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.
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教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 12 分鐘
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.
1 個視頻 （總計 2 分鐘）, 1 個閱讀材料
1 個閱讀材料
More about Introduction to Probability and Data10分鐘
完成時間為 1 小時
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.
6 個視頻 （總計 28 分鐘）, 2 個閱讀材料, 2 個測驗
6 個視頻
Introduction3分鐘
Data Basics5分鐘
Observational Studies & Experiments4分鐘
Sampling and sources of bias8分鐘
Experimental Design2分鐘
(Spotlight) Random Sample Assignment3分鐘
2 個閱讀材料
Lesson Learning Objectives10分鐘
Suggested Readings and Practice10分鐘
2 個練習
Week 1 Practice Quiz10分鐘
Week 1 Quiz14分鐘
完成時間為 1 小時
Introduction to Data Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
2 個閱讀材料, 1 個測驗
2 個閱讀材料
About Lab Choices (Read Before Selection)10分鐘
Week 1 Lab Instructions (RStudio)10分鐘
1 個練習
Week 1 Lab: Introduction to R and RStudio16分鐘
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2完成時間為 2 小時
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.
7 個視頻 （總計 46 分鐘）, 3 個閱讀材料, 2 個測驗
7 個視頻
Measures of Center4分鐘
Measures of Spread6分鐘
Robust Statistics1分鐘
Transforming Data3分鐘
Exploring Categorical Variables8分鐘
Introduction to Inference12分鐘
3 個閱讀材料
Lesson Learning Objectives10分鐘
Lesson Learning Objectives10分鐘
Suggested Readings and Practice10分鐘
2 個練習
Week 2 Practice Quiz10分鐘
Week 2 Quiz12分鐘
完成時間為 1 小時
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 個閱讀材料, 1 個測驗
2 個閱讀材料
Week 2 Lab Instructions (RStudio)10分鐘
Week 2 Lab Instructions (RStudio Cloud)10分鐘
1 個練習
Week 2 Lab: Introduction to Data20分鐘
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3完成時間為 2 小時
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.
9 個視頻 （總計 82 分鐘）, 3 個閱讀材料, 2 個測驗
9 個視頻
Introduction5分鐘
Disjoint Events + General Addition Rule9分鐘
Independence9分鐘
Probability Examples9分鐘
(Spotlight) Disjoint vs. Independent2分鐘
Conditional Probability12分鐘
Probability Trees10分鐘
Bayesian Inference14分鐘
Examples of Bayesian Inference7分鐘
3 個閱讀材料
Lesson Learning Objectives10分鐘
Lesson Learning Objectives10分鐘
Suggested Readings and Practice10分鐘
2 個練習
Week 3 Practice Quiz6分鐘
Week 3 Quiz10分鐘
完成時間為 1 小時
Introduction to Probability Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
2 個閱讀材料, 1 個測驗
2 個閱讀材料
Week 3 Lab Instructions (RStudio)10分鐘
Week 3 Lab Instructions (RStudio Cloud)10分鐘
1 個練習
Week 3 Lab: Probability10分鐘
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4完成時間為 2 小時
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).
6 個視頻 （總計 67 分鐘）, 4 個閱讀材料, 2 個測驗
6 個視頻
Evaluating the Normal Distribution2分鐘
Working with the Normal Distribution5分鐘
Binomial Distribution17分鐘
Normal Approximation to Binomial14分鐘
Working with the Binomial Distribution9分鐘
4 個閱讀材料
Lesson Learning Objectives10分鐘
Lesson Learning Objectives10分鐘
Suggested Readings and Practice10分鐘
Data Analysis Project Example10分鐘
2 個練習
Week 4 Practice Quiz14分鐘
Week 4 Quiz14分鐘
4.7
754 個審閱34%
完成這些課程後已開始新的職業生涯
30%
通過此課程獲得實實在在的工作福利
11%
加薪或升職
來自Introduction to Probability and Data的熱門評論
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.
講師
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
關於 杜克大学
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.
關於 Statistics with R 專項課程
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.
常見問題
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
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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