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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About this Course
최근 조회 291,133회
학습자 경력 결과
33%
이 강좌를 수료한 후 새로운 경력 시작하기
30%
이 강좌를 통해 확실한 경력상 이점 얻기
11%
급여 인상 또는 승진하기
귀하가 습득할 기술
StatisticsR ProgrammingRstudioExploratory Data Analysis
학습자 경력 결과
33%
이 강좌를 수료한 후 새로운 경력 시작하기
30%
이 강좌를 통해 확실한 경력상 이점 얻기
11%
급여 인상 또는 승진하기
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계
완료하는 데 약 22시간 필요
권장: 5 weeks of study, 5-7 hours/week
영어
자막: 영어, 한국어
제공자:
듀크대학교
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.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 12분 필요
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.
완료하는 데 12분 필요
1개 동영상 (총 2분), 1 reading
1개의 동영상
1개의 읽기 자료
More about Introduction to Probability and Data10m
완료하는 데 1시간 필요
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.
완료하는 데 1시간 필요
6개 동영상 (총 28분), 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.
완료하는 데 1시간 필요
2 readings
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
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
7개 동영상 (총 46분), 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.
완료하는 데 1시간 필요
2 readings
2개의 읽기 자료
Week 2 Lab Instructions (RStudio)10m
Week 2 Lab Instructions (RStudio Cloud)10m
1개 연습문제
Week 2 Lab: Introduction to Data20m
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
9개 동영상 (총 82분), 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.
완료하는 데 1시간 필요
2 readings
2개의 읽기 자료
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
1개 연습문제
Week 3 Lab: Probability10m
완료하는 데 2시간 필요
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).
완료하는 데 2시간 필요
6개 동영상 (총 67분), 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
검토
4.7
INTRODUCTION TO PROBABILITY AND DATA의 최상위 리뷰
AA 제공Jan 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.
BB 제공Sep 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.
AA 제공Jun 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.
HD 제공Mar 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.
MM 제공Jul 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
JH 제공Mar 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!
HW 제공Dec 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.
SS 제공Jul 26, 2017
Great course! Explained the concepts so clear and crisp and the exercises with R are great. The project reinforces all the concepts. All in all, a great course for beginners in statistics and R.
SG 제공Jun 18, 2019
The contents of the course about statistics are friendly to the beginners and easy to understand, however, the R learning is a little bit hard to those who have no computer or coding background.
RR 제공Sep 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
SN 제공Mar 11, 2019
This course was quite helpful for someone who like me who doesn't have a strong understanding of statistics. The highlight of the course was learning a new data analysis tool - R and RStudio.
GL 제공Jun 16, 2019
The final project is quite challenging but I have learned so much from this course. The lecture is great and the professor explains each concept well with examples. Really worthy of taking!!!
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
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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