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

4.5
estrellas
22 calificaciones

Wendy Martin

2593 ya inscrito

ofrecido por

Parte del título de grado Master of Science in Data Science

Más información

Programa especializado: Data Science Methods for Quality ImprovementUniversidad de Colorado en Boulder

Acerca de este Curso

7387 vistas recientes

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.
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.

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 1 de 3 en

Programa especializado: Data Science Methods for Quality Improvement

Nivel principiante

Beginner level; no previous experience necessary.

Aprox. 18 horas para completar

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

Calculate descriptive statistics and create graphical representations using R software
Solve problems and make decisions using probability distributions
Explore the basics of sampling and sampling distributions with respect to statistical inference
Classify types of data with scales of measurement

Habilidades que obtendrás

analyzing data
describing data
using R
graphing data

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 1 de 3 en

Programa especializado: Data Science Methods for Quality Improvement

Nivel principiante

Beginner level; no previous experience necessary.

Aprox. 18 horas para completar

Inglés (English)

Subtítulos: Inglés (English)

Instructor

Calificación del instructor

4,11/5 (9 calificaciones)

Wendy Martin

W. Edwards Deming Professor of Management

Engineering Management Program

3077 alumnos

3 cursos

ofrecido por

Universidad de Colorado en Boulder

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.

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.

Más información

Programa - Qué aprenderás en este curso

Semana

Semana 1

3 horas para completar

Data and Measurement

Upon completion of this module, students will be able to use R and R Studio to work with data and classify types of data using measurement scales.

3 horas para completar

7 videos (Total 48 minutos), 2 lecturas, 2 cuestionarios

7 videos

Welcome to Managing, Describing and Analyzing Data1m

Types of Data and Measurement Scales6m

Measurement Scales: Nominal and Ordinal6m

Measurement Scales: Interval, Ratio and Absolute6m

Measurement as a Process, The Big 5 Aspects of Data10m

Sampling Concepts6m

Working in RStudio10m

2 lecturas

Welcome and Where to Find Help10m

Attention Learners: R Code / File Resources10m

2 ejercicios de práctica

Week 1 Practice Assessment30m

Assessment: Data and Measurement45m

Semana

Semana 2

5 horas para completar

Describing Data Graphically and Numerically

Upon completion of this module, students will be able to use R and RStudio to create visual representations of data, and calculate descriptive statistics to describe location, spread and shape of data.

5 horas para completar

11 videos (Total 85 minutos)

11 videos

Create a Run Chart9m

Frequency Distributions7m

Frequency Polygons and Histograms7m

Histogram Patterns and Density Plots7m

Box and Whisker Plots7m

Measures of Central Tendency Mean9m

Measures of Central Tendency: Median, Mode7m

Measures of Position8m

Measures of Dispersion 7m

Measures of Shape6m

Measures of Relationship6m

3 ejercicios de práctica

Week 2 Practice Assessment30m

Assessment: Describing Data Graphically1h 15m

Assessment: Describing Data Numerically1h 15m

Semana

Semana 3

4 horas para completar

Probability and Probability Distributions

Upon completion of this module, students will be able to apply the rules and conditions of probability and probability distributions to make decisions and solve problems using R and R Studio.

4 horas para completar

8 videos (Total 70 minutos)

Semana

Semana 4

3 horas para completar

Sampling Distributions, Error and Estimation

Upon completion of this module, students will be able to use R and RStudio to characterize sampling and sampling distributions, error and estimation with respect to statistical inference.

3 horas para completar

8 videos (Total 55 minutos)

Reseñas

4.5

7 reseña

5 stars
68,18 %
4 stars
22,72 %
3 stars
4,54 %
1 star
4,54 %

Principales reseñas sobre MANAGING, DESCRIBING, AND ANALYZING DATA

por MW12 de mar. de 2021

The instructor is clear and easy to follow. The lessons are succinct. It helps to be familiar with the topics already.

por BT18 de abr. de 2021

We learned some theory and practiced in R. A perfect combination!

Ver todas las reseñas

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.

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.

Managing, Describing, and Analyzing Data