From controlling to Data Analyst

Controllers often compare plan and actual results, challenge an unexpected variance and turn a report into a discussion. Those are analytical skills. In applications, describe the decision you supported and the reasoning you used. Avoid presenting yourself as a beginner who happens to know Excel. You are adding data methods to existing commercial judgement.

A practical next step is SQL for retrieving and combining data, followed by a visualisation tool and basic data modelling. Learn enough Python only if it serves a real analysis task you want to automate or deepen. The goal is not to collect every tool. It is to create a reliable path from source data to an answer someone can check.

Choose a dataset that lets you frame a question a controller might ask: which segments changed, where did an operational measure diverge or what is driving a recurring issue? Show data cleaning, your measure definitions, the analysis and the limits of the conclusion. A generic dashboard without a decision context says less about your ability.

• Map your reporting experience to the questions an analyst role needs to answer.
• Learn SQL through queries that join and validate business data.
• Create two analysis projects with clear assumptions and documented results.
• Use vacancy language to adjust your examples and identify the next skill gap.