“As much as 30% of the entire world’s stored data is generated in the health care industry. A single patient typically generates close to 80 megabytes each year in imaging and electronic medical record (EMR) data. This trove of data has obvious clinical, financial, and operational value for the health care industry, and the new value pathways that such data could enable have been estimated by McKinsey to be worth more than $300 billion annually in reduced costs alone…Read More”
NEJM Catalyst: Using It or Losing It? The Case for Data Scientists Inside Health Care by Marco D. Huesch, MBBS, PhD & Timothy J. Mosher, MD
A Practical Introduction to Factor Analysis: Exploratory Factor Analysis
Survey questions or “items” (e.g., on a scale from 1 to 5, how strongly do you agree with the following statement…) may be repeated measures of certain underlying “factors”. Where factors are a true underlying construct that a survey attempts to measure. For example, a factor a survey may attempt to measure might be the anxiety caused by learning statistical analysis (using SPSS software). Factor analysis looks at understanding what is really being measured by multiple questions in a survey.
There are a ton of new concepts in this class, but online resources are often a more simple and clear way to learn.
“Simpson’s paradox, or the Yule–Simpson effect, is a phenomenon in probability and statistics, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined. It is sometimes given the descriptive title reversal paradox or amalgamation paradox.”
This seems counterintuitive, but the 5 minute video below explains the concept well.
Wikipedia: Simpson’s paradox
Minute Physics: Simpson’s Paradox
The Best Free Data Science Courses on the Internet
Data science is blossoming as a field at the moment. Popular jargon from traditional statistics to new machine learning techniques are used colloquially in both online articles and day-to-day exchanges. One of the excellent things about data science, noted by David Venturi, is that by nature the field is computer-based. Why not learn about it all for free online then? Venturi has written several articles enumerating lists of massive open online courses (MOOC) relevant to someone interested in only a single highly-ranked data science class, or a complete masters degree in data science for the more dedicated individual. One of the benefits of these courses is they are more poignant and focus on only the knowledge relevant to applying data science skills. Another perk is the nonexistent price tag, as opposed to the tens or hundreds of thousands of dollars of student loans one could thrust themselves into while pursuing a data science masters at a formal institution. Venturi explains why he left grad school to learn about data science before finishing his first semester. If nothing else, some of these courses may be useful to supplement a graduate school education.
FreeCodeCamp.org: David Venturi
FreeCodeCamp.org: The best Data Science courses on the internet, ranked by your reviews
The Journal of the American Medical Association posted the most viewed research articles published in each of their medical specialty journals. They then interviewed the authors of each publication to determine why physicians and the public were so intrigued by their work.
NCBI: Six persistent research misconceptions by Kenneth Rothman