Data Analysis In Python For Lean Six Sigma Professionals

What you'll learn
Learn Lean Six Sigma Data Analysis in Python
Get Step by Step Procedure for all Six Sigma Analysis is covered
No Programming Experience Needed. Course will start with Python installation
One Full Fledged Lean Six Sigma Case Study with Solutions
Download all Python Source Files for all the analysis
Requirements
Prior knowledge of the Six Sigma Analysis tools is needed.
If you don't have Green Belt Level proficiency, then register for "Lean Six Sigma Green Belt Course with Python" course instead
Description
Why you should consider this PYTHON course?As a Lean Six Sigma Professional, you are already aware how to perform Six Sigma Data Analysis & Discovery using Minitab, Excel, JMP or SPSSData Science is a skill in demand and you have an added advantage due to your prior Six Sigma Data Analysis proficiencyBut without able to perform all the analysis in Python, you at Big Dis-advantageWhat you will Get in this Course?Step-by-Step Procedure starting with Python installation to perform all the below Six Sigma Data AnalysisNo Programing Experience NeededLearn data manipulation prior to analysisExposure to various Python Packages mentioned belowDownload all Python Source FilesOne End to End Six Sigma Analysis Case StudyCourse CurriculumSix Sigma Tools Covered using PythonData Manipulation 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 PackagesNumpyPandasMatplotlibSeabornStatsmodelsScipyPySPCStemgraphic
Overview
Section 1: Welcome
Lecture 1 Introduction
Lecture 2 Six Sigma Data Analysis covered in Python in this Course
Lecture 3 Introduction to Python
Section 2: Getting started with Python
Lecture 4 Installing Python
Lecture 5 Getting Started with Jupyter I
Lecture 6 Getting Started with Jupyter II
Lecture 7 Data Types in Python
Lecture 8 Python Packages
Lecture 9 Numpy Basics
Lecture 10 Pandas Basics
Lecture 11 Data Clean up using Pandas
Section 3: Business Statistics
Lecture 12 Descriptive Statistics in Python
Lecture 13 Plotting Histogram in Python
Lecture 14 Computing Confidence Interval in Python
Lecture 15 Normality Tests in Python
Section 4: Graphical Analysis Methods
Lecture 16 Creating Box Plots in Python
Lecture 17 Stem & Leaf Plots in Python
Section 5: Assessing Process Capability
Lecture 18 Performing Process Capability in Python
Section 6: Performing Hypothesis Tests
Lecture 19 Perform 1 t Test in Python
Lecture 20 Perform 2 t Test in Python
Lecture 21 Perform Paired t Test in Python
Lecture 22 Perform ANOVA in Python
Lecture 23 Perform Chi-square test in Python
Lecture 24 Perform 1P Test in Python
Lecture 25 Perform 2P Test in Python
Lecture 26 Creating Scatter Diagram in Python
Lecture 27 Computing Correlation Coefficient in Python
Lecture 28 Regression in Python
Section 7: Statistical Process Control
Lecture 29 Plotting Control Charts in Python
Section 8: Clear Calls Case Study
Lecture 30 ClearCalls Case Study Overview & Python Data Analysis Source Files with solution
Lecture 31 Bonus Lecture: Details of our other courses
Lean Six Sigma Professionals


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