Become A Quality Analytics Expert Using Excel

Quality Analytics for Quality Management using Excel

What you'll 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

Requirements

Microsoft Excel

No prior data analysis experience needed for this course

No additional software is needed.

Some general knowledge about manufacturing and business operations preferred.

Description

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.

Overview

Section 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

Section 2: Visualizations

Lecture 9 Introduction to Visualizations and Pie Charts

Lecture 10 Histograms

Lecture 11 Bar Charts

Lecture 12 Line Charts

Lecture 13 Box and Whisker

Lecture 14 Radial Charts

Lecture 15 Combo Charts

Lecture 16 Scatter Plots

Lecture 17 Conditional Formatting Part 1

Lecture 18 Conditional Formatting Part 2

Lecture 19 Conditional Formatting Part 3

Lecture 20 Sparklines

Lecture 21 Appendix: Control Charts

Lecture 22 Section 2 Practice & Solutions Part 1

Lecture 23 Section 2 Practice & Solutions Part 2

Section 3: Pivot Tables, Charts and Slicers

Lecture 24 Introduction to Pivot Tables

Lecture 25 Root Cause Analysis

Lecture 26 Comparative Analysis

Lecture 27 Pivot Charts and Slicers

Lecture 28 Section #3 Practice & Solutions Part 1

Lecture 29 Section #3 Practice & Solutions Part 2

Lecture 30 Section #3 Practice & Solutions Part 3

Section 4: Statistics: Hypothesis testing and Regression

Lecture 31 Types of Data

Lecture 32 Fundamentals of Sampling

Lecture 33 Distributions Part 1

Lecture 34 Distributions Part 2

Lecture 35 Introduction to Hypothesis Testing

Lecture 36 T-Tests Part 1

Lecture 37 T-Tests Part 2

Lecture 38 T-Tests Part 3

Lecture 39 Chi-Squared Test Part 1

Lecture 40 Chi-Squared Test Part 2

Lecture 41 Test for Normality Part 1

Lecture 42 Test for Normality Part 2

Lecture 43 ANOVA Part 1

Lecture 44 ANOVA Part 2

Lecture 45 Simple Regression Part 1

Lecture 46 Simple Regression Part 2

Lecture 47 Multi-Regression Part 1

Lecture 48 Multi-Regression Part 2

Lecture 49 Multi-Regression Part 3

Lecture 50 Section 4 Practice & Solutions Part 1

Lecture 51 Section 4 Practice & Solutions Part 2

Section 5: Forecasting

Lecture 52 Introduction to Forecasting

Lecture 53 Factor Forecasting - Regression Part 1

Lecture 54 Factor Forecasting - Regression Part 2

Lecture 55 Factor Forecasting - Monte Carlo Simulation - Part 1

Lecture 56 Factor Forecasting - Monte Carlo Simulation - Part 2

Lecture 57 Factor Forecasting - Monte Carlo Simulation - Part 3

Lecture 58 Time Series Forecasting - Simple Moving Average Part 1

Lecture 59 Time Series Forecasting - Simple Moving Average Part 2

Lecture 60 Time Series Forecasting - Parameter Tuning Part 1

Lecture 61 Time Series Forecasting - Parameter Tuning Part 2

Lecture 62 Time Series Forecasting - Parameter Tuning Part 3

Lecture 63 Time Series Forecasting - Auto Regression

Lecture 64 Time Series Forecasting - Time Series ARMA

Lecture 65 Time Series Forecasting - Time Series ARIMA

Lecture 66 Section 5 Practice Set 1 & Solutions Part 1

Lecture 67 Section 5 Practice Set 1 & Solutions Part 2

Lecture 68 Practice Set 2 & Solutions Part 1

Lecture 69 Practice Set 2 & Solutions Part 2

Lecture 70 Practice Set 2 & Solutions Part 3

Section 6: Miscellaneous Analytical Tools

Lecture 71 Intro and Goal Seek

Lecture 72 Scenario Analysis

Lecture 73 Data Tables

Lecture 74 Introduction to Excel Solver Tool

Lecture 75 Excel Solver Tool - The Backpack Problem

Lecture 76 Excel Solver Tool - The Mixing Problem

Lecture 77 Excel Solver Tool - The Traveling Salesman Problem

Lecture 78 Section 6 Practice & Solutions Part 1

Lecture 79 Section 6 Practice & Solutions Part 2

Lecture 80 Section 6 Practice & Solutions Part 3

Section 7: Control Chart Fundamentals

Lecture 81 What are Control Charts?

Lecture 82 Parts of a Control Chart, Making a Control Chart in Excel

Lecture 83 Benefits of Control Charting

Lecture 84 Types of Data and Types of Control Charts

Lecture 85 Appendix: Offset Formula

Section 8: Signals

Lecture 86 Control Charting Assumptions

Lecture 87 Strong Signals

Lecture 88 Moderate Sustained Signals

Lecture 89 Weak Signals

Lecture 90 Appendix 8.1: Root Cause Analysis Part 1

Lecture 91 Appendix 8.2: Root Cause Analysis Part 2

Lecture 92 Appendix 8.3: Improving Stable Processes

Section 9: Control Charts with Categorical Data

Lecture 93 P-Charts

Lecture 94 U-Charts

Section 10: Control Charts Without Charting

Lecture 95 Control Charts without charts fundamentals

Lecture 96 Control Charts without charts - Signals continued

Lecture 97 Nested Logic Part 1

Lecture 98 Nested Logic Part 2

Lecture 99 Nested Logic Part 3

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