Data Analyst — Python, SQL, Power BI — end-to-end projects from raw data to insight

I did not come to data because spreadsheets sounded easy — I came because I enjoy turning vague questions into something testable. A number on its own is rarely interesting; what hooks me is the chain from definition → query → chart → decision. That is why my public work spans three different shapes of data: public mobility (Santander Cycles in R), structured sport results (Premier League in Python), and transactional retail (Apple global sales in MySQL with views and stored procedures).

Practically, that means I am comfortable owning a slice end-to-end: load and clean, document assumptions, explore, visualise, and (where SQL allows) leave the next analyst a procedure instead of a one-off SELECT. My GitHub is the proof surface — notebooks, R Markdown, and ordered SQL files you can open without my narration.

I work part-time on the floor at Aqua Shard while studying full-time, which sharpened pace, accuracy, and communication under pressure — the same habits I bring when presenting findings. I am looking for a Data Analyst internship (consulting-flavoured teams especially) where I can support client delivery with rigorous SQL/Python and clear reporting — including Power BI as I grow that stack (see upcoming work below).