This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
Este curso forma parte de Programa Especializado - Statistics with R
Introduction to Probability and Data
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Resultados profesionales del estudiante
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
Habilidades que obtendrás
StatisticsR ProgrammingRstudioExploratory Data Analysis
Resultados profesionales del estudiante
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
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 5 en
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
12 minutos para completar
About Introduction to Probability and Data
This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from OpenIntro Statistics, 3rd Edition, https://leanpub.com/openintro-statistics/, (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing. Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the resource page (https://www.coursera.org/learn/probability-intro/resources/crMc4) listing useful resources for this course. Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.
12 minutos para completar
1 video (Total 2 minutos), 1 lectura
1 video
1 lectura
More about Introduction to Probability and Data10m
1 hora para completar
Introduction to Data
Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. If you have any questions, feel free to post them on this module's forum (https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1) and discuss with your peers! To get started, view the learning objectives (https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives) of Lesson 1 in this module.
1 hora para completar
6 videos (Total 28 minutos), 2 lecturas, 2 cuestionarios
6 videos
Data Basics5m
Observational Studies & Experiments4m
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
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1 hora para completar
2 lecturas
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
2 horas para completar
Exploratory Data Analysis and Introduction to Inference
Welcome to Week 2 of Introduction to Probability and Data! Hope you enjoyed materials from Week 1. This week we will delve into numerical and categorical data in more depth, and introduce inference.
2 horas para completar
7 videos (Total 46 minutos), 3 lecturas, 2 cuestionarios
7 videos
Measures of Center4m
Measures of Spread6m
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
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1 hora para completar
2 lecturas
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
2 horas para completar
Introduction to Probability
Welcome to Week 3 of Introduction to Probability and Data! Last week we explored numerical and categorical data. This week we will discuss probability, conditional probability, the Bayes’ theorem, and provide a light introduction to Bayesian inference. Thank you for your enthusiasm and participation, and have a great week! I’m looking forward to working with you on the rest of this course.
2 horas para completar
9 videos (Total 82 minutos), 3 lecturas, 2 cuestionarios
9 videos
Disjoint Events + General Addition Rule9m
Independence9m
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
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1 hora para completar
2 lecturas
2 lecturas
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
1 ejercicio de práctica
Week 3 Lab: Probability10m
2 horas para completar
Probability Distributions
Great work so far! Welcome to Week 4 -- the last content week of Introduction to Probability and Data! This week we will introduce two probability distributions: the normal and the binomial distributions in particular. As usual, you can evaluate your knowledge in this week's quiz. There will be no labs for this week. Please don't hesitate to post any questions, discussions and related topics on this week's forum (https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1).
2 horas para completar
6 videos (Total 67 minutos), 4 lecturas, 2 cuestionarios
6 videos
Evaluating the Normal Distribution2m
Working with the Normal Distribution5m
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
Revisiones
Principales revisiones sobre INTRODUCTION TO PROBABILITY AND DATA
por AAJan 23, 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 AAJun 9, 2017
A good introductory course to data analysis, statistics and the R programming language. Recommended to people who are new to data analysis and also those who are experienced and could use a refresh.
por BBSep 3, 2019
Very clearly explained and the pace is awesome! I really enjoy each deadline and l can already see how it is impacting my day to day work and life. I ook forward to completing the course! Thank you.
por HDMar 30, 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.
por MMJul 5, 2019
Good but questions lacked clarity in what is expected. i.e. "Work out the Boy to Girl ratio" - in what fashion do we do this, as it appeared to be the same as simply working out the "proportion of b
por JHMar 26, 2020
The instructions for the final project need to be much clearer. I had a hard time figuring it out, and all of the projects I peer-edited were done poorly. Otherwise, I enjoyed the course very much!
por HWDec 13, 2017
The lectures were very clear and concise and the examples were very relevant. Some of the R instructions left a little something to be desired, but nothing a little time and google couldn't solve.
por RRSep 17, 2016
Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher
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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
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.
¿Qué recibiré si me suscribo a este Programa especializado?
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¿Hay ayuda económica disponible?
Will I receive a transcript from Duke University for completing this course?
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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