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 faz parte do Programa de cursos integrados Statistics with R
Introduction to Probability and Data
oferecido por
Informações sobre o curso
4.7
2,595 classificações
Habilidades que você terá
StatisticsR ProgrammingRstudioExploratory Data Analysis
Programa - O que você aprenderá com este curso
Semana
1About Introduction to Probability and Data
<p>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.<p>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 <a href="https://leanpub.com/openintro-statistics/" target="_blank">OpenIntro Statistics, 3rd Edition</a> (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.<p>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 <a href="https://www.coursera.org/learn/probability-intro/resources/crMc4" target="_blank">resource page</a> listing useful resources for this course. <p>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.</p>
1 vídeo (total de (Total 2 mín.) min), 1 leitura
1 vídeos
1 leituras
More about Introduction to Probability and Data10min
Introduction to Data
<p>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 <a href="https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this module's forum</b></a> and discuss with your peers! To get started, view the <a href="https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives" target="_blank"><b>learning objectives</b></a> of Lesson 1 in this module.</p>
7 vídeos (total de (Total 30 mín.) min), 5 leituras, 3 testes
7 videos
Introduction3min
Data Basics5min
Observational Studies & Experiments4min
Sampling and sources of bias8min
Experimental Design2min
(Spotlight) Random Sample Assignment3min
DataCamp Instructions2min
5 leituras
Lesson Learning Objectives10min
Suggested Readings and Practice10min
About Lesson Choices (Read Before Selection)10min
Week 1 Lab Instructions (RStudio)10min
Week 1 Lab Instructions (DataCamp)10min
3 exercícios práticos
Week 1 Practice Quiz10min
Week 1 Quiz14min
Week 1 Lab: Introduction to R and RStudio16min
Semana
2Exploratory Data Analysis and Introduction to Inference
<p>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. </p>
7 vídeos (total de (Total 46 mín.) min), 5 leituras, 3 testes
7 videos
Measures of Center4min
Measures of Spread6min
Robust Statistics1min
Transforming Data3min
Exploring Categorical Variables8min
Introduction to Inference12min
5 leituras
Lesson Learning Objectives10min
Lesson Learning Objectives10min
Suggested Readings and Practice10min
Week 2 Lab Instructions (RStudio)10min
Week 2 Lab Instructions (DataCamp)10min
3 exercícios práticos
Week 2 Practice Quiz10min
Week 2 Quiz12min
Week 2 Lab: Introduction to Data20min
Semana
3Introduction to Probability
<p>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. </p><p>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. </p>
9 vídeos (total de (Total 82 mín.) min), 5 leituras, 3 testes
9 videos
Introduction5min
Disjoint Events + General Addition Rule9min
Independence9min
Probability Examples9min
(Spotlight) Disjoint vs. Independent2min
Conditional Probability12min
Probability Trees10min
Bayesian Inference14min
Examples of Bayesian Inference7min
5 leituras
Lesson Learning Objectives10min
Lesson Learning Objectives10min
Suggested Readings and Practice10min
Week 3 Lab Instructions (RStudio)10min
Week 3 Lab Instructions (DataCamp)10min
3 exercícios práticos
Week 3 Practice Quiz6min
Week 3 Quiz10min
Week 3 Lab: Probability10min
Semana
4Probability Distributions
<p>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 <b>no labs</b> for this week. Please don't hesitate to post any questions, discussions and related topics on <a href="https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this week's forum</b></a>.</p>
6 vídeos (total de (Total 67 mín.) min), 4 leituras, 2 testes
6 videos
Normal Distribution17min
Evaluating the Normal Distribution2min
Working with the Normal Distribution5min
Binomial Distribution17min
Normal Approximation to Binomial14min
Working with the Binomial Distribution9min
4 leituras
Lesson Learning Objectives10min
Lesson Learning Objectives10min
Suggested Readings and Practice10min
Data Analysis Project Example10min
2 exercícios práticos
Week 4 Practice Quiz14min
Week 4 Quiz14min
29%
comecei uma nova carreira após concluir estes cursos
26%
consegui um benefício significativo de carreira com este curso
14%
recebi um aumento ou promoção
Melhores avaliações
por AA•Jan 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 HD•Mar 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.
Instrutores
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
Sobre Duke University
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.
Sobre o Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
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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