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學生對 加州大学戴维斯分校 提供的 Analytical Solutions to Common Healthcare Problems 的評價和反饋

4.4
14 個評分
5 條評論

課程概述

In this course, we’re going to go over analytical solutions to common healthcare problems. I will review these business problems and you’ll build out various data structures to organize your data. We’ll then explore ways to group data and categorize medical codes into analytical categories. You will then be able to extract, transform, and load data into data structures required for solving medical problems and be able to also harmonize data from multiple sources. Finally, you will create a data dictionary to communicate the source and value of data. Creating these artifacts of data processes is a key skill when working with healthcare data....

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RT
2020年1月5日

Very good, although I would suggest the Health Informatics as a starting course

SC
2020年1月17日

Excellent material and a great introduction to data analytics!

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1 - Analytical Solutions to Common Healthcare Problems 的 5 個評論(共 5 個)

創建者 Rosetta T

2020年1月6日

Very good, although I would suggest the Health Informatics as a starting course

創建者 Silvio C

2020年1月18日

Excellent material and a great introduction to data analytics!

創建者 Abiodun A

2020年6月26日

Very informative. Would have preferred more practical examples on data analysis

創建者 JOEL B

2020年5月23日

Thank you ...

創建者 Debopam R

2020年10月30日

I had a lot of hope with this course but was sadly disappointed. The course content is all over the place and does not follow a structure or a flow. It talks about groupers in week 2, and talks on ETL in week 3, again brings risk stratification and some modelling concept in week 4 but linkages are missing.

My suggestion think how an analytics practitioner should follow a methodology and take him through the steps. It should start with SEMMA concept and take a practitioner through the steps like

S : what data is needed , concept and concept mapping, understand flow, input and output

E: explore : descriptive and visualizations

M: Modify: bring here concepts of grouper algorithms, risk adjustment

M : Talk here on predictive models, regressions, supervised and unsupervised learnings and risk stratification strategies

A: Talk about how you analyze output, how you train your learning set

Show with use causes scenarios of handling Fraud, Risk Adjustments and Risk Stratification.