Data Analysis in Python for Lean Six Sigma Professionals

Data Analysis in Python for Lean Six Sigma Professionals, available at $59.99, has an average rating of 4.7, with 31 lectures, based on 60 reviews, and has 292 subscribers.

You will learn about 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 This course is ideal for individuals who are Lean Six Sigma Professionals It is particularly useful for Lean Six Sigma Professionals.

Enroll now: Data Analysis in Python for Lean Six Sigma Professionals

Summary

Title: Data Analysis in Python for Lean Six Sigma Professionals

Price: $59.99

Average Rating: 4.7

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Lean Six Sigma Professionals

Target Audiences

Lean Six Sigma Professionals

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 SPSS
Data Science is a skill in demand and you have an added advantage due to your prior Six Sigma Data Analysis proficiency
But without able to perform all the analysis in Python, you at Big Dis-advantage

What you will Get in this Course?

Step-by-Step Procedure starting with Python installation to perform all the below Six Sigma Data Analysis
No Programing Experience Needed
Learn data manipulation prior to analysis
Exposure to various Python Packages mentioned below
Download all Python Source Files
One End to End Six Sigma Analysis Case Study

Course Curriculum

Six Sigma Tools Covered using Python

Data Manipulation in Python
Descriptive Statistics
Histogram, Distribution Curve, Confidence levels
Boxplot
Stem & Leaf Plot
Scatter Plot
Heat Map
Pearson’s Correlation
Multiple Linear Regression
ANOVA
T-tests – 1t, 2t and Paired t
Proportions Test – 1P, 2P
Chi-square Test
SPC (Control Charts – mR, XbarR, XbarS, NP, P, C, U charts)

Python Packages

Numpy
Pandas
Matplotlib
Seaborn
Statsmodels
Scipy
PySPC
Stemgraphic

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction

Lecture 2: Six Sigma Data Analysis covered in Python in this Course

Lecture 3: Introduction to Python

Chapter 2: 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 3: Business Statistics

Lecture 1: Descriptive Statistics in Python

Lecture 2: Plotting Histogram in Python

Lecture 3: Computing Confidence Interval in Python

Lecture 4: Normality Tests in Python

Chapter 4: Graphical Analysis Methods

Lecture 1: Creating Box Plots in Python

Lecture 2: Stem & Leaf Plots in Python

Chapter 5: Assessing Process Capability

Lecture 1: Performing Process Capability in Python

Chapter 6: Performing Hypothesis Tests

Lecture 1: Perform 1 t Test in Python

Lecture 2: Perform 2 t Test in Python

Lecture 3: Perform Paired t Test in Python

Lecture 4: Perform ANOVA in Python

Lecture 5: Perform Chi-square test in Python

Lecture 6: Perform 1P Test in Python

Lecture 7: Perform 2P Test in Python

Lecture 8: Creating Scatter Diagram in Python

Lecture 9: Computing Correlation Coefficient in Python

Lecture 10: Regression in Python

Chapter 7: Statistical Process Control

Lecture 1: Plotting Control Charts in Python

Chapter 8: Clear Calls Case Study

Lecture 1: ClearCalls Case Study Overview & Python Data Analysis Source Files with solution

Lecture 2: Bonus Lecture: Details of our other courses

Instructors

Nilakantasrinivasan Janakiraman
Customer Experience & Business Transformation Leader

Rating Distribution

1 stars: 0 votes
2 stars: 3 votes
3 stars: 10 votes
4 stars: 25 votes
5 stars: 22 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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