Data and Analytics CV writing

Tie each piece of work to a decision or a measurable change. Instead of saying you built a sales dashboard, say who used it, what question it answered and what the business did differently as a result. If you cannot share figures, describe the direction and scale of the change. Impact written this way separates analysts from report producers.

SQL matters most for almost every analytics role, followed by a BI tool such as Power BI, Tableau or Looker, and Python or R for deeper analysis. After tools, employers look for statistics, data modelling and the ability to explain findings to non-technical colleagues. List the tools once in a skills section and prove them in your experience.

Yes, when you are early in your career or moving into data, and only if they are real analyses rather than tutorial exercises. Choose a project with a messy dataset, a clear question and a documented conclusion, and link to the code or write-up. Once you have production experience, personal projects should shrink to a line or disappear.

Yes. A data analytics CV emphasises reporting, business intelligence and decision support. A data science CV emphasises modelling, experimentation and machine learning, with evidence that models reached production or informed a real decision. Many roles blend both, so read the job advert carefully and lead with whichever side it weights more heavily.