Data Analysis In Python For Lean Six Sigma Professionals

Perform Six Sigma Data Analysis using Python like Data Scientists - No Programming Exp Needed - Download Source Files

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