There's a moment in every deep data analysis when the numbers start to feel like a story, and the analyst is standing right at that threshold. They've done the unglamorous work of unpivoting themes and counting frequencies, but the next question, which themes travel together, is where the real insight lives. This isn't just about adding another layer of complexity; it's about moving from inventory to interpretation.
The practical challenge here is one of vocabulary as much as technique. When you don't know that "co-occurrence analysis" is the term for what you're trying to do, a web search feels like wandering in the dark. That's not a failure of effort; it's a gap in the mental model. The good news is that the raw material is already in a usable shape. With unique course identifiers and each theme on its own line, the author has already built the foundation for a matrix that answers exactly the question they're asking. A simple self-join in Power Query, matching on course ID, will produce pairs of themes that share a course. From there, a pivot table turns those pairs into a count of how often any two themes appear together. No exotic software, no new skills required, just a shift in perspective.
What makes this worth pushing through is the payoff for their larger scan. Knowing that "business acumen" and "venture capital" co-occur in a third of relevant courses tells you something about how the discipline frames itself. Seeing which themes never share a line can be just as revealing, pointing to gaps in the curriculum or a deliberate separation of concerns. That's the kind of pattern that a frequency table hides and a co-occurrence matrix exposes. For a nationwide scan, this transforms a list of courses into a map of intellectual priorities across institutions.
The instinct to visualize is correct, and no specialized tool is needed for that either. A heatmap of the co-occurrence matrix, built directly from the pivot table, will show clusters and outliers at a glance. The vocabulary they're missing is "network graph," but a simple conditional formatting rule on the matrix can get them most of the way there. The next time they hit a wall in a spreadsheet, the question isn't "What tool do I need?" but "What shape does the answer take?" That framing turns a frustrating search into a solvable design problem. Start with the pair counts, build the matrix, and let the blanks and clusters ask the next question for you.