Sue Desmond-Hellmann is using precision public health -- an approach that incorporates big data, consumer monitoring, gene sequencing and other innovative tools -- to solve the world's most difficult medical problems. It's already helped cut HIV transmission from mothers to babies by nearly half in sub-Saharan Africa, and now it's being used to address alarming infant mortality rates all over the world. The goal: to save lives by bringing the right interventions to the right populations at the right time.
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build.
This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).
