University of Colorado Boulder - Data Science Methods for Quality Improvement Specialization
- Offered byCoursera
- Public/Government Institute
Data Science Methods for Quality Improvement Specialization at Coursera Overview
Data Science Methods for Quality Improvement Specialization
at Coursera
Duration | 4 months |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Science Methods for Quality Improvement Specialization
Table of content
- Overview
- Highlights
- Course Details
- Curriculum
Data Science Methods for Quality Improvement Specialization at Coursera Highlights
Data Science Methods for Quality Improvement Specialization
at Coursera
- Earn a Certificate upon completio
Skills -Data Science,Process Engineering,Data Analysis,Quality Improvement,Rstudio,analyzing data,describing data using R, graphing data
Data Science Methods for Quality Improvement Specialization at Coursera Course details
Data Science Methods for Quality Improvement Specialization
at Coursera
Skills you will learn
More about this course
- This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry
- The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability
- By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown
- Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field
- This specialization can be taken for academic credit as part of CU Boulder's Master of Science in Data Science (MS-DS) degree offered on the Coursera platform
Data Science Methods for Quality Improvement Specialization at Coursera Curriculum
Data Science Methods for Quality Improvement Specialization
at Coursera
Managing, Describing, and Analyzing Data
Stability and Capability in Quality Improvement
Measurement Systems Analysis
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