Acerca de este Curso
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Nivel principiante

Aprox. 22 horas para completar

Sugerido: 5 weeks of study, 5-7 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Habilidades que obtendrás

StatisticsR ProgrammingRstudioExploratory Data Analysis

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel principiante

Aprox. 22 horas para completar

Sugerido: 5 weeks of study, 5-7 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Programa - Qué aprenderás en este curso

Semana
1
12 minutos para completar

About Introduction to Probability and Data

1 video (Total 2 minutos), 1 reading
1 lectura
More about Introduction to Probability and Data10m
1 hora para completar

Introduction to Data

6 videos (Total 28 minutos), 2 readings, 2 quizzes
6 videos
Sampling and sources of bias8m
Experimental Design2m
(Spotlight) Random Sample Assignment3m
2 lecturas
Lesson Learning Objectives10m
Suggested Readings and Practice10m
2 ejercicios de práctica
Week 1 Practice Quiz10m
Week 1 Quiz14m
1 hora para completar

Introduction to Data Project

2 readings, 1 quiz
2 lecturas
About Lab Choices (Read Before Selection)10m
Week 1 Lab Instructions (RStudio)10m
1 ejercicio de práctica
Week 1 Lab: Introduction to R and RStudio16m
Semana
2
2 horas para completar

Exploratory Data Analysis and Introduction to Inference

7 videos (Total 46 minutos), 3 readings, 2 quizzes
7 videos
Robust Statistics1m
Transforming Data3m
Exploring Categorical Variables8m
Introduction to Inference12m
3 lecturas
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Suggested Readings and Practice10m
2 ejercicios de práctica
Week 2 Practice Quiz10m
Week 2 Quiz12m
1 hora para completar

Exploratory Data Analysis and Introduction to Inference Project

2 readings, 1 quiz
2 lecturas
Week 2 Lab Instructions (RStudio)10m
Week 2 Lab Instructions (RStudio Cloud)10m
1 ejercicio de práctica
Week 2 Lab: Introduction to Data20m
Semana
3
2 horas para completar

Introduction to Probability

9 videos (Total 82 minutos), 3 readings, 2 quizzes
9 videos
Probability Examples9m
(Spotlight) Disjoint vs. Independent2m
Conditional Probability12m
Probability Trees10m
Bayesian Inference14m
Examples of Bayesian Inference7m
3 lecturas
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Suggested Readings and Practice10m
2 ejercicios de práctica
Week 3 Practice Quiz6m
Week 3 Quiz10m
1 hora para completar

Introduction to Probability Project

2 readings, 1 quiz
2 lecturas
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
1 ejercicio de práctica
Week 3 Lab: Probability10m
Semana
4
2 horas para completar

Probability Distributions

6 videos (Total 67 minutos), 4 readings, 2 quizzes
6 videos
Binomial Distribution17m
Normal Approximation to Binomial14m
Working with the Binomial Distribution9m
4 lecturas
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Suggested Readings and Practice10m
Data Analysis Project Example10m
2 ejercicios de práctica
Week 4 Practice Quiz14m
Week 4 Quiz14m
4.7
703 revisionesChevron Right

33%

comenzó una nueva carrera después de completar estos cursos

30%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

11%

consiguió un aumento de sueldo o ascenso

Principales revisiones sobre Introduction to Probability and Data

por AAJan 24th 2018

This course literally taught me a lot, the concepts were beautifully explained but the way it was delivered and overall exercises and the difficulty of problems made it more challenging and enjoying.

por HDMar 31st 2018

The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.

Instructor

Avatar

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

Acerca de Universidad Duke

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world....

Acerca de Programa especializado Statistics with R

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions....
Statistics with R

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • 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.

  • No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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