- Data Science
- Process Engineering
- Data Analysis
- Quality Improvement
- Rstudio
- analyzing data
- describing data
- using R
- graphing data
- Make decisions about process improvement
- Analyzing a process for stability
- Analyzing a process for capability
Data Science Methods for Quality Improvement 专项课程
开启数据科学领域的职业生涯。. Master strategies in data science methods for quality improvement.
您将学到的内容有
Manage, describe, and analyze data using applied statistics
Apply continuous and/or discrete data methods for process analysis, improvement, and ongoing management in a business or workplace
Analyze measurement systems to ensure their stability and capability
Calculate descriptive statistics and create graphical representations using R software
您将获得的技能
关于此 专项课程
1,625 次近期查看
Data analysis skills are widely sought by employers, both nationally and internationally. 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.
Learners are encouraged to complete this specialization in the order the courses are presented.
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. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
应用的学习项目
Learners develop an understanding of how to manage, describe, and analyze continuous and discrete data using examples from business and industry. They explore how to assess processes for sources of variation through time as well as determine process capability with respect to customer requirements. Learners gain familiarity with the analysis procedures to assess measurement systems for continuous and discrete data in order to make decisions regarding the capability and acceptability of the measurement system. Assignments require learners to perform analyses for various data types and scenarios, interpret results, and make appropriate decisions.
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
Familiarity with RStudio and applied statistics is recommended.
完成课程大约需要 4 个月
建议进度：3 小时/周
英语（English）
字幕：英语（English）
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
Familiarity with RStudio and applied statistics is recommended.
完成课程大约需要 4 个月
建议进度：3 小时/周
英语（English）
字幕：英语（English）
专项课程的运作方式
加入课程
Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习，请直接注册专项课程，或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时，您将自动订阅整个专项课程。您可以只完成一门课程，您可以随时暂停学习或结束订阅。访问您的学生面板，跟踪您的课程注册情况和进度。
实践项目
每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。
获得证书
在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 3 门课程
Managing, Describing, and Analyzing Data
18 个评分
In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution. Finally, you will learn the basics of sampling error, sampling distributions, and errors in decision-making.
Stability and Capability in Quality Improvement
In this course, you will learn to analyze data in terms of process stability and statistical control and why having a stable process is imperative prior to perform statistical hypothesis testing. You will create statistical process control charts for both continuous and discrete data using R software. You will analyze data sets for statistical control using control rules based on probability. Additionally, you will learn how to assess a process with respect to how capable it is of meeting specifications, either internal or external, and make decisions about process improvement.
Measurement Systems Analysis
In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior to performing any statistical analysis. You will analyze continuous measurement systems and statistically characterize both accuracy and precision using R software. You will perform measurement systems analysis for potential, short-term and long-term statistical control and capability. Additionally, you will learn how to assess a discrete measurement and perform analyses for internal consistency, concordance between assessors, and concordance with a standard. Finally, you will learn how to make decisions on measurement systems process improvement.
提供方
科罗拉多大学波德分校
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
立即开始攻读硕士学位
此 专项课程 隶属于 科罗拉多大学波德分校 提供的 100% 在线 Master of Science in Data Science。
如果您被录取参加全部课程，您的课程将计入您的学位学习进程。
常见问题
退款政策是如何规定的？
我可以只注册一门课程吗？
有助学金吗？
我可以免费学习课程吗？
此课程是 100% 在线学习吗？是否需要现场参加课程？
完成专项课程后我会获得大学学分吗？
还有其他问题吗？请访问 学生帮助中心。
