Overview
Junior data analyst roles get hundreds of applications. Most resumes look identical: "proficient in Excel," "knowledge of SQL," "strong analytical skills." None of that tells a hiring manager what you can actually do with data. The resumes that stand out are the ones that show a finished analysis with a clear result.
This resume belongs to Ravi Patel, a mathematics and statistics graduate from the University of Warwick. He completed a data analytics internship at a retail company and built several personal projects using publicly available datasets. His resume works because every item shows an input, a method, and an output.
What Makes This Resume Work
Projects demonstrate end to end analysis. Ravi built a dashboard in Tableau that visualised UK housing price trends across 10 regions using Land Registry data. He also wrote a Python script that cleaned and analysed 50,000 rows of NHS waiting time data, identifying three trusts with statistically significant increases. These are complete projects with real datasets, not toy examples from a course.
The internship is results focused. During his three month placement, he wrote SQL queries to extract sales data for weekly reports used by four department managers. He automated a manual Excel reporting process using Python, reducing the time from three hours to twenty minutes. That automation metric alone is worth more than a paragraph of generic duties.
Technical skills are tiered. He separates his skills into categories: languages (Python, SQL, R), visualisation tools (Tableau, Power BI), databases (PostgreSQL, MySQL), and libraries (pandas, matplotlib, scikit learn). This structure helps recruiters quickly assess his technical fit without reading through every bullet point.
The education section includes relevant coursework. Statistical Methods, Data Mining, Linear Algebra, and Probability Theory are all listed. For a data analyst role, these modules signal that Ravi has the mathematical foundation to do more than just run queries. He understands what the numbers mean.
Key Takeaways
Build at least two data projects using real, publicly available datasets and put them on your resume. Describe the dataset size, the tools you used, and the insight you found. A finished analysis is the single best thing a graduate data analyst can show.
If you automated something during an internship or even during a university assignment, quantify the time saved. Employers hire analysts to make things faster and clearer. Show that you already know how to do both.
Organise your technical skills into clear categories. A recruiter scanning your resume for "SQL" or "Tableau" should find it in under three seconds.
