Data Analytics for Lean Six Sigma

Instructor: Inez Zwetsloot

Duration: 11.00

This course introduces students to the fundamentals of data analytics and how it can be used to improve Lean Six Sigma processes. Students will learn how to use data to identify areas of improvement and drive successful outcomes. ▼▲

Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews

Go to class

Course Feature

Cost:

Free

Provider:

Coursera

Certificate:

Paid Certification

Language:

English

Start Date:

10th Jul, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course, Data Analytics for Lean Six Sigma, provides an introduction to data analytics techniques that are typically useful within Lean Six Sigma improvement projects. Participants will learn how to analyse and interpret data gathered within such a project, using Minitab. The course will also provide an overview of Lean Six Sigma and its applications.

Throughout the course, emphasis will be placed on the use of data analytics tools and the interpretation of the outcome. Examples from actual Lean Six Sigma projects will be used to illustrate the tools. No mathematical background will be discussed.

The setting chosen for the data example is a Lean Six Sigma improvement project, however the data analytics tools are applicable in a broader setting.

Dr. Inez Zwetsloot and the IBIS UvA team wish participants the best of luck in this course.

[Applications]
Upon completion of this course, participants are able to apply the data analytics techniques they have learned to analyse and interpret data gathered within Lean Six Sigma improvement projects. They are also able to use Minitab to analyse the data. Furthermore, participants are able to apply the data analytics techniques they have learned in a broader setting apart from improvement projects.

[Career Paths]
1. Data Analyst: Data Analysts are responsible for collecting, organizing, and analyzing data to help inform business decisions. They use a variety of tools and techniques to identify trends and patterns in data sets, and then present their findings to stakeholders. Data Analysts are in high demand as businesses increasingly rely on data-driven decision making.

2. Business Process Analyst: Business Process Analysts are responsible for analyzing and improving business processes. They use Lean Six Sigma and other process improvement techniques to identify areas of improvement, develop solutions, and implement changes. Business Process Analysts are essential for organizations looking to streamline their operations and increase efficiency.

3. Quality Assurance Manager: Quality Assurance Managers are responsible for ensuring that products and services meet quality standards. They use Lean Six Sigma and other quality management techniques to identify areas of improvement, develop solutions, and implement changes. Quality Assurance Managers are essential for organizations looking to maintain high standards of quality.

4. Data Scientist: Data Scientists are responsible for collecting, organizing, and analyzing large amounts of data to identify trends and patterns. They use a variety of tools and techniques to uncover insights from data sets, and then present their findings to stakeholders. Data Scientists are in high demand as businesses increasingly rely on data-driven decision making.

[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the development of skills in data analysis, data visualization, and machine learning. It also covers topics such as statistics, programming, and database management. This degree path is becoming increasingly popular as businesses and organizations are recognizing the value of data-driven decision making.

2. Master of Science in Business Analytics: This degree path focuses on the application of data analytics to business problems. It covers topics such as predictive analytics, data mining, and optimization. This degree path is ideal for those who want to use data to make informed decisions in the business world.

3. Master of Science in Artificial Intelligence: This degree path focuses on the development of skills in artificial intelligence and machine learning. It covers topics such as natural language processing, computer vision, and robotics. This degree path is ideal for those who want to use data to create intelligent systems that can make decisions autonomously.

4. Doctor of Philosophy in Data Science: This degree path focuses on the development of advanced skills in data analysis, data visualization, and machine learning. It also covers topics such as statistics, programming, and database management. This degree path is ideal for those who want to pursue a career in research and academia.

Course Syllabus

Data and Lean Six Sigma

This module introduces Lean Six Sigma and shows you where data and data analytics have their place within the DMAIC framework. It also introduces the software package Minitab. This package is used throughout the videos for data analytics. It is not mandatory to use this package. I just really like it!

Understanding and visualizing data

This module explains how to visualize data. It discusses visualizing single variables as well as visualizing two variables. You will learn to select the appropriate graph. For this it is essential to first learn the distinction between numerical and categorical data.

Using probability distributions

In this module on using probability distributions, you will learn how to quantify uncertainty. Furthermore you will learn to answer an important business question: “what percentage of products or cases meet our specifications?".

Introduction to testing

You will learn to model your CTQ and influence factor(s) and to use a decision tree to select the appropriate tool for data based testing of this model. Furthermore, causality is introduced.

Testing: numerical Y and categorical X

In this module on statistical testing, you will learn how to establish relationship between a numerical Y variable (the CTQ) and categorical influence factors (the X variables).

Testing: numerical Y and numerical Y

What is the relation between the length of stay and the age of a patient? In this module you will learn to answers these types of questions using statistical tests to relate a numerical CTQ (the Y variable) to a numerical influence factor (the X variable).

Testing: categorical Y

Finally you will learn how to test a relationship between a Y and a X variable whenever your Y variable (the CTQ) is a categorical variable.

Pros & Cons

Pros Cons

Amazing course contents and delivery
Structurally explained each step
Highly practical and perfect for professionals
Relevant industry examples
Well presented by the video professor
Perfect for helping students understand

Not enough content
No zip file of lecture videos and assignments
Expensive Minitab license fee
No version of course taught in Microsoft Excel

Course Provider

Provider Coursera's Stats at AZClass

In this course, you will learn data analysis techniques that are often useful in Lean Six Sigma improvement projects. You will be able to use Minitab to analyze the data and interpret the results. You'll also learn what Lean Six Sigma is and how it can be applied to data analysis. In addition, you'll learn how to apply data analysis tools in wider contexts beyond improvement projects. With examples from real Lean Six Sigma projects, you will be able to better understand these tools and techniques. You will have the knowledge and skills to analyze and interpret data collected in a Lean Six Sigma project. So, join them and start your journey to become a data analysis expert!

Rating Grade: B This is a trending provider perfect for gaining traction and maybe a good option for users who are looking for a reliable source of learning content.