Defaults in PowerPoint are set up for slides – not data visualizations. Read my blog post for tips on reconfiguring PowerPoint to make it easy for dataviz!
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.
Understanding legacy data is necessary if you want to analyze datasets that are extracted from old systems. This knowledge is still relevant, as we still use these old systems today, as I discuss in my blog post.
GitHub beginners – even in data science – often feel intimidated when starting their GitHub accounts and trying to interact with the web page. Don’t be shy! Catch the highlights from a recent GitHub beginners workshop I held!
Time series plots in R are totally customizable using the ggplot2 package, and can come out with a look that is clean and sharp. However, you usually end up fighting with formatting the x-axis and other options, and I explain in my blog post.
GPower case example shows a use-case where we needed to select an outcome measure for our study, then do a power calculation for sample size required under different outcome effect size scenarios. My blog post shows what I did, and how I documented/curated the results.
A/B testing seems straightforward, but there are a lot of picky details. What A and B conditions do you actually test? How long do you run the test? How do you calculate the statistics for the test? Answer your questions by taking this LinkedIn Learning course.