Data Visualization teaches data analysis technique and the construction of dependable data models, working with multiple data sources through cleaning and integration, and then producing a range of visualizations. Daytona State's version uses Microsoft Excel and Power BI as the primary tools.
Within the SCNS taxonomy, CAP is the Computer Applications prefix. This course sits in the second year of an A.S. in data analytics, business intelligence, or computer information technology. Daytona State publishes it at 4 credits with prerequisite COP2700, offered in spring. It appears at approximately three Florida institutions.
The framing worth noticing is that this is a data preparation course as much as a charting course. The published description gives at least equal weight to cleaning and integrating multiple sources and to building reliable models — which is accurate to the profession, where the visualization is the last and smallest part of the work.
Florida course inventories carry this number as CAP2741C, the integrated lecture-and-laboratory form. Daytona State publishes CAP2741 without the C at 4 credits. The 80 contact hours reported here follows the 20-hours-per-credit convention this repository's integrated computing courses use consistently. SCNS equivalency does not cross numbers, and the suffix is part of the number — confirm the value on your own institution's catalog page.
Students arrive wanting to make dashboards and are frequently surprised that the majority of effort goes into getting the data into a usable state. That proportion is accurate to professional practice, and the published course description reflects it deliberately.
Three points worth internalizing:
The graphical integrity content is the part with the most professional weight. Most misleading visualizations are not deceptive by intent — they are produced by accepting a default.
The recurring offenders: a truncated y-axis that turns a two percent change into a dramatic cliff; dual axes scaled to manufacture a correlation; pie charts with too many slices, which humans cannot compare by angle; 3D effects that distort area; inconsistent time intervals on an axis; and colour scales that imply an ordering that does not exist.
Two habits: start bar charts at zero — bar length encodes magnitude, so a truncated axis lies by construction, while line charts showing change over time may legitimately be truncated; and ask what the reader will conclude, then check whether the data supports that conclusion. A chart that is technically accurate and predictably misread is a bad chart.
The related caution: correlation shown on a chart is still not causation, and a well-designed visualization is unusually persuasive, which makes the responsibility greater rather than smaller.
Worth flagging because students building portfolios for government and education employers will meet it. Colour alone should never carry meaning — roughly one in twelve men has some form of colour vision deficiency, and red/green encoding is the classic failure. Use shape, position, direct labelling, or texture in addition to colour, choose colourblind-safe palettes, and maintain adequate contrast.
Public agencies and educational institutions in Florida operate under accessibility obligations for the material they publish, which means a dashboard delivered to a state agency or a school district may be assessed against them. Building the habit now costs nothing.
Data analysis is being reshaped by AI-assisted tooling, and a current course should be direct about where it helps and where it fails.
Where it helps: generating formula and query expressions from a description, explaining an unfamiliar expression someone else wrote, suggesting chart types, drafting the narrative summary of a finding, and accelerating the cleaning of messy text fields. Natural-language query features are built into current BI platforms and genuinely lower the barrier for casual users.
Where it fails, and it matters here: AI tools cannot tell you whether your model is right. They will happily write a measure that aggregates across a broken relationship and return a confident number. They also cannot tell you whether a finding is meaningful or an artifact of how the data was collected — which is the actual analytical judgment. And data uploaded to a public AI service is disclosed: organizational data, and certainly anything containing personal information, must not go into a tool the employer has not approved.
What this means for the career: producing a chart is the part most exposed to automation. Knowing which question to ask, whether the data can answer it, and whether the answer is trustworthy is not. Build toward that.
Daytona State publishes CAP2741 at 4 credits with prerequisite COP2700, offered in spring. The 80 contact hours is derived at the 20-hours-per-credit convention for integrated computing courses — confirm on your syllabus. Assessment is typically project-based: given a messy dataset, produce a cleaned model and a working dashboard, and explain it. The explanation carries real weight, and students who can present a finding clearly outperform those who only build.
The first digit of an SCNS number denotes the year of offering, not transferability. Courses at the 1000 and 2000 levels transfer transparently between Florida public institutions, and 3000 to 4000 is unproblematic since both are upper division. The boundary that actually matters is 2000 to 3000, where lower-division credit generally cannot satisfy an upper-division requirement.
CAP2741C is 2000-level and will not substitute for an upper-division analytics course such as CAP3744 in a bachelor's program. Students continuing to a B.A.S. or B.S. should have it evaluated in writing.
Generated September 2, 2026 · Updated September 2, 2026