이 전문 분야 정보

최근 조회 84,767

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health.

In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around.

This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019.

The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data.

학습자 경력 결과
67%
이 특화 과정을(를) 수료한 후 새로운 경력을 시작함
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인 강좌
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 일정
유연한 마감을 설정하고 유지 관리합니다.
초급 단계
완료하는 데 약 4개월 필요
매주 3시간 권장
영어
자막: 영어
학습자 경력 결과
67%
이 특화 과정을(를) 수료한 후 새로운 경력을 시작함
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인 강좌
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 일정
유연한 마감을 설정하고 유지 관리합니다.
초급 단계
완료하는 데 약 4개월 필요
매주 3시간 권장
영어
자막: 영어

이 전문 분야에는 4개의 강좌가 있습니다.

강좌1

강좌 1

Introduction to Statistics & Data Analysis in Public Health

4.7
별점
538개의 평가
115개의 리뷰
강좌2

강좌 2

Linear Regression in R for Public Health

4.8
별점
220개의 평가
46개의 리뷰
강좌3

강좌 3

Logistic Regression in R for Public Health

4.8
별점
168개의 평가
30개의 리뷰
강좌4

강좌 4

Survival Analysis in R for Public Health

4.5
별점
137개의 평가
35개의 리뷰

제공자:

임페리얼 칼리지 런던 로고

임페리얼 칼리지 런던

석사 학위 취득 시작

This 특화 과정 is part of the 100% online Global Master of Public Health from 임페리얼 칼리지 런던. If you are admitted to the full program, your courses count towards your degree learning.

자주 묻는 질문

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • 3/4 hours a week for 3 to 4 months

  • The specialisation will assume no knowledge of statistics or R software.

  • We recommend taking the courses in the order in which they are displayed on the main page of the Specialization

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