Most of the work is cleaning, not analyzing
A large share of real data work involves identifying duplicate records, handling missing values, and reconciling inconsistent formats before any meaningful analysis can happen. This is rarely glamorous, but it is where most analytical errors originate if skipped.
Fellows who get comfortable with this stage early tend to produce far more trustworthy results later.
Translating numbers into decisions
A chart by itself does not help a stakeholder make a decision. Fellows learn to frame findings around a specific question: what should we do differently because of this data?
This requires understanding the business context well enough to know which numbers actually matter, and which are just noise.
- Always tie an analysis back to a specific decision or question.
- Validate data sources before trusting the output of any query.
- Practice explaining technical findings to a non-technical audience clearly.
Building dashboards people actually use
A dashboard that looks impressive but gets opened once and never again has failed at its actual job. Fellows learn to design dashboards around the specific recurring questions a team has, rather than trying to show everything at once.
This is a skill built through iteration and feedback, not something that can be fully learned from a course alone.
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