本课程是 Data Science Methods for Quality Improvement 专项课程专项课程的一部分

Wendy Martin

提供方

隶属于 Master of Science in Data Science 学位
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Data Science Methods for Quality Improvement 专项课程科罗拉多大学波德分校

课程信息

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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.
This course 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.

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第 2 门课程（共 3 门）

Data Science Methods for Quality Improvement 专项课程

中级

Background knowledge in statistics is helpful; visit Managing, Describing, and Analyzing Data as a prerequisite to this course. R / RStudio required.

完成时间大约为9 小时

英语（English）

对员工进行热门技能培训能否为您的公司带来益处？

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您将学到的内容有

Understand how to use, select, and interpret process control charts to identify special causes of variation
Create and interpret control charts for normal and non-normal distributions
Create and interpret control charts for discrete data
Analyze the capability of a process to meet customer specifications

您将获得的技能

Analyzing a process for capability
Analyzing a process for stability
Make decisions about process improvement
R Programming
Rstudio

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 2 门课程（共 3 门）

Data Science Methods for Quality Improvement 专项课程

中级

Background knowledge in statistics is helpful; visit Managing, Describing, and Analyzing Data as a prerequisite to this course. R / RStudio required.

完成时间大约为9 小时

英语（English）

对员工进行热门技能培训能否为您的公司带来益处？

体验 Coursera 企业版

授课教师

Wendy Martin

W. Edwards Deming Professor of Management

Engineering Management Program

3,656 个学生

3 门课程

提供方

科罗拉多大学波德分校

立即开始攻读硕士学位

此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。

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了解顶级公司的员工如何掌握热门技能

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授课大纲 - 您将从这门课程中学到什么

周

周 1

完成时间为 2 小时

Understanding Process Variation, Process Control and Control Charts

In this module, you will learn how to define a process and break it down into components for the purpose of identifying potential sources of variation. You will learn how to classify variation into common and special causes through the use of a control chart. You’ll discover the Taguchi Loss function, and how it relates to the philosophy of quality, and its association to the product control and process control cycles. You will learn the basic anatomy of a control chart as well as the process used to create a control chart, and common errors encountered when using a control chart in practice. You will be able to calculate an appropriate sample size, as well as determine when a process is in control or out of control based on statistical rules.

完成时间为 2 小时

13 个视频（总计 80 分钟）, 1 个阅读材料, 1 个测验

13 个视频

Working in RStudio10分钟

Process Variation6分钟

Common and Special Cause Variation6分钟

Purpose of a Control Chart4分钟

Conformance Quality5分钟

The Product and Process Control Cycles6分钟

Process Dominance5分钟

Control Chart Basics4分钟

Creating a Control Chart - Steps 1 and 25分钟

Creating a Control Chart - Step 2 (continued)5分钟

Creating a Control Chart - Step 34分钟

Creating a Control Chart - Step 45分钟

Creating a Control Chart - Steps 5, 6 and 78分钟

1 个阅读材料

Welcome and Where to Find Help 10分钟

1 个练习

Process Variation, Process Control and Control Charts30分钟

周

周 2

完成时间为 2 小时

Xbar and R / Xbar and S Charts / X and MR Charts

In this module, you will learn how to select the appropriate chart given information on sample size and data type. You’ll learn how to create and interpret control charts with subgroups for variables data, as well as how to create them in R. You will also create and interpret control charts with a sample size of one data that is normally distributed. You'll learn how to monitor other statistics using the Individuals and Moving Range Chart. Finally, you will interpret the control charts for statistical control / stability.

完成时间为 2 小时

11 个视频（总计 66 分钟）

11 个视频

Mean and Range Charts - Part 14分钟

Mean and Range Charts - Part 25分钟

Mean and Range Charts - Part 37分钟

Mean and Standard Deviation Charts - Part 15分钟

Mean and Standard Deviation Charts - Part 25分钟

Individuals and Moving Range Charts - Part 14分钟

Individuals and Moving Range Charts - Part 25分钟

Individuals and Moving Range Charts - Part 37分钟

Individuals and Moving Range Charts - Part 44分钟

Setup Dominant Processes8分钟

Machine Dominant Processes8分钟

1 个练习

Xbar and R / Xbar and S Charts / X and MR Charts30分钟

周

周 3

完成时间为 2 小时

X and Moving Range Charts for Non-Normally Distributed Data

In this module, you will learn how to create an X and Moving Range Chart when the underlying distribution is not normally distributed. You’ll learn how to calculate control limits for the X and MR Charts with LogNormal transformed distribution and exponential distribution. Additionally, you will learn how to fit a distribution to the data and calculate control limits associated with the selected distribution. Finally, you will interpret the control charts for statistical control / stability.

完成时间为 2 小时

12 个视频（总计 58 分钟）

12 个视频

Introduction3分钟

Log Transformed Data - Part 14分钟

Log Transformed Data - Part 24分钟

Exponential Data - Part 15分钟

Exponential Data - Part 24分钟

Exponential Data - Part 38分钟

Introduction to Distribution Fitting3分钟

Goodness of Fit Testing - One Distribution4分钟

Goodness of Fit Testing - Multiple Distributions4分钟

The Johnson Distribution - Part 14分钟

The Johnson Distribution - Part 25分钟

Selecting the Best Fit and Creating the Control Chart3分钟

1 个练习

X and Moving Range Charts for Non-Normally Distributed Data30分钟

周

周 4

完成时间为 2 小时

Process Capability

完成时间为 2 小时

16 个视频（总计 73 分钟）

16 个视频

Process Control vs Process Capability3分钟

Capability Indices4分钟

Cpm and the Taguchi Loss Function2分钟

Capability vs Performance Measures2分钟

Capability / Performance - Xbar and R chart Part 16分钟

Capability / Performance - Xbar and R chart Part 25分钟

Capability / Performance - Xbar and s chart Part 15分钟

Capability / Performance - Xbar and s chart Part 25分钟

Capability / Performance - X and MR chart4分钟

Capability / Performance - Transformed Data Part 16分钟

Capability / Performance - Transformed Data Part 26分钟

Capability / Performance - Transformed Data Part 32分钟

Capability / Performance - Exponential Part 15分钟

Capability / Performance - Exponential Part 24分钟

Capability / Performance - Distribution Fitting Part 14分钟

Capability / Performance - Distribution Fitting Part 23分钟

1 个练习

Process Capability30分钟

关于 Data Science Methods for Quality Improvement 专项课程

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.

Data Science Methods for Quality Improvement

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Stability and Capability in Quality Improvement

课程信息

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您将获得的技能