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.
이 강좌는 Statistics with R 전문 분야의 일부입니다.
Introduction to Probability and Data
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1완료하는 데 12분 필요
About 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 video (Total 2 min), 1 reading
1개의 동영상
1개의 읽기 자료
More about Introduction to Probability and Data10m
완료하는 데 1시간 필요
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>
6 videos (Total 28 min), 2 readings, 2 quizzes
6개의 동영상
Data Basics5m
Observational Studies & Experiments4m
Sampling and sources of bias8m
Experimental Design2m
(Spotlight) Random Sample Assignment3m
2개의 읽기 자료
Lesson Learning Objectives10m
Suggested Readings and Practice10m
2개 연습문제
Week 1 Practice Quiz10m
Week 1 Quiz14m
완료하는 데 1시간 필요
Introduction to Data Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
2 readings, 1 quiz
2개의 읽기 자료
About Lab Choices (Read Before Selection)10m
Week 1 Lab Instructions (RStudio)10m
1개 연습문제
Week 1 Lab: Introduction to R and RStudio16m
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2완료하는 데 2시간 필요
Exploratory 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 videos (Total 46 min), 3 readings, 2 quizzes
7개의 동영상
Measures of Center4m
Measures of Spread6m
Robust Statistics1m
Transforming Data3m
Exploring Categorical Variables8m
Introduction to Inference12m
3개의 읽기 자료
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Suggested Readings and Practice10m
2개 연습문제
Week 2 Practice Quiz10m
Week 2 Quiz12m
완료하는 데 1시간 필요
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.
2 readings, 1 quiz
2개의 읽기 자료
Week 2 Lab Instructions (RStudio)10m
Week 2 Lab Instructions (RStudio Cloud)10m
1개 연습문제
Week 2 Lab: Introduction to Data20m
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3완료하는 데 2시간 필요
Introduction 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 videos (Total 82 min), 3 readings, 2 quizzes
9개의 동영상
Disjoint Events + General Addition Rule9m
Independence9m
Probability Examples9m
(Spotlight) Disjoint vs. Independent2m
Conditional Probability12m
Probability Trees10m
Bayesian Inference14m
Examples of Bayesian Inference7m
3개의 읽기 자료
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Suggested Readings and Practice10m
2개 연습문제
Week 3 Practice Quiz6m
Week 3 Quiz10m
완료하는 데 1시간 필요
Introduction to Probability Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
2 readings, 1 quiz
2개의 읽기 자료
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
1개 연습문제
Week 3 Lab: Probability10m
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4완료하는 데 2시간 필요
Probability 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 videos (Total 67 min), 4 readings, 2 quizzes
6개의 동영상
Evaluating the Normal Distribution2m
Working with the Normal Distribution5m
Binomial Distribution17m
Normal Approximation to Binomial14m
Working with the Binomial Distribution9m
4개의 읽기 자료
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Suggested Readings and Practice10m
Data Analysis Project Example10m
2개 연습문제
Week 4 Practice Quiz14m
Week 4 Quiz14m
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5완료하는 데 2시간 필요
Data Analysis Project
<p>Well done! You have reached the last week of Introduction to Probability and Data! There will not be any new videos in this week, instead, you will be asked to complete an initial data analysis project with a real-world data set. The project is designed to help you discover and explore research questions of your own, using real data and statistical methods we learn in this class. The the project will be graded via peer assessments, meaning that you will need to evaluate three peers' projects after submitting your own.</p><p>Get started with your data analysis in this week! It should be interesting and very exciting! As usual, feel free to post questions, concerns, and comments about the project on <a href="https://www.coursera.org/learn/probability-intro/module/BaTDb/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this week's forum</b></a>.</p>
1 reading, 1 quiz
1개의 읽기 자료
Project Information10m
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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.
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.
강사
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
듀크대학교 정보
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.
Statistics with R 전문 분야 정보
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.
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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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