About this Course
최근 조회 102,222

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 22시간 필요

권장: Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics....

영어

자막: 영어

귀하가 습득할 기술

StatisticsBayesian StatisticsBayesian InferenceR Programming
Course을(를) 수강하는 학습자
  • Biostatisticians
  • Data Scientists
  • Researchers
  • Research Assistants
  • Data Analysts

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 22시간 필요

권장: Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics....

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 3시간 필요

Probability and Bayes' Theorem

8개 동영상 (총 38분), 4 readings, 5 quizzes
8개의 동영상
Lesson 1.1 Classical and frequentist probability6m
Lesson 1.2 Bayesian probability and coherence3m
Lesson 2.1 Conditional probability4m
Lesson 2.2 Bayes' theorem6m
Lesson 3.1 Bernoulli and binomial distributions5m
Lesson 3.2 Uniform distribution5m
Lesson 3.3 Exponential and normal distributions2m
4개의 읽기 자료
Module 1 objectives, assignments, and supplementary materials3m
Background for Lesson 110m
Supplementary material for Lesson 23m
Supplementary material for Lesson 320m
5개 연습문제
Lesson 116m
Lesson 212m
Lesson 3.120m
Lesson 3.2-3.310m
Module 1 Honors15m
2
완료하는 데 3시간 필요

Statistical Inference

11개 동영상 (총 59분), 5 readings, 4 quizzes
11개의 동영상
Lesson 4.2 Likelihood function and maximum likelihood7m
Lesson 4.3 Computing the MLE3m
Lesson 4.4 Computing the MLE: examples4m
Introduction to R6m
Plotting the likelihood in R4m
Plotting the likelihood in Excel4m
Lesson 5.1 Inference example: frequentist4m
Lesson 5.2 Inference example: Bayesian6m
Lesson 5.3 Continuous version of Bayes' theorem4m
Lesson 5.4 Posterior intervals7m
5개의 읽기 자료
Module 2 objectives, assignments, and supplementary materials3m
Background for Lesson 410m
Supplementary material for Lesson 45m
Background for Lesson 510m
Supplementary material for Lesson 510m
4개 연습문제
Lesson 48m
Lesson 5.1-5.218m
Lesson 5.3-5.416m
Module 2 Honors6m
3
완료하는 데 2시간 필요

Priors and Models for Discrete Data

9개 동영상 (총 66분), 2 readings, 4 quizzes
9개의 동영상
Lesson 6.2 Prior predictive: binomial example5m
Lesson 6.3 Posterior predictive distribution4m
Lesson 7.1 Bernoulli/binomial likelihood with uniform prior3m
Lesson 7.2 Conjugate priors4m
Lesson 7.3 Posterior mean and effective sample size7m
Data analysis example in R12m
Data analysis example in Excel16m
Lesson 8.1 Poisson data8m
2개의 읽기 자료
Module 3 objectives, assignments, and supplementary materials3m
R and Excel code from example analysis10m
4개 연습문제
Lesson 612m
Lesson 715m
Lesson 815m
Module 3 Honors8m
4
완료하는 데 3시간 필요

Models for Continuous Data

9개 동영상 (총 69분), 5 readings, 5 quizzes
9개의 동영상
Lesson 10.1 Normal likelihood with variance known3m
Lesson 10.2 Normal likelihood with variance unknown3m
Lesson 11.1 Non-informative priors8m
Lesson 11.2 Jeffreys prior3m
Linear regression in R17m
Linear regression in Excel (Analysis ToolPak)13m
Linear regression in Excel (StatPlus by AnalystSoft)14m
Conclusion1m
5개의 읽기 자료
Module 4 objectives, assignments, and supplementary materials3m
Supplementary material for Lesson 1010m
Supplementary material for Lesson 115m
Background for Lesson 1210m
R and Excel code for regression5m
5개 연습문제
Lesson 912m
Lesson 1020m
Lesson 1110m
Regression15m
Module 4 Honors6m
4.6
455개의 리뷰Chevron Right

21%

이 강좌를 수료한 후 새로운 경력 시작하기

15%

이 강좌를 통해 확실한 경력상 이점 얻기

Bayesian Statistics: From Concept to Data Analysis의 최상위 리뷰

대학: GSSep 1st 2017

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.

대학: JHJun 27th 2018

Great course. The content moves at a nice pace and the videos are really good to follow. The Quizzes are also set at a good level. You can't pass this course unless you have understood the material.

강사

Avatar

Herbert Lee

Professor
Applied Mathematics and Statistics

캘리포니아대학교 산타크루스캠퍼스 정보

UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience....

자주 묻는 질문

  • 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.

  • 수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • You should have exposure to the concepts from a basic statistics class (for example, probability, the Central Limit Theorem, confidence intervals, linear regression) and calculus (integration and differentiation), but it is not expected that you remember how to do all of these items. The course will provide some overview of the statistical concepts, which should be enough to remind you of the necessary details if you've at least seen the concepts previously. On the calculus side, the lectures will include some use of calculus, so it is important that you understand the concept of an integral as finding the area under a curve, or differentiating to find a maximum, but you will not be required to do any integration or differentiation yourself.

  • Data analysis is done using computer software. This course provides the option of Excel or R. Equivalent content is provided for both options. A very brief introduction to R is provided for people who have never used it before, but this is not meant to be a course on R. Learners using Excel are expected to already have basic familiarity of Excel.

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.