Classification crosswalks are easy to make, and can help you reduce cardinality in categorical variables, making for insightful data science portfolio projects with only descriptive statistics. Read my blog post for guidance!
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!
Connecting SAS to other applications is often necessary, and there are many ways to do it. Read this blog post for a couple of use-cases of SAS data integration using various SAS components.
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!
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.
End-to-end AI pipelines are being created routinely in industry, and one complaint is that academics can only contribute to one component of the pipeline. Really? Read my blog post for an alternative viewpoint!