Data Collection and Root Cause Analysis from Coursera

The course will equip you with the competencies and essential skills required to excel in the American Society for Quality (ASQ) Certified Six Sigma Yellow Belt (CSSYB) exam and contribute to process improvement programs. This course focuses on various data collection tools and techniques to analyze data, identify the root causes of a problem, and explore the concepts of measurement system analysis (MSA), and hypothesis testing.

By the end of this course, you will be able to:

• Define data requirements to gather relevant data from the process using appropriate data collection methods.

• Calculate baseline process performance metrics based on the collected data.

• Analyze data for variations and use data analysis tools and techniques to identify the root causes for the problem or variation.

The course is best suited for entry-level professionals who are new to the world of Six Sigma and wish to improve their professional experience and opportunities. For this course, no prior knowledge is required, however, it is recommended that you complete the first course, Introduction to Lean Six Sigma and Project Identification Methods in the ASQ-Certified Six Sigma Yellow Belt Exam Prep Specialization.

What's inside

Syllabus

Basic Statistics and Process Performance Measurement

This module introduces you to descriptive statistics, a branch of statistics that involves summarizing and describing the main features of a dataset. It provides tools and techniques to organize, present, and analyze data to gain insights into its central tendencies, variability, and distribution. Descriptive statistics is fundamental in data analysis and is a basis for more advanced statistical methods. You will also be introduced to inferential statistics. The module also explains the various data types and helps you differentiate between qualitative and quantitative data and data coming from internal and external sources. It describes the data collection process. Further, the module delves into the concept of measurement system analysis (MSA) and its components to understand the variations in the measurement process.

Root Cause Analysis Techniques

This module explains the differences between value-added and non-value-added activities. It makes a case for non-value-added activities that are necessary to enable the smooth running of the organization. The module also discusses how to identify the bottlenecks in a system and suggests ways to eliminate them. Lastly, the module explores the various techniques to conduct a root cause analysis (RCA) for the identified problem in your process or organization. The first one, Pareto analysis, is based on the Pareto principle, which states that approximately 80% of the effects come from 20% of the causes. This analysis helps prioritize potential root causes based on their relative impact. You will also learn how to use the fishbone diagram, also known as the Ishikawa or cause and effect diagram, which visually represents the potential causes contributing to a problem while categorizing the possible causes into specific groups to facilitate the identification of root causes. Additionally, you will learn about the five whys, a simple yet powerful technique involving repeatedly asking “why” to identify the root cause of a problem. It helps to peel the layers of symptoms and surface-level causes to get to the core issue.

Hypothesis Testing and Investigating the Relationship

This module provides a comprehensive overview of hypothesis testing, an essential statistical tool used to assess the validity of claims or hypotheses about populations. You will learn about the hypothesis testing process, its application in real-world scenarios, and how to interpret the hypothesis test results to make better decisions. The module will take you through the different types of hypotheses, types of errors, and the significance of the p-value in hypothesis testing. You will also learn about the principles and applications of correlation and regression techniques. The module discusses the types of correlation and the roles of dependent and independent variables in regression analysis. lt explains the implications of R-squared values in regression analysis. The module also explains simple linear regression and the difference between deterministic and probabilistic models.

Peer-Reviewed Assignment

This is a peer-review assignment based on the concepts taught in the Data Collection and Root Cause Analysis course. In this assignment, you will apply your knowledge of hypothesis testing to a real-life scenario.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Prepares learners for the American Society for Quality (ASQ) Certified Six Sigma Yellow Belt (CSSYB) exam, which can boost their career prospects

Suitable for entry-level professionals seeking to enter the field of Six Sigma and enhance their professional experience

Provides practical knowledge and skills in data collection, analysis, and root cause identification, equipping learners to contribute to process improvement programs

Covers essential topics such as basic statistics, measurement system analysis, hypothesis testing, and root cause analysis techniques, providing a solid foundation for Six Sigma professionals

Facilitates collaboration and peer learning through a peer-reviewed assignment, allowing learners to engage with their peers and receive feedback

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 Collection and Root Cause Analysis with these activities:

The Lean Six Sigma Pocket Toolbook

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Review this essential reference for Six Sigma tools and techniques to supplement your course learning.

View Lean Six Sigma - An Introduction and Toolkit... on Amazon

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Read the book and take notes on key concepts.
Refer to the book during your course studies for additional insights.
Use the book as a reference for future Six Sigma projects and applications.

Basic Statistics and Process Performance Measurement

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Review the basics of statistics and process performance measurement to prepare for the course.

Browse courses on Basic Statistics

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Study the course syllabus and textbook readings.
Review online resources on basic statistics and process performance measurement.
Complete practice problems and exercises.

Connect with Six Sigma Professionals

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Seek guidance and mentorship from experienced Six Sigma professionals to enhance your understanding.

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Identify potential mentors within your network or through professional organizations.
Reach out to mentors and request their guidance.
Attend industry events to connect with Six Sigma professionals.

Nine other activities

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Peer review of data collection methods

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Peer review provides valuable feedback on data collection methods, ensuring accuracy and reliability of data.

Browse courses on Data Collection

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Present data collection methods to peers
Receive and analyze peer feedback
Refine data collection methods based on feedback

Discussion Forum Participation

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Engage with peers in the course discussion forum to clarify concepts and share insights.

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Read the assigned readings and participate in online discussions.
Ask questions and respond to others' questions.
Contribute to group projects or assignments.

Blog Post on Data Analysis Applications

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Create a blog post that demonstrates your understanding and application of data analysis techniques within the context of Six Sigma.

Browse courses on Data Analysis

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Identify a specific topic related to data analysis and Six Sigma.
Research the topic and gather relevant data.
Analyze the data using appropriate statistical techniques.
Write a blog post that presents your findings and insights.

Practice root cause analysis using fishbone diagram

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Practicing root cause analysis helps identify the underlying causes of problems and develop effective solutions.

Browse courses on Root Cause Analysis

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Identify the problem to be analyzed
Draw the fishbone diagram
Brainstorm potential root causes

Solve hypothesis testing problems

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Solving hypothesis testing problems helps develop critical thinking and decision-making skills in data analysis.

Browse courses on Hypothesis Testing

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State the null and alternative hypotheses
Calculate the test statistic and p-value
Make a decision and interpret the results

MSA and Hypothesis Testing Exercises

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Complete exercises and practice drills to reinforce the concepts of MSA and hypothesis testing.

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Work through practice problems on MSA and hypothesis testing.
Use statistical software to conduct hypothesis tests.
Interpret the results of hypothesis tests and draw conclusions.

Six Sigma Certification Workshop

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Attend a Six Sigma certification workshop to gain hands-on experience and prepare for certification.

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Identify and register for a Six Sigma certification workshop.
Attend the workshop and actively participate in discussions and exercises.
Review the workshop materials and practice the techniques taught.

Capture project details using a project charter

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Creating a project charter helps define the scope, objectives, and deliverables of a project, ensuring that everyone is aligned.

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Define project scope and objectives
Identify project stakeholders
Outline project deliverables

Root Cause Analysis Project

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Apply root cause analysis techniques to identify and address real-world problems.

Browse courses on Root Cause Analysis

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Identify a problem or issue that needs to be solved.
Use root cause analysis tools and techniques to identify the underlying causes of the problem.
Develop and implement solutions to address the root causes.
Evaluate the effectiveness of the solutions and make adjustments as needed.

Career center

Learners who complete Data Collection and Root Cause Analysis will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, collect and analyze data, and communicate your findings effectively.

See salaries and explore the career path for Data Analyst

Quality Assurance Analyst

Quality Assurance Analysts are responsible for ensuring that products and services meet quality standards. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, conduct root cause analysis, and develop and implement quality control measures.

See salaries and explore the career path for Quality Assurance Analyst

Process Improvement Specialist

Process Improvement Specialists are responsible for improving the efficiency and effectiveness of business processes. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, collect and analyze data, and develop and implement process improvement plans.

See salaries and explore the career path for Process Improvement Specialist

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, collect and analyze data, and communicate your findings effectively.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists are responsible for using data to solve business problems. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, collect and analyze data, and develop and implement data-driven solutions.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts are responsible for analyzing business needs and developing solutions to improve business performance. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, collect and analyze data, and develop and implement business solutions.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts are responsible for using mathematical and analytical techniques to solve business problems. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, collect and analyze data, and develop and implement operations research models.

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Machine Learning Engineer

Machine Learning Engineers are responsible for developing and implementing machine learning models. This course will provide you with the skills and knowledge you need to succeed in this role, including how to identify and solve problems, collect and analyze data, and develop and implement machine learning algorithms.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. This course may be useful for Software Engineers who are interested in developing data-driven applications or who want to improve their problem-solving and analytical skills.

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Sales Manager

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Marketing Manager

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Financial Analyst

Financial Analysts are responsible for analyzing financial data and making recommendations to investors. This course may be useful for Financial Analysts who are interested in improving their problem-solving and analytical skills, or who want to learn more about data-driven financial analysis.

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Product Manager

Product Managers are responsible for managing the development and launch of new products. This course may be useful for Product Managers who are interested in improving their problem-solving and analytical skills, or who want to learn more about data-driven decision-making.

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Consultant

Consultants are responsible for providing advice and guidance to businesses on a variety of topics. This course may be useful for Consultants who are interested in improving their problem-solving and analytical skills, or who want to learn more about data-driven decision-making.

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Entrepreneur

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