courseraАнализ данных

Essential Causal Inference Techniques for Data Science

Курс от Coursera

Начальный≈ 2.4 чАнглийский

О курсе

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!

Навыки, которые вы освоите

Regression Analysis Machine Learning Machine Learning Methods Statistical Methods Data Science R Programming Applied Machine Learning R (Software)Statistical Inference Statistical Machine Learning Data-Driven Decision-Making Statistical Programming

Программа курса

1 модулей · 5 учебных материалов

01Essential Causal Inference Techniques for Data Science5 материалов

Essential Causal Inference Techniques for Data Science

Project OverviewЧтение Causal Inference Guided ProjectЛабораторная Causal Inference Practice QuizЗадание

Учитесь у экспертов

Vinod Bakthavachalam

Data Scientist