2 Course Bundle: Quality Analytics

2 Course Bundle: Quality Analytics, available at $74.99, has an average rating of 4.61, with 99 lectures, based on 9 reviews, and has 87 subscribers.

You will learn about Fundamental Quality Management Principles Basic and intermediate Data Analytics Excel functions and applications for quality management and data analytics Create and use control charts to monitor and improve business operations How to create and use Pivot Tables for root cause analysis and comparisons Create and use Monte Carlo Simulation to understand and predict operational risk Statistical Process Control Conduct a Capability Analysis to understand quality performance and limitations This course is ideal for individuals who are Quality Engineers or Industrial Engineers or Quality Managers or Plant Managers or Operations Managers or Process Engineers or Lean Six Sigma Green Belts or Lean Six Sigma Black Belts or Lean Six Sigma Yellow Belts or Manufacturing and Production Managers It is particularly useful for Quality Engineers or Industrial Engineers or Quality Managers or Plant Managers or Operations Managers or Process Engineers or Lean Six Sigma Green Belts or Lean Six Sigma Black Belts or Lean Six Sigma Yellow Belts or Manufacturing and Production Managers.

Enroll now: 2 Course Bundle: Quality Analytics

Summary

Title: 2 Course Bundle: Quality Analytics

Price: $74.99

Average Rating: 4.61

Number of Lectures: 99

Number of Published Lectures: 99

Number of Curriculum Items: 99

Number of Published Curriculum Objects: 99

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Fundamental Quality Management Principles
Basic and intermediate Data Analytics
Excel functions and applications for quality management and data analytics
Create and use control charts to monitor and improve business operations
How to create and use Pivot Tables for root cause analysis and comparisons
Create and use Monte Carlo Simulation to understand and predict operational risk
Statistical Process Control
Conduct a Capability Analysis to understand quality performance and limitations

Who Should Attend

Quality Engineers
Industrial Engineers
Quality Managers
Plant Managers
Operations Managers
Process Engineers
Lean Six Sigma Green Belts
Lean Six Sigma Black Belts
Lean Six Sigma Yellow Belts
Manufacturing and Production Managers

Target Audiences

Quality Engineers
Industrial Engineers
Quality Managers
Plant Managers
Operations Managers
Process Engineers
Lean Six Sigma Green Belts
Lean Six Sigma Black Belts
Lean Six Sigma Yellow Belts
Manufacturing and Production Managers

This 2 course bundle includes the best of both worlds as two of the most popular courses in my catalog are combined to bring novel and exciting value to students of quality management. First, students will be introduced to Excel through the data analytics course. Here they will become masters of the Excel environment. Students will learn everything from the basics of excel navigation and worksheet functions, to the more meaningful and exploratory applications of excel such as data visualizations (Bar charts, line charts, combo charts and more), pivot tables for root cause analysis, category analysis and comparative analysis,, statistics including hypothesis testing, monte Carlo simulation and much more. Then, after the mastery of excel, students will take on exciting topics of quality analytics. Students of this course will apply the data analytics curriculum to real world applications of quality management for business operations. Topics within this module include understanding what statistical process control is and how it is used to improve and predict business performance. Topics within this module include control charting in excel (with and without data visualizations) and capability analysis which allows businesses to understand their their ability to meet and exceed their customer tolerances and expectations.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Navigation in Excel Part 1

Lecture 2: Navigation in Excel Part 2

Lecture 3: Basic Excel Formulas

Lecture 4: Data Structures

Lecture 5: Intermediate Excel Functions

Lecture 6: Descriptive Statistics Part 1

Lecture 7: Descriptive Statistics Part 2

Lecture 8: Section #1 Practice & Solutions

Chapter 2: Visualizations

Lecture 1: Introduction to Visualizations and Pie Charts

Lecture 2: Histograms

Lecture 3: Bar Charts

Lecture 4: Line Charts

Lecture 5: Box and Whisker

Lecture 6: Radial Charts

Lecture 7: Combo Charts

Lecture 8: Scatter Plots

Lecture 9: Conditional Formatting Part 1

Lecture 10: Conditional Formatting Part 2

Lecture 11: Conditional Formatting Part 3

Lecture 12: Sparklines

Lecture 13: Appendix: Control Charts

Lecture 14: Section 2 Practice & Solutions Part 1

Lecture 15: Section 2 Practice & Solutions Part 2

Chapter 3: Pivot Tables, Charts and Slicers

Lecture 1: Introduction to Pivot Tables

Lecture 2: Root Cause Analysis

Lecture 3: Comparative Analysis

Lecture 4: Pivot Charts and Slicers

Lecture 5: Section #3 Practice & Solutions Part 1

Lecture 6: Section #3 Practice & Solutions Part 2

Lecture 7: Section #3 Practice & Solutions Part 3

Chapter 4: Statistics: Hypothesis testing and Regression

Lecture 1: Types of Data

Lecture 2: Fundamentals of Sampling

Lecture 3: Distributions Part 1

Lecture 4: Distributions Part 2

Lecture 5: Introduction to Hypothesis Testing

Lecture 6: T-Tests Part 1

Lecture 7: T-Tests Part 2

Lecture 8: T-Tests Part 3

Lecture 9: Chi-Squared Test Part 1

Lecture 10: Chi-Squared Test Part 2

Lecture 11: Test for Normality Part 1

Lecture 12: Test for Normality Part 2

Lecture 13: ANOVA Part 1

Lecture 14: ANOVA Part 2

Lecture 15: Simple Regression Part 1

Lecture 16: Simple Regression Part 2

Lecture 17: Multi-Regression Part 1

Lecture 18: Multi-Regression Part 2

Lecture 19: Multi-Regression Part 3

Lecture 20: Section 4 Practice & Solutions Part 1

Lecture 21: Section 4 Practice & Solutions Part 2

Chapter 5: Forecasting

Lecture 1: Introduction to Forecasting

Lecture 2: Factor Forecasting – Regression Part 1

Lecture 3: Factor Forecasting – Regression Part 2

Lecture 4: Factor Forecasting – Monte Carlo Simulation – Part 1

Lecture 5: Factor Forecasting – Monte Carlo Simulation – Part 2

Lecture 6: Factor Forecasting – Monte Carlo Simulation – Part 3

Lecture 7: Time Series Forecasting – Simple Moving Average Part 1

Lecture 8: Time Series Forecasting – Simple Moving Average Part 2

Lecture 9: Time Series Forecasting – Parameter Tuning Part 1

Lecture 10: Time Series Forecasting – Parameter Tuning Part 2

Lecture 11: Time Series Forecasting – Parameter Tuning Part 3

Lecture 12: Time Series Forecasting – Auto Regression

Lecture 13: Time Series Forecasting – Time Series ARMA

Lecture 14: Time Series Forecasting – Time Series ARIMA

Lecture 15: Section 5 Practice Set 1 & Solutions Part 1

Lecture 16: Section 5 Practice Set 1 & Solutions Part 2

Lecture 17: Practice Set 2 & Solutions Part 1

Lecture 18: Practice Set 2 & Solutions Part 2

Lecture 19: Practice Set 2 & Solutions Part 3

Chapter 6: Miscellaneous Analytical Tools

Lecture 1: Intro and Goal Seek

Lecture 2: Scenario Analysis

Lecture 3: Data Tables

Lecture 4: Introduction to Excel Solver Tool

Lecture 5: Excel Solver Tool – The Backpack Problem

Lecture 6: Excel Solver Tool – The Mixing Problem

Lecture 7: Excel Solver Tool – The Traveling Salesman Problem

Lecture 8: Section 6 Practice & Solutions Part 1

Lecture 9: Section 6 Practice & Solutions Part 2

Lecture 10: Section 6 Practice & Solutions Part 3

Chapter 7: Control Chart Fundamentals

Lecture 1: What are Control Charts?

Lecture 2: Parts of a Control Chart, Making a Control Chart in Excel

Lecture 3: Benefits of Control Charting

Lecture 4: Types of Data and Types of Control Charts

Lecture 5: Appendix: Offset Formula

Chapter 8: Signals

Lecture 1: Control Charting Assumptions

Lecture 2: Strong Signals

Lecture 3: Moderate Sustained Signals

Lecture 4: Weak Signals

Lecture 5: Appendix 8.1: Root Cause Analysis Part 1

Lecture 6: Appendix 8.2: Root Cause Analysis Part 2

Lecture 7: Appendix 8.3: Improving Stable Processes

Instructors

Michael Parent
Principal – Michael Parent Consulting

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4 stars: 5 votes
5 stars: 4 votes

Frequently Asked Questions

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