture 7: Activity: Create SIPOC and Process Map for the Clear Calls Case

Chapter 7: Measure : Cause & Effect Relationships

Lecture 1: Y=f(x) – Understanding the relationship between Output and Inputs

Lecture 2: Scenario where Cause & Effect Diagram is useful

Lecture 3: Cause & Effect Diagram

Lecture 4: Constructing a Cause & Effect Diagram

Lecture 5: Cause and Effect Matrix

Lecture 6: 5 Why Technique

Lecture 7: Activity: Cause & Effecti Diagram and 5Why for Clear Calls Case Study

Chapter 8: Measure Phase : Measurement System Analysis (MSA) or Gage R&R

Lecture 1: Elements of Measurement System

Lecture 2: Resolution & Accuracy

Lecture 3: Precision

Lecture 4: Discrete Gage R&R

Chapter 9: Measure Phase : Data Collection – Planning & Execution

Lecture 1: Types of Data

Lecture 2: Types of Measurement Scales

Lecture 3: Data Collection – An Overview

Lecture 4: Completing a Formal Data Collection Plan

Lecture 5: Data Collection Format

Chapter 10: Measure Phase: Data Sampling

Lecture 1: Sampling & Sampling Methods

Lecture 2: Population Sampling

Lecture 3: Process Sampling

Lecture 4: Sample Size Computation Part 1

Lecture 5: Sample Size Computation Part 2

Lecture 6: Activity : Clear Calls Case Sample Size Computation

Chapter 11: Getting started with Python

Lecture 1: Installing Python

Lecture 2: Getting Started with Jupyter I

Lecture 3: Getting Started with Jupyter II

Lecture 4: Data Types in Python

Lecture 5: Python Packages

Lecture 6: Numpy Basics

Lecture 7: Pandas Basics

Lecture 8: Data Clean up using Pandas

Chapter 12: Measure Phase : Introduction to Business Statistics

Lecture 1: Types of Statistics