Tag Archives: data collection

AI on the Edge: What it is, and Data Storage Challenges it Poses

AI on the edge refers to doing the AI processing and equations at the site of the object collecting the data.

“AI on the edge” was a new term for me that I learned from Marc Staimer, founder of Dragon Slayer Consulting, who was interviewed in a podcast. Marc explained how AI on the edge poses a data storage problem, and my blog post proposes a solution!

Convert CSV to RDS When Using R for Easier Data Handling

If you want to use R for a project and the source CSV is very big, it can improve input/output efficiency to convert the file to an RDS.

Convert CSV to RDS is what you want to do if you are working with big data files in R GUI and want to improve efficiency. Read my blog post for an explanation and video demonstrations of this process!

Counting Rows in SAS and R Use Totally Different Strategies

If you are a data scientist working with large datasets, you need to learn the commands to count both columns and rows in the dataset, whether you are using SAS or R.

Counting rows in SAS and R is approached differently, because the two programs process data in different ways. Read my blog post where I describe both ways.

Data Science YouTube Channel Planned Expansion for 2022 – Please Subscribe!

In 2022 I am going to be putting new content on my YouTube channel focused on teaching data science, and providing educational resources.

Data science YouTube channel that brings you educational resources, career advice, live interactive sessions, and keeps you up-to-date in innovation and analytics – that’s what I have planned for 2022! Read my blog post for details.

Native Formats in SAS and R for Data Are Different: Here’s How!

Why use particular data formats for different programming languages in statistics? Because the programs can then process the data faster and with more accuracy.

Native formats in SAS and R of data objects have different qualities – and there are reasons behind these differences. Learn about them in this blog post!

Paid Mentoring for Data Science: Is it Worth the Cost?

If you are wondering if you should set up a paid mentoring relationship with a data science mentor, you definitely want to read this. You want to make sure you get your money's worth.

Paid mentoring for data science can be worth the cost in certain circumstances – but not others. My blog post provides guidance on hiring a paid mentor.

SAS-R Integration Example: Transform in R, Analyze in SAS!

You can use SAS and R together in one project. I show you how to develop an analytic dataset in R and put it in SAS ODA for analysis.

Looking for a SAS-R integration example that uses the best of both worlds? I show you a use-case where I was in a hurry, and did transformation in R with the analysis in SAS!

Alternative to the PDSA Model for QA/QI in Healthcare? Old-fashioned Epidemiology and Biostatistics! Part 4 of 5

The Plan Do Study Act model does not take into account all functions of a healthcare quality improvement and assurance department

Want an alternative to the Plan-Do-Study-Act (PDSA) model for quality assurance/quality improvement (QA/QI) in healthcare? I recommend approaching QA/QI a different way, by thinking about the various functions of the QA/QI department.

“Bad Blood” Shows how Theranos was an Abject Failure in Data Stewardship: Part 3 of 5

You need governance in data science whether you are doing clinical research in a healthcare setting or in a laboratory.

The book “Bad Blood” describes the fall of startup unicorn Theranos, but also provides insight into the company’s abject failure at data stewardship, which I talk about in this blog post.

Quality Improvement in Healthcare: What is the PDSA Model, and How Well Does it Work for QA/QI? Part 1 of 5

Continuous quality improvement through conducting research projects to get evidence to inform change

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

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