Career path
Data Analyst
A data analyst turns raw data into decisions — pulling numbers, finding the story in them, and telling it clearly enough that someone acts. It's one of the most common entry points into data work, and a frequent landing spot for people switching from operations, finance, or marketing.
What the job actually is
Most of the work is asking a sharp question, getting the data, and answering it honestly. You'll write SQL to pull data, clean and reshape it, build charts and dashboards, and translate the result into a recommendation. The hard part is rarely the tools — it's framing the right question and not fooling yourself (or your stakeholders) with a misleading chart.
A typical day
A morning might be a stakeholder asking "why did signups drop last week?" You pull the data, segment it, rule out the obvious (a tracking bug, a holiday), and find the real driver. Afternoons skew toward building or maintaining dashboards, documenting definitions so numbers mean the same thing to everyone, and the occasional deeper analysis that takes a few days.
Skills that matter
- SQL — non-negotiable; it's how you get the data.
- A spreadsheet and a BI tool (Excel/Sheets plus something like Looker, Power BI, or Tableau).
- Clear communication — the analysis only counts if a decision-maker understands it.
- Basic statistics — enough to know when a difference is noise.
- A scripting language (often Python or R) helps as you grow, but isn't always required to start.
How to switch in
Analysts come from everywhere. If you're in operations, finance, or marketing, you likely already use data — the move is to formalise the SQL and visualisation skills and build a small portfolio of real analyses. Domain knowledge from your current field is an advantage, not something to discard: a marketer-turned-analyst understands marketing data better than a generalist.
Frequently asked questions
Do I need a degree to become a data analyst?
Often no. Employers increasingly weigh demonstrable SQL skills and a portfolio over a specific degree. A numerate background helps, but career-switchers from non-technical fields move into analytics regularly.
Data analyst vs data scientist — what's the difference?
Analysts focus on describing what happened and why, for decisions today. Data scientists lean more on statistics and machine learning to predict or model. Analyst is the more common entry point and many data scientists started there.