Tools I Use
As a data analyst, these are the main tools and technologies I work with daily:
Excel
Excel is where I begin. It is unassuming, but incredibly powerful. Before I write a single line of code, I use Excel to manually inspect raw data, spot obvious anomalies, and understand the shape of what I am working with. Pivot tables and quick formulas help me sanity-check my assumptions before I scale up
Structured Query Language
SQL is where the real heavy lifting happens. Whether I am pulling data from a cloud database or joining multiple tables to create a unified view, SQL is my bridge between raw storage and actionable analysis. I focus heavily on query efficiency, because the fastest analysis is useless if it takes ten minutes to run.
Python
Python enters the room when the data gets messy or the volume gets large. I rely on Pandas for cleaning and reshaping data that Excel simply cannot handle. More recently, I have been exploring exploratory data analysis in Python to uncover patterns I might miss in aggregated views
Power BI
Finally, Power BI is how I hand the insights back to organizations. A complex analysis means nothing if it is trapped inside a Jupyter Notebook. I use Power BI to build clean, interactive dashboards that let stakeholders ask their own follow-up questions. My goal is not just to show data, but to invite exploration.
R
R, on the other hand, is my go to when I need to think statistically. There is something about the RStudio environment that slows me down just enough to question my assumptions before making deductions.