Este curso forma parte de Programa especializado: Data Science Methods for Quality Improvement

Wendy Martin

Ofrecido Por

Parte del título de grado Master of Science in Data Science
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Programa especializado: Data Science Methods for Quality ImprovementUniversidad de Colorado en Boulder

Acerca de este Curso

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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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Curso 2 de 3 en

Programa especializado: Data Science Methods for Quality Improvement

Nivel intermedio

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

Aprox. 9 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Qué aprenderás

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

Habilidades que obtendrás

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

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 3 en

Programa especializado: Data Science Methods for Quality Improvement

Nivel intermedio

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

Aprox. 9 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructor

Wendy Martin

W. Edwards Deming Professor of Management

Engineering Management Program

3653 alumnos

3 cursos

Ofrecido por

Universidad de Colorado en Boulder

Comienza a trabajar para obtener tu maestría

Este curso es parte del Master of Science in Data Science completamente en línea de Universidad de Colorado en Boulder. Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.

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Programa - Qué aprenderás en este curso

Semana

Semana 1

2 horas para completar

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 horas para completar

13 videos (Total 80 minutos), 1 lectura, 1 cuestionario

13 videos

Working in RStudio10m

Process Variation6m

Common and Special Cause Variation6m

Purpose of a Control Chart4m

Conformance Quality5m

The Product and Process Control Cycles6m

Process Dominance5m

Control Chart Basics4m

Creating a Control Chart - Steps 1 and 25m

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

Creating a Control Chart - Step 34m

Creating a Control Chart - Step 45m

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

1 lectura

Welcome and Where to Find Help 10m

1 ejercicio de práctica

Process Variation, Process Control and Control Charts30m

Semana

Semana 2

2 horas para completar

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 horas para completar

11 videos (Total 66 minutos)

11 videos

Mean and Range Charts - Part 14m

Mean and Range Charts - Part 25m

Mean and Range Charts - Part 37m

Mean and Standard Deviation Charts - Part 15m

Mean and Standard Deviation Charts - Part 25m

Individuals and Moving Range Charts - Part 14m

Individuals and Moving Range Charts - Part 25m

Individuals and Moving Range Charts - Part 37m

Individuals and Moving Range Charts - Part 44m

Setup Dominant Processes8m

Machine Dominant Processes8m

1 ejercicio de práctica

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

Semana

Semana 3

2 horas para completar

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 horas para completar

12 videos (Total 58 minutos)

12 videos

Introduction3m

Log Transformed Data - Part 14m

Log Transformed Data - Part 24m

Exponential Data - Part 15m

Exponential Data - Part 24m

Exponential Data - Part 38m

Introduction to Distribution Fitting3m

Goodness of Fit Testing - One Distribution4m

Goodness of Fit Testing - Multiple Distributions4m

The Johnson Distribution - Part 14m

The Johnson Distribution - Part 25m

Selecting the Best Fit and Creating the Control Chart3m

1 ejercicio de práctica

X and Moving Range Charts for Non-Normally Distributed Data30m

Semana

Semana 4

2 horas para completar

Process Capability

2 horas para completar

16 videos (Total 73 minutos)

16 videos

Process Control vs Process Capability3m

Capability Indices4m

Cpm and the Taguchi Loss Function2m

Capability vs Performance Measures2m

Capability / Performance - Xbar and R chart Part 16m

Capability / Performance - Xbar and R chart Part 25m

Capability / Performance - Xbar and s chart Part 15m

Capability / Performance - Xbar and s chart Part 25m

Capability / Performance - X and MR chart4m

Capability / Performance - Transformed Data Part 16m

Capability / Performance - Transformed Data Part 26m

Capability / Performance - Transformed Data Part 32m

Capability / Performance - Exponential Part 15m

Capability / Performance - Exponential Part 24m

Capability / Performance - Distribution Fitting Part 14m

Capability / Performance - Distribution Fitting Part 23m

1 ejercicio de práctica

Process Capability30m

Acerca de Programa especializado: Data Science Methods for Quality Improvement

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

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Stability and Capability in Quality Improvement

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Qué aprenderás

Habilidades que obtendrás