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Essential Causal Inference Techniques for Data Science(으)로 돌아가기

Coursera Project Network의 Essential Causal Inference Techniques for Data Science 학습자 리뷰 및 피드백

4.6
별점
29개의 평가

강좌 소개

Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference. In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science called double selection and causal forests. These will help you rigorously answer questions like those above and become a better data scientist!...

최상위 리뷰

필터링 기준:

Essential Causal Inference Techniques for Data Science의 6개 리뷰 중 1~6

교육 기관: Keerat K G

2021년 1월 31일

교육 기관: Tom B

2021년 4월 16일

교육 기관: Chiara L

2022년 3월 10일

교육 기관: Sasmito Y H

2022년 9월 19일

교육 기관: Nersu A

2022년 8월 19일

교육 기관: seyed r m

2022년 2월 3일