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Inez Zwetsloot
Welcome to this course on Data Analytics for Lean Six Sigma.
Read more
Welcome to this course on Data Analytics for Lean Six Sigma.
Read more
Welcome to this course on Data Analytics for Lean Six Sigma.
In this course you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this course you are able to analyse and interpret data gathered within such a project. You will be able to use Minitab to analyse the data. I will also briefly explain what Lean Six Sigma is.
I will emphasize on use of data analytics tools and the interpretation of the outcome. I will use many different examples from actual Lean Six Sigma projects to illustrate all tools. I will not discuss any mathematical background.
The setting we chose for our data example is a Lean Six Sigma improvement project. However data analytics tools are very widely applicable. So you will find that you will learn techniques that you can use in a broader setting apart from improvement projects.
I hope that you enjoy this course and good luck!
Dr. Inez Zwetsloot & the IBIS UvA team
Register for this course and see more details by visiting:
OpenCourser.com/course/nl9iy3/data
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What's inside
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!
Read more
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!
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores data analytics techniques used in Lean Six Sigma improvement projects, making it relevant to professionals in quality management
Emphasizes the interpretation of data analytics outcomes, providing learners with valuable analytical skills
Introduces Minitab software for data analysis, familiarizing learners with industry-standard tools
Suitable for learners with no prior mathematical background in data analytics
Provides numerous examples from real-world Lean Six Sigma projects, making the learning experience practical
Save this course
Save Data Analytics for Lean Six Sigma to your list so you can find it easily later:
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Reviews summary
Lean six sigma data analysis fundamentals
Based on over 590 five-star reviews, learners say this course teaches foundational statistical concepts used in Lean Six Sigma to analyze and improve processes. Learners particularly appreciate the practical examples and use of Minitab software, which they say provides hands-on experience and clarifies complex concepts. Many note that the instructor's clear explanations and engaging teaching style make the material easy to understand, even for beginners. While some mention that it assumes some background in statistics, most find it accessible and well-structured.
Reviewers consistently praise the instructor's clear and engaging teaching style, which they say makes complex statistical concepts easy to grasp.
"The practice quizzes and videos were very helpful."
"The instructor is very clear in the explanations and in the practical part the software Minitab is very amigable and potente."
"The faculty is having excellent knowledge."
This course is highly practical, with many reviewers mentioning that the exercises and quizzes using Minitab software were helpful in reinforcing their understanding.
"The quizzes are interesting and help the students to further understand what they learned."
"I feel more confident about the data analytics and basics of lean six sigma now."
A few reviewers express a desire for more advanced content or a capstone project to apply the concepts they've learned.
"I would recommend adding more descriptions on how to do this with R."
"This could be nice to have some extra documentation to better understand the mathematics behind all this."
There are mixed opinions on whether the course assumes too much prior knowledge in statistics. Some reviewers say it's accessible for beginners, while others suggest it might be more suitable for those with some background in the subject.
"The course is very interesting and the insights you get, though after may seem as common sense, are usefull tools that most of the people may use in many fields, BUT the use of minitab is terrible, the software costs and may not always be available."
"Would have liked to see more active learning, such as pauses in videos to test whether I as a student really understood material at a deep level."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Analytics for Lean Six Sigma with these
activities:
Follow Data Analytics Tutorials
Show steps
Follow guided data analytics tutorials to reinforce your learning and develop practical skills.
Browse courses on
Data Analytics
Show steps
-
Find online tutorials on data analytics topics.
-
Work through the tutorials step-by-step.
-
Apply the techniques learned in the tutorials to real-world data.
Organize Study Groups
Show steps
Organize study groups with your classmates to discuss course concepts, work on problems together, and quiz each other.
Show steps
-
Find classmates who are interested in forming a study group.
-
Set up regular meeting times and locations.
-
Create a study plan and divide responsibilities.
-
Meet regularly to discuss course material, ask questions, and solve problems.
Volunteer in Data Analytics Projects
Show steps
Volunteer your time to work on data analytics projects for non-profit organizations or community groups.
Show steps
-
Identify non-profit organizations or community groups that are seeking volunteers with data analytics skills.
-
Contact the organizations and inquire about volunteer opportunities.
-
Participate in data analytics projects and contribute your skills.
-
Gain practical experience and make a positive impact on the community.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Data Analytics Drills
Show steps
Practice data analytics drills to solidify your understanding of the techniques covered in the course.
Browse courses on
Data Analytics
Show steps
-
Solve data analytics problems from textbooks or online resources.
-
Participate in online data analytics challenges or competitions.
Data Analytics Tool Kit
Show steps
Create a compilation of useful data analytics tools, resources, and tutorials.
Show steps
-
Research different data analytics tools and resources.
-
Identify the most useful tools and resources for your needs.
-
Create a document or website that compiles the tools and resources.
-
Share your compilation with other students or professionals.
Attend Data Analytics Workshops
Show steps
Attend data analytics workshops and conferences to learn from experts and network with professionals in the field.
Show steps
-
Research upcoming data analytics workshops and conferences.
-
Identify workshops that align with your interests and learning goals.
-
Register for the workshops and attend.
-
Take notes, ask questions, and engage with the speakers.
-
Follow up with the speakers and other attendees to expand your network.
Data Analytics Report
Show steps
Create a data analytics report that applies the techniques learned in the course to a real-world problem.
Show steps
-
Identify a problem or question that can be addressed using data analytics.
-
Gather and clean the necessary data.
-
Analyze the data using appropriate statistical methods.
-
Interpret the results of the analysis and draw conclusions.
-
Write a report that summarizes your findings and recommendations.
Contribute to Data Analytics Projects
Show steps
Contribute to open-source data analytics projects to gain experience and build your portfolio.
Show steps
-
Find open-source data analytics projects on platforms like GitHub.
-
Identify ways to contribute to the projects.
-
Submit your contributions to the project.
-
Collaborate with other contributors and learn from their experiences.
Mentor Junior Data Analysts
Show steps
Mentor junior data analysts to share your knowledge and expertise, while also reinforcing your own understanding of the subject.
Show steps
-
Identify junior data analysts who are seeking mentors.
-
Offer your mentorship and provide guidance on data analytics concepts and techniques.
-
Review their work, answer their questions, and provide feedback.
-
Share your experiences and insights to help them grow in their careers.
Follow Data Analytics Tutorials
Show steps
Follow guided data analytics tutorials to reinforce your learning and develop practical skills.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Find online tutorials on data analytics topics.
- Work through the tutorials step-by-step.
- Apply the techniques learned in the tutorials to real-world data.
Organize Study Groups
Show steps
Organize study groups with your classmates to discuss course concepts, work on problems together, and quiz each other.
Show steps
Show steps
Show steps
- Find classmates who are interested in forming a study group.
- Set up regular meeting times and locations.
- Create a study plan and divide responsibilities.
- Meet regularly to discuss course material, ask questions, and solve problems.
Volunteer in Data Analytics Projects
Show steps
Volunteer your time to work on data analytics projects for non-profit organizations or community groups.
Show steps
Show steps
Show steps
- Identify non-profit organizations or community groups that are seeking volunteers with data analytics skills.
- Contact the organizations and inquire about volunteer opportunities.
- Participate in data analytics projects and contribute your skills.
- Gain practical experience and make a positive impact on the community.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Data Analytics Drills
Show steps
Practice data analytics drills to solidify your understanding of the techniques covered in the course.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Solve data analytics problems from textbooks or online resources.
- Participate in online data analytics challenges or competitions.
Data Analytics Tool Kit
Show steps
Create a compilation of useful data analytics tools, resources, and tutorials.
Show steps
Show steps
Show steps
- Research different data analytics tools and resources.
- Identify the most useful tools and resources for your needs.
- Create a document or website that compiles the tools and resources.
- Share your compilation with other students or professionals.
Attend Data Analytics Workshops
Show steps
Attend data analytics workshops and conferences to learn from experts and network with professionals in the field.
Show steps
Show steps
Show steps
- Research upcoming data analytics workshops and conferences.
- Identify workshops that align with your interests and learning goals.
- Register for the workshops and attend.
- Take notes, ask questions, and engage with the speakers.
- Follow up with the speakers and other attendees to expand your network.
Data Analytics Report
Show steps
Create a data analytics report that applies the techniques learned in the course to a real-world problem.
Show steps
Show steps
Show steps
- Identify a problem or question that can be addressed using data analytics.
- Gather and clean the necessary data.
- Analyze the data using appropriate statistical methods.
- Interpret the results of the analysis and draw conclusions.
- Write a report that summarizes your findings and recommendations.
Contribute to Data Analytics Projects
Show steps
Contribute to open-source data analytics projects to gain experience and build your portfolio.
Show steps
Show steps
Show steps
- Find open-source data analytics projects on platforms like GitHub.
- Identify ways to contribute to the projects.
- Submit your contributions to the project.
- Collaborate with other contributors and learn from their experiences.
Mentor Junior Data Analysts
Show steps
Mentor junior data analysts to share your knowledge and expertise, while also reinforcing your own understanding of the subject.
Show steps
Show steps
Show steps
- Identify junior data analysts who are seeking mentors.
- Offer your mentorship and provide guidance on data analytics concepts and techniques.
- Review their work, answer their questions, and provide feedback.
- Share your experiences and insights to help them grow in their careers.
Career center
Learners who complete Data Analytics for Lean Six Sigma will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists plan, design, and conduct experiments to extract valuable insights from data, typically by using statistical analysis and modeling. Taking the Data Analytics for Lean Six Sigma course can help you build a foundation in data analysis techniques that are commonly used in various industries. Gaining proficiency in these techniques can enhance your ability to analyze and interpret data, which is a critical skill for Data Scientists as they collaborate with other professionals to solve complex business problems and make informed decisions.
Business Analyst
Business Analyst
Business Analysts use data analysis to identify areas for improvement, solve problems, and optimize business processes. The Data Analytics for Lean Six Sigma course aligns well with the skills and knowledge required for Business Analysts. By learning data analytics techniques and their application in real-world improvement projects, individuals can enhance their ability to analyze data, identify trends, and provide valuable insights to businesses.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to help organizations make informed decisions and solve problems. The Data Analytics for Lean Six Sigma course can be a valuable resource for Statisticians, providing them with additional knowledge and skills in data visualization, probability distributions, and statistical testing. These techniques are fundamental to Statistical work and can help Statisticians effectively communicate data-driven insights to stakeholders.
Market Researcher
Market Researcher
Market Researchers conduct research studies to gather and analyze data about consumer behavior, market trends, and industry dynamics. Taking the Data Analytics for Lean Six Sigma course can be beneficial for Market Researchers as it provides a foundational understanding of data analysis techniques. This knowledge can help Market Researchers effectively design and execute research studies, analyze data, and draw meaningful conclusions to inform marketing strategies and decision-making.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in various industries, such as manufacturing, logistics, and healthcare. The Data Analytics for Lean Six Sigma course complements the skills of Operations Research Analysts by providing them with practical knowledge in data analysis, probability distributions, and statistical testing. These techniques are essential for understanding and modeling business processes, making the course relevant to Operations Research Analysts seeking to advance their careers.
Process Improvement Specialist
Process Improvement Specialist
Process Improvement Specialists analyze and improve business processes to increase efficiency and effectiveness. The Data Analytics for Lean Six Sigma course is highly relevant for Process Improvement Specialists, providing them with valuable knowledge in data analysis and statistical techniques. By learning how to analyze data, identify bottlenecks, and develop improvement plans, Process Improvement Specialists can enhance their ability to optimize processes and achieve quantifiable results.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify patterns, trends, and insights that help businesses make informed decisions. The Data Analytics for Lean Six Sigma course can be a valuable resource for Data Analysts, providing them with additional tools and techniques for data analysis. The course's focus on practical applications in real-world projects can help Data Analysts gain hands-on experience and enhance their problem-solving abilities in a business setting.
Quality Engineer
Quality Engineer
Quality Engineers apply engineering principles and techniques to improve the quality of products and processes. By taking the Data Analytics for Lean Six Sigma course, Quality Engineers can enhance their understanding of data analysis and statistical methods. This knowledge is crucial for analyzing data, identifying quality issues, and developing solutions to improve product and process performance. The course's emphasis on Lean Six Sigma principles aligns well with the quality improvement goals of Quality Engineers.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks in various contexts, including finance, insurance, and healthcare. The Data Analytics for Lean Six Sigma course can be beneficial for Risk Analysts, providing them with additional skills in data analysis and statistical modeling. By learning how to analyze data and quantify risks, Risk Analysts can enhance their ability to develop and implement risk mitigation strategies and make informed decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems and applications. While the Data Analytics for Lean Six Sigma course may not be directly relevant to the core responsibilities of Software Engineers, it can provide valuable foundational knowledge in data analysis and statistical techniques. This knowledge can be beneficial for Software Engineers who wish to work on data-intensive projects or contribute to projects involving data-driven decision-making.
Systems Analyst
Systems Analyst
Systems Analysts design, implement, and maintain computer systems and applications. While the Data Analytics for Lean Six Sigma course may not directly align with the core responsibilities of Systems Analysts, it can provide valuable foundational knowledge in data analysis and statistical techniques. This knowledge can be beneficial for Systems Analysts who wish to expand their skillset and contribute to projects involving data-driven decision-making.
Financial Analyst
Financial Analyst
Financial Analysts analyze and interpret financial data to make recommendations on investments and financial decisions. While the Data Analytics for Lean Six Sigma course may not directly align with the core responsibilities of Financial Analysts, it can provide valuable foundational knowledge in data analysis and statistical techniques. This knowledge can be beneficial for Financial Analysts who wish to expand their skillset and contribute to projects involving data-driven decision-making.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risks in various fields, such as insurance and finance. While the Data Analytics for Lean Six Sigma course may not directly align with the core responsibilities of Actuaries, it can provide valuable foundational knowledge in data analysis and statistical techniques. This knowledge can be beneficial for Actuaries who wish to expand their skillset and contribute to projects involving data-driven decision-making.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases to store and manage data. While the Data Analytics for Lean Six Sigma course may not directly align with the core responsibilities of Database Administrators, it can provide valuable foundational knowledge in data analysis and statistical techniques. This knowledge can be beneficial for Database Administrators who wish to contribute to projects involving data-driven decision-making.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure to store, process, and analyze large volumes of data. While the Data Analytics for Lean Six Sigma course may not directly align with the core responsibilities of Data Engineers, it can provide valuable foundational knowledge in data analysis and statistical techniques. This knowledge can be beneficial for Data Engineers who wish to contribute to projects involving data-driven decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/nl9iy3/data
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Analytics for Lean Six Sigma.
Provides a comprehensive overview of Lean Six Sigma principles and tools, including data analytics techniques. It valuable reference for practitioners and students alike.
Provides a comprehensive overview of data analytics techniques that are commonly used in Lean Six Sigma projects. It good resource for those who want to learn how to use data to improve their Lean Six Sigma projects.
Handy reference guide to Lean Six Sigma tools and techniques. It good resource for those who are already familiar with Lean Six Sigma and need a quick refresher on specific tools.
Provides a detailed overview of statistical methods that are used in Lean Six Sigma projects. It good resource for those who want to learn how to use statistics to improve their Lean Six Sigma projects.
Provides a comprehensive overview of Lean Six Sigma for small businesses. It good resource for those who want to learn how to use Lean Six Sigma to improve their small business.
Provides a comprehensive overview of Lean Six Sigma, including its history, principles, and tools. It good starting point for those who are new to Lean Six Sigma.
Save
Provides a practical guide to using data analytics to improve startup performance. While not specifically focused on Lean Six Sigma, it covers many of the same principles and tools.
Save
Provides a practical guide to using R for data science. It covers a wide range of topics, from data cleaning and manipulation to data visualization and machine learning.
Provides a practical guide to using Python for data analysis. It covers a wide range of topics, from data cleaning and manipulation to data visualization and machine learning.
Provides a practical guide to using Excel for data analytics. It covers a wide range of topics, from data cleaning and manipulation to data visualization and statistical analysis.
Provides a comprehensive overview of data mining for business intelligence. It covers a wide range of topics, from data collection and preparation to data mining algorithms and business applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nl9iy3/data
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Effort
5 weeks of study, 2-4 hours per week.
Level
Introductory
Via
Coursera
Institution
University of Amsterdam
Instructor
Inez Zwetsloot
Language
English
Transcripts
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Good to know
Explores data analytics techniques used in Lean Six Sigma improvement projects, making it relevant to professionals in quality management
Emphasizes the interpretation of data analytics outcomes, providing learners with valuable analytical skills
Introduces Minitab software for data analysis, familiarizing learners with industry-standard tools
Suitable for learners with no prior mathematical background in data analytics
Provides numerous examples from real-world Lean Six Sigma projects, making the learning experience practical
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