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Lean Six Sigma Green Belt Online Course With Python

17-01-2023, 04:06, posted by Baturi

Last updated 4/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.38 GB | Duration: 17h 39m
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

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