Data Analysis for Black Belt
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The Data Analytics for Black Belt course presents a deep dive into statistical tools used in implementing Six Sigma in the real world. You will focus on the issues of metrics and their importance in Define, Measure, Analyze, Improve, and Control (DMAIC) projects, as well as identifying the different types of data and how data is used to solve problems. The primary purpose of this course is to train learners who plan to earn their Lean Six Sigma Black Belt certification through Georgia Tech. However, those who wish to take the course as a standalone learning opportunity are welcome to register. This course is an updated version of Applied Statistics for Six Sigma. Learners who have already taken Applied Statistics for Six Sigma do not need to take this course to move on to the Lean Six Sigma Black Belt course.
What You Will Learn
- Measurement, data, and performance metric reporting
- The three disciplines of statistics
- Normal distribution, central tendency, variability, and sample size
- Processes and variations
How You Will Benefit
- Gain understanding of how to use statistical tools in Six Sigma.
- Select the appropriate tests and interpret results.
INTRODUCTION TO MINITAB
- How to utilize software to perform Lean Six Sigma data analysis
USE CONTINUOUS DATA TO DESCRIBE A PROCESS
- Standard deviation
- Normal curve
UNDERSTAND THE RELEVANCE OF PRODUCE/PROCESS DESIGN SPECIFICATION
- Target value
- Upper and lower specification limits
USE STATISTICAL TOOLS TO PROVE THE HYPOTHESIS ASSOCIATE WITH DATA SETS
- Continuous Y; Discrete X
- Discrete X; Continuous Y
- Continuous X; Continuous Y
Who should attend
This course is designed for those interested in learning data analytics and hypothesis testing and its application in Lean Six Sigma training. Potential candidates can come from all industries regardless of role or title. Students do not have to be currently employed to take this course.