This course is part of the Data Science Methods for Quality Improvement Specialization

Wendy Martin

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Part of the Master of Science in Data Science degree
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Data Science Methods for Quality Improvement SpecializationUniversity of Colorado Boulder

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Data Science Methods for Quality Improvement Specialization

Intermediate Level

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

Approx. 9 hours to complete

English

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What you will learn

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

Skills you will gain

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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Data Science Methods for Quality Improvement Specialization

Intermediate Level

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

Approx. 9 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Wendy Martin

W. Edwards Deming Professor of Management

Engineering Management Program

3,846 Learners

4 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

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.

2 hours to complete

13 videos (Total 80 min), 1 reading, 1 quiz

13 videos

Working in RStudio10mProcess Variation6mCommon and Special Cause Variation6mPurpose of a Control Chart4mConformance Quality5mThe Product and Process Control Cycles6mProcess Dominance5mControl Chart Basics4mCreating a Control Chart - Steps 1 and 25mCreating a Control Chart - Step 2 (continued)5mCreating a Control Chart - Step 34mCreating a Control Chart - Step 45mCreating a Control Chart - Steps 5, 6 and 78m

1 reading

Welcome and Where to Find Help 10m

1 practice exercise

Process Variation, Process Control and Control Charts30m

Week2

Week 2

2 hours to complete

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.

2 hours to complete

11 videos (Total 66 min)

11 videos

Mean and Range Charts - Part 14mMean and Range Charts - Part 25mMean and Range Charts - Part 37mMean and Standard Deviation Charts - Part 15mMean and Standard Deviation Charts - Part 25mIndividuals and Moving Range Charts - Part 14mIndividuals and Moving Range Charts - Part 25mIndividuals and Moving Range Charts - Part 37mIndividuals and Moving Range Charts - Part 44mSetup Dominant Processes8mMachine Dominant Processes8m

1 practice exercise

Xbar and R / Xbar and S Charts / X and MR Charts30m

Week3

Week 3

2 hours to complete

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.

2 hours to complete

12 videos (Total 58 min)

12 videos

Introduction3mLog Transformed Data - Part 14mLog Transformed Data - Part 24mExponential Data - Part 15mExponential Data - Part 24mExponential Data - Part 38mIntroduction to Distribution Fitting3mGoodness of Fit Testing - One Distribution4mGoodness of Fit Testing - Multiple Distributions4mThe Johnson Distribution - Part 14mThe Johnson Distribution - Part 25mSelecting the Best Fit and Creating the Control Chart3m

1 practice exercise

X and Moving Range Charts for Non-Normally Distributed Data30m

Week4

Week 4

2 hours to complete

Process Capability

2 hours to complete

16 videos (Total 73 min)

16 videos

Process Control vs Process Capability3mCapability Indices4mCpm and the Taguchi Loss Function2mCapability vs Performance Measures2mCapability / Performance - Xbar and R chart Part 16mCapability / Performance - Xbar and R chart Part 25mCapability / Performance - Xbar and s chart Part 15mCapability / Performance - Xbar and s chart Part 25mCapability / Performance - X and MR chart4mCapability / Performance - Transformed Data Part 16mCapability / Performance - Transformed Data Part 26mCapability / Performance - Transformed Data Part 32mCapability / Performance - Exponential Part 15mCapability / Performance - Exponential Part 24mCapability / Performance - Distribution Fitting Part 14mCapability / Performance - Distribution Fitting Part 23m

1 practice exercise

Process Capability30m

About the Data Science Methods for Quality Improvement Specialization

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

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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