The course will teach you the competencies and essential skills required to pass the American Society for Quality (ASQ) Certified Six Sigma Green Belt (CSSGB) exam. This course focuses specifically on the last three phases of the define, measure, analyze, improve, and control (DMAIC) framework and will enable you to analyze the root causes of existing issues, implement solutions, and ensure the process's sustainability. You will gain insight into data analysis techniques such as hypothesis testing and regression analysis and be able to develop control plans for process improvement.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
By the end of the course, you will be able to:
- Describe the importance of analyzing data and identifying root causes of problems in the process.
- Apply data analysis techniques such as hypothesis testing, regression analysis, and correlation analysis to identify the issues in your process.
- Develop control plans and strategies for sustaining process improvements.
- Use control charts and statistical process control techniques to monitor process performance.
The course offers practical exposure with a peer-graded project that enables you to apply your knowledge in real-world scenarios. The course is for individuals who wish to analyze and solve quality problems and are working in process improvement teams or involved with Six Sigma, lean, or other quality improvement projects.
This course is part of the ASQ-Certified Six Sigma Green Belt (CSSGB) Exam Prep Specialization.
What you'll learn
- Describe the importance of analyzing data and identifying root causes of problems in the process.
- Apply data analysis techniques such as hypothesis testing, regression analysis, and correlation analysis to identify the issues in your process.
- Develop control plans and strategies for sustaining process improvements.
- Use control charts and statistical process control techniques to monitor process performance.
Syllabus
Analyze: Exploratory data analysis (EDA)
In this module, you will learn why exploratory data analysis (EDA) is a critical component of data analysis in various domains, including statistics, data science, and machine learning. It involves examining and visualizing the characteristics of a dataset to gain insights, discover patterns, and identify relationships between variables. It also helps understand the data, identify quality issues, and formulate hypotheses for further analysis. Additionally, you will learn the fundamentals of correlation and linear regression and how to analyze and extract meaningful insights from datasets. This helps to strengthen the foundation for subsequent data analysis and modeling tasks, enabling informed decision-making and hypothesis generation.
Analyze: Hypothesis testing
In this module, you will learn about hypothesis testing, which is an important component of statistical analysis and data-driven decision-making. You will also learn how to apply hypothesis-testing techniques to infer population parameters based on sample data. Additionally, you will formulate hypotheses, select appropriate tests, conduct hypothesis tests, interpret results, and effectively communicate the findings. Hypothesis testing provides a systematic framework for making data-driven decisions and drawing valid conclusions based on statistical evidence.
Improve: Design of experiments (DOE) and control charts
In this module, you will learn about the design of experiments (DOE), which plays a crucial role in the improve phase of the Six Sigma methodology. It provides a structured approach for data-driven decision-making and process optimization and enables you to examine the parameter space efficiently to comprehend the relationships between factors and responses. You will also gain insight into designing and conducting efficient experiments, analyzing experimental data, and optimizing process variables. By applying DOE techniques, you can systematically assess the process space, identify influential factors, and optimize process performance while minimizing time, cost, and resources. The module also provides an overview of control charts, their uses, and applications in the Six Sigma methodology. Additionally, you will gain an understanding of various types of control chart patterns used to detect special causes of variation.
Control: Sustain improvements and control the process
In this module, you will learn about strategies and practices to ensure that the improvements implemented in a process are sustained over the long term. You will learn how to establish a culture of continuous improvement, maintain process stability, monitor performance, and engage stakeholders in sustaining improvements. By mastering these strategies and techniques, you can ensure that the improvements implemented in a process have long-lasting effects and contribute to the organization’s overall success.
Peer Review Assignment
This is a peer-review assignment based on the concepts taught in The DMAIC Framework: Analyze, Improve, and Control Phase course. In this assignment, you will be able to explain how to apply the analyze, improve, and control phases of the DMAIC framework in a real-life scenario.
Prerequesites
Successful completion of the following courses:
Overview: Six Sigma and the Organization
The DMAIC Framework: The Define and Measure Phase
