Source: RPubs – Research
Super helpful list of R packages. Looking forward to checking out tidycensus.
Useful R packages in a handy searchable table
A neat way to visualize overlap in data between multiple datasets.
Source: Program Overlap Matrix
Guest post by Khushbu Shah The most common question asked by prospective data scientists is – “What is the best programming language for Machine Learning?” The answer to this question always results in a debate whether
Job Details: The IBM Science for Social Good initiative is an o
I’ve been reading this book over the past few years and recently picked it up again. It is hard to put it down. The author provides an honest discussion on the limits of prediction due to ontological and epistemological factors There is lots in here for systems thinkers, modelers and “Big Data” analysts.
Source: Predicting the Future
A few selected quotes.
“More data help only if they are representative of what we want to study.”
“No algorithm can overcome a question that is too broad or misplaced.”
“…it is difficult for nonexpert data consumers to know which methods to use to answer a question. As a consequence, assembly-line analysis is common; methods that amount to mass production are used over and over. And too many investigators count on the size of the data pool to straighten out any problems, which doesn’t always happen.”
I can see lots of applications of this concept (XAI) where AI and related methods (e.g. ML) are currently being applied to public health and healthcare issues.