About this 전문분야

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

완료하는 데 약 7개월 필요

매주 4시간 권장

영어

자막: 영어, 한국어

귀하가 습득할 기술

Bayesian StatisticsLinear RegressionStatistical InferenceR Programming

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

완료하는 데 약 7개월 필요

매주 4시간 권장

영어

자막: 영어, 한국어

How the 전문분야 Works

강좌 수강

Coursera 전문 분야는 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 전문 분야에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 하나의 전문 분야에 속하는 강좌에 등록하면 해당 전문 분야 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료해도 됩니다. — 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.

실습 프로젝트

모든 전문 분야에는 실습 프로젝트가 포함되어 있습니다. 전문 분야를 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 전문 분야에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우 각 강좌를 완료해야 프로젝트를 시작할 수 있습니다.

수료증 취득

모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

how it works

이 전문분야에는 5개의 강좌가 있습니다.

강좌1

Introduction to Probability and Data

4.7
2,959개의 평가
662개의 리뷰
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....
강좌2

Inferential Statistics

4.8
1,327개의 평가
247개의 리뷰
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. 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 course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data...
강좌3

Linear Regression and Modeling

4.7
932개의 평가
166개의 리뷰
This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio....
강좌4

베이지안 통계

3.9
532개의 평가
158개의 리뷰
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."...
강좌5

Statistics with R Capstone

4.7
116개의 평가
25개의 리뷰
The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question. The analysis will implement both frequentist and Bayesian techniques and discuss in context of the data how these two approaches are similar and different, and what these differences mean for conclusions that can be drawn from the data. A sampling of the final projects will be featured on the Duke Statistical Science department website. Note: Only learners who have passed the four previous courses in the specialization are eligible to take the Capstone....

강사

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Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science
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David Banks

Professor of the Practice
Statistical Science
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Colin Rundel

Assistant Professor of the Practice
Statistical Science
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Merlise A Clyde

Professor
Department 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....

자주 묻는 질문

  • 네! 시작하려면 관심 있는 강좌 카드를 클릭하여 등록합니다. 강좌를 등록하고 완료하면 공유할 수 있는 인증서를 얻거나 강좌를 청강하여 강좌 자료를 무료로 볼 수 있습니다. 전문 분야 과정에 있는 강좌에 등록하면, 전체 전문 분야에 등록하게 됩니다. 학습자 대시보드에서 진행 사항을 추적할 수 있습니다.

  • 이 강좌는 100% 온라인으로 진행되므로 강의실에 직접 참석할 필요가 없습니다. 웹 또는 모바일 장치를 통해 언제 어디서든 강의, 읽기 자료, 과제에 접근할 수 있습니다.

  • Basic math, no programming experience required. A genuine interest in data analysis is a plus!

    In the later courses in the Specialization, we assume knowledge and skills equivalent to those which would have been gained in the prior courses (for example: if you decide to take course four, Bayesian Statistics, without taking the prior three courses we assume you have knowledge of frequentist statistics and R equivalent to what is taught in the first three courses).

  • Yes.

  • Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • In this specialization, R is a requirement, and the labs have been enhanced and revised from the previous course. Success in the fourth course and the capstone project will depend heavily on successfully completing the first three courses in this specialization. Therefore, we require all students complete all courses to obtain the certificate.

  • Yes. You will need R and RStudio. Both are free and publicly available. You will need administrator access to your computer to install this software.

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