Lean Six Sigma Green Belt Online Course With Python

Prepare for Six Sigma Green Belt Certification & Perform Data Analysis Using Python - No Programming Experience Needed

What you'll learn

Prepare for Lean Six Sigma Green Belt Certification

Able to perform various Lean Six Sigma Dat Analysis using Python

No Programming Experience Needed - Python Data Analysis will be covered step by step in videos

Easily solve real life business & home related problems using Lean Six Sigma Techniques

Requirements

None

Description

Why you should consider the FIRST LEAN SIX SIGMA GREEN BELT CERTIFICATION COURSE USING PYTHON?There is no need to emphasize the importance of Data Science or Lean Six Sigma in today's Job MarketPython is the most popular and trending tool for Data Science nowLean Six Sigma involves a lot of Data Analysis & Statistical DiscoveryTraditionally Lean Six Sigma Data Analysis uses Minitab & ExcelIN CURRENT SCENARIO, if you are NOT learning Lean Six Sigma Green Belt Data Analysis using Python, it's obvious what you are missing!GET THE BEST OF LEAN SIX SIGMA GREEN BELT CERTIFICATION & DATA SCIENCE WITH PYTHON IN ONE COURSE & AT ONE SHOTWhat to Expect in this Course?Prepare for ASQ / IASSC CSSGB Certification 176 Lectures / 17 Hours of ContentData Analysis in Python  with Step by Step Procedure for All Six Sigma Analysis - No Programming Experience NeededData Manupulation in PythonDescriptive StatisticsHistogram, Distribution Curve, Confidence levelsBoxplotStem & Leaf PlotScatter PlotHeat MapPearson’s CorrelationMultiple Linear RegressionANOVAT-tests – 1t, 2t and Paired tProportions Test - 1P, 2PChi-square TestSPC (Control Charts - mR, XbarR, XbarS, NP, P, C, U charts)Python Packages - Numpy, Pandas, Matplotlib, Seaborn, Statsmodels, Scipy, PySPC, StemgraphicFull Fledged Lean Six Sigma Case Study with Solutions (in Python Scripts)More than 100 Resources to Download (including Python Source Files for all the analysisPractice questions - 19 Crossword puzzle questions on various six sigma topics included

Overview

Section 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

Section 2: Getting Started With Six Sigma

Lecture 4 What is Six Sigma

Lecture 5 What is '6' & what is 'Sigma' in Six Sigma

Lecture 6 How different is Six Sigma (99.9996%) from 99% good?

Lecture 7 How is Six Sigma used by Organizations?

Lecture 8 Six Sigma Benefits & Goals

Lecture 9 Introduction to Lean

Lecture 10 Lean and Six Sigma - How the combination is more powerful!

Lecture 11 Introduction to ClearCalls Case Study

Lecture 12 Important Terms in Six Sigma

Lecture 13 Six Sigma Roles

Lecture 14 Why Six Sigma

Section 3: Six Sigma Problem Solving Approach

Lecture 15 Introduction to Problem Solving

Lecture 16 Six Sigma Problem Solving Approaches

Lecture 17 Six Sigma DMAIC Projects

Lecture 18 Define Phase Deliverables

Lecture 19 Measure Phase Deliverables

Lecture 20 Analyze Phase Deliverables

Lecture 21 Improve Phase Deliverables

Lecture 22 Control Phase Deliverables

Lecture 23 Design for Six Sigma (DFSS) Overview

Lecture 24 Six Sigma Project Selection Methods

Section 4: Listening to Customers

Lecture 25 Types of Customers

Lecture 26 Voice of Customers Basics & why it matters?

Lecture 27 VOC Capturing Methods

Lecture 28 Plan, Execute and Analyze VOC

Lecture 29 Affinity Diagram Scenario

Lecture 30 Affinity Diagram

Lecture 31 Prioritizing Customer Needs using Kano Model

Lecture 32 Customer Satisfaction and Loyalty Measurement

Lecture 33 Activity: Create Affinity Diagram for Clear Calls Case Study

Section 5: Define Phase : Completing a Project Charter

Lecture 34 Project Charter Overview

Lecture 35 Completing Project Charter - Part 1

Lecture 36 Completing Project Charter - Part 2

Lecture 37 Completing Project Charter - Part 3

Lecture 38 Project Scoping using In-frame/Out-frame Tool

Lecture 39 Six Sigma Project Routines

Lecture 40 Activity: Create a Project Charter for ClearCalls Six Sigma Project Case Study

Section 6: Define Phase : Process Mapping Tools

Lecture 41 Process Mapping Tools - An overview

Lecture 42 SIPOC

Lecture 43 Process Flow Diagram

Lecture 44 Scenario where Deployment Flow Chart is useful

Lecture 45 Deployment Flow Charts

Lecture 46 Benefits of Deployment Flow Charts

Lecture 47 Activity: Create SIPOC and Process Map for the Clear Calls Case

Section 7: Measure : Cause & Effect Relationships

Lecture 48 Y=f(x) - Understanding the relationship between Output and Inputs

Lecture 49 Scenario where Cause & Effect Diagram is useful

Lecture 50 Cause & Effect Diagram

Lecture 51 Constructing a Cause & Effect Diagram

Lecture 52 Cause and Effect Matrix

Lecture 53 5 Why Technique

Lecture 54 Activity: Cause & Effecti Diagram and 5Why for Clear Calls Case Study

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

Lecture 55 Elements of Measurement System

Lecture 56 Resolution & Accuracy

Lecture 57 Precision

Lecture 58 Discrete Gage R&R

Section 9: Measure Phase : Data Collection - Planning & Execution

Lecture 59 Types of Data

Lecture 60 Types of Measurement Scales

Lecture 61 Data Collection - An Overview

Lecture 62 Completing a Formal Data Collection Plan

Lecture 63 Data Collection Format

Section 10: Measure Phase: Data Sampling

Lecture 64 Sampling & Sampling Methods

Lecture 65 Population Sampling

Lecture 66 Process Sampling

Lecture 67 Sample Size Computation Part 1

Lecture 68 Sample Size Computation Part 2

Lecture 69 Activity : Clear Calls Case Sample Size Computation

Section 11: Getting started with Python

Lecture 70 Installing Python

Lecture 71 Getting Started with Jupyter I

Lecture 72 Getting Started with Jupyter II

Lecture 73 Data Types in Python

Lecture 74 Python Packages

Lecture 75 Numpy Basics

Lecture 76 Pandas Basics

Lecture 77 Data Clean up using Pandas

Section 12: Measure Phase : Introduction to Business Statistics

Lecture 78 Types of Statistics

Lecture 79 Scenario where Histogram is useful

Lecture 80 Understanding Histograms

Lecture 81 Interpretation of Histograms

Lecture 82 Probability Distributions

Lecture 83 Measures of Central Tendency

Lecture 84 Measures of Dispersion

Lecture 85 Understanding Normal Distribution

Lecture 86 Understanding Outliers

Lecture 87 Skewness & Kurtosis

Lecture 88 Confidence Level and Limits

Lecture 89 Descriptive Statistics in Python

Lecture 90 Plotting Histogram in Python

Lecture 91 Computing Confidence Interval in Python

Lecture 92 Normality Tests in Python

Lecture 93 Activity: Descriptive Analysis for Clear Calls Case Study

Section 13: Measure Phase : Graphical Analysis Methods

Lecture 94 Box & Whisker Plots

Lecture 95 Creating Box Plots in Python

Lecture 96 Stem & Leaf Plots in Python

Lecture 97 Scenario where Run Chart is useful

Lecture 98 Understanding Run Charts

Lecture 99 Detecting 4 patterns using Run Charts

Lecture 100 Using Run Table to draw inferences

Lecture 101 Activity: Graphical Analysis for Clear Calls Case Study

Section 14: Measure Phase: Assessing Process Capability

Lecture 102 Understanding Process Capability

Lecture 103 Application of Process Capability

Lecture 104 Performing Process Capability Study

Lecture 105 Perform Process Capability in Python

Lecture 106 Process Capability in Minitab (For Understanding Purpose)

Lecture 107 Role of Long Term Process Capability

Lecture 108 Activity: Perform Process Capability for Clear Calls Case Study

Section 15: Analyze Phase : Root Cause Analysis

Lecture 109 Approach to RCA

Section 16: Analyze Phase : Theory of Hypothesis Testing

Lecture 110 Introduction to Statistical Hypothesis

Lecture 111 Framing Hypothesis Statements

Lecture 112 Understanding Statistical Significance and Alpha Level

Lecture 113 Statistical Vs Practical Significance

Lecture 114 Understanding the role of Test Statistic

Lecture 115 Understanding the role of Critical Statistic

Lecture 116 P-Value and its importance in Hypothesis Testing

Lecture 117 Errors associated with Hypothesis Testing

Section 17: Analyze Phase : Performing Hypothesis Tests

Lecture 118 Selection of appropriate Hypothesis Tests

Lecture 119 Tests for Means

Lecture 120 Perform 1 t Test in Python

Lecture 121 Perform 2 t Test in Python

Lecture 122 Perform Paired t Test in Python

Lecture 123 Analysis of Variance (ANOVA)

Lecture 124 Perform ANOVA in Python

Lecture 125 Chi-square Tests

Lecture 126 Perform Chisquare test in Python

Lecture 127 Proportions Tests

Lecture 128 Perform 1P Test in Python

Lecture 129 Perform 2P Test in Python

Lecture 130 Scenario where Scatter Diagram is useful

Lecture 131 Using Scatter Diagram to study association

Lecture 132 Creating Scatter Diagram in Python

Lecture 133 Using Correlation Coefficient to establish relationships

Lecture 134 Computing Correlation Coefficient in Python

Lecture 135 Introduction to Regression Analysis

Lecture 136 Line of Best Fit in EXCEL

Lecture 137 Regression in Python

Lecture 138 Activity: Perform Various Hypothesis for Clear Calls Case Study

Section 18: Analyze Phase : Quantification of Opportunity to Improve

Lecture 139 Using Process Value Analysis as an Alternate to Hypothesis Testing

Lecture 140 Scenario where Pareto is useful

Lecture 141 Using Pareto Diagram to Prioritize Causes

Lecture 142 Narrowing down to actionable areas with Control-Impact Matrix

Lecture 143 Activity : Perform Process Value Analysis for Clear Calls data

Section 19: Improve Phase : Generating & Screening Solutions

Lecture 144 Lateral Thinking & Random Stimulus

Lecture 145 Practical Brainstorming Tools

Lecture 146 Types of Brainstorming

Lecture 147 Introduction to Idea Screening Techniques

Lecture 148 First Pass Idea Screening Tools

Lecture 149 Second Pass Idea Screening Tools

Lecture 150 Design of Experiments

Section 20: Improve Phase: Lean Management Systems (Repeated from Section 2)

Lecture 151 Introduction to Lean

Lecture 152 Lean Principles

Lecture 153 Concept of Muda

Lecture 154 Types of Wastes

Lecture 155 Value Stream Mappping

Lecture 156 5S

Lecture 157 Push-Pull

Lecture 158 SMED

Lecture 159 Poka-Yoke

Section 21: Improve Phase : Failure Modes & Effects Analysis (FMEA)

Lecture 160 Overview to Risk Management

Lecture 161 Introduction to FMEA

Lecture 162 Completing a FMEA

Lecture 163 Prioritizing Risks from FMEA to move towards actions

Lecture 164 Application of FMEA

Lecture 165 Appreciation of Design FMEA

Lecture 166 Activity: Complete FMEA for Clear Calls Case Study

Section 22: Control Phase : Statistical Process Control

Lecture 167 History of Statistical Process Control

Lecture 168 Theory of Control Charts

Lecture 169 Selection of Control Charts

Lecture 170 Continuous Control Charts

Lecture 171 Discrete Control Charts

Lecture 172 Application of Control Charts

Lecture 173 Plotting Control Charts in Python

Lecture 174 Activity: SPC and 2t Test for Pre-Post Improvement Validation

Section 23: Control Phase : Control Plan

Lecture 175 Control Plan - Sustaining Benefits

Lecture 176 Project Closure

Section 24: Lean Six Sigma Green Belt Certification - Next Steps

Lecture 177 Clear Calls Python Data Analysis Source Files

Lecture 178 Bonus Lecture: Optional Info - Lean Six Sigma Green Belt Certification

Lecture 179 Bonus Lecture: List of our other courses

Operation Managers,Customer Service Managers,IT Professionals,Python Programmers who wish to learn Lean Six Sigma