As a data science leader, what should you put in place so your organization doesn’t end up a data mess like startup Theranos? This blog posts provides guidance.
Tag Archives: big data
This blog post talks about how lack of product description led to data-related misconduct at Theranos, because they could never nail down exactly what they were trying to do.
If you are not sure if you will like doing research in healthcare, instead of starting with big data, start with data collection and get to know the data as it comes into the dataset.
Monika posts her “data science newbie do-it-yourself starter kit”, with links to cheap or free learning resources for the data science newbie who wants to get started in healthcare analytics.
Wondering what the Plan-Do-Study-Act (PDSA) Model is, and if you should adopt it for quality improvement in healthcare? Read my series of blog posts on the subject for my personal experience and recommendations
While other countries have found a way to control their community spread of COVID-19 while waiting for the vaccine program to be implemented, the United States has totally failed at this. An epicurve is a diagram of the timing of an outbreak, and in other countries, this curve has been flattened. But in the United […]
We experience artificial intelligence all the time on the internet in terms of friend suggestions on social media, internet ads that reflect what we have been searching for, and “smart” recommendations from online stores. But the reality is that even the people who build those formulas cannot usually explain why you were shown a certain […]
Get to know three of my favorite SAS documentation pages: the one with sort order, the one that lists all the SAS formats, and the one that explains all the SAS operators and expressions!
If you receive payroll in the US, you can see that the data on the payroll stub is pretty complicated. This course in payroll is helpful for data scientists who find themselves analyzing US payroll data, because it explains the business rules and regulations behind the data.
SAS is known for big data and data warehousing, but how do you actually design and build a SAS data warehouse or data lake? What datasets do you include? How do you transform them? How do you serve warehouse users? How do you manage your developers? This book has your answers!