Dataset source documentation is good to keep when you are doing an analysis with data from multiple datasets. Read my blog to learn how easy it is to throw together some quick dataset source documentation in PowerPoint so that you don’t forget what you did.
Shapes and images in dataviz, if chosen wisely, can greatly enhance the communicative value of the visualization. Read my blog post for tips in selecting shapes for data visualizations!
Data science coaching for the right person at the right time can empower them to quickly turn around their grades in college, immediately wrap up a portfolio project, or springboard their career into management or higher! Read my blog to see how.
Table editing in R is easier than in SAS, because you can refer to columns, rows, and individual cells in the same way you do in MS Excel. Read my blog post for example R table editing code.
R for logistic regression in health data analytics is a reasonable choice, if you know what packages to use. You don’t have to use SAS! My blog post provides you example R code and a tutorial!
Portfolio project examples are sometimes needed for newbies in data science who are looking to complete independent projects. This blog post provides some great examples of independent projects you can do with datasets available online!
Internship strategy for data science is not obvious, and even if you are in a college program, they often expect you to find your own internship. Download our internship strategy guide and get the experience you want!
“Rapid application development” (RAD) refers to an approach to designing and developing computer applications. In public health and healthcare, we are not taught about application development – but it’s good for us to learn about it, since we have to deal with data from health applications. My blog post talks about the RAD approach I […]