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Отзывы учащихся о курсе Identifying Patient Populations от партнера Система университетов штата Колорадо

4.8
Оценки: 15
Рецензии: 4

О курсе

This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud....

Лучшие рецензии

QQ

Jun 20, 2019

excellent course.\n\nThe first MOOC on computational pheonotying

AB

May 13, 2019

This is a well-presented course. I highly recommend.

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1–5 из 5 отзывов о курсе Identifying Patient Populations

автор: Angela B

May 13, 2019

This is a well-presented course. I highly recommend.

автор: qianmengxiao

Jun 20, 2019

excellent course.

The first MOOC on computational pheonotying

автор: Mor K

Jul 22, 2019

Great course, gives a solid understanding of computational phenotyping. Also teaches some R programming!

автор: William H

Apr 05, 2019

The instructor does a great job of providing hands-on teaching in addition to lecture. However, this course required a lot of knowledge of R, which wasn't provided in the introductory course.

автор: Vu T T T

Sep 14, 2019

A good course on identifying patient populations with R. However, some concepts/use of functions in R programming do not get explained and needs learning from other sources.

Course 4, 5 and 6 have not been made available so you might want to check their availability before planning to pursue the entire specialization.