Tools I Use & Trust
The exact stack I use on every data project — tested on real datasets, reviewed honestly, and recommended because they actually work.
These four tools handle 90% of everything I do as a data analyst.
My go-to for querying, filtering, aggregating, and analyzing data from relational databases. I use MySQL Workbench for all my SQL projects — it's where most of my portfolio work lives.
See My SQL Projects →My preferred tool for building interactive dashboards and sharing insights visually. Power BI connects directly to SQL databases which makes the SQL → dashboard workflow seamless.
See My Power BI Posts →Used for data cleaning, exploratory analysis, and visualization. Pandas is essential for wrangling messy datasets, and Matplotlib handles most of my custom charts.
See My Python Posts →Still the most versatile tool in any analyst's kit. I use Excel for quick exploratory work, pivot tables, and presenting findings to non-technical stakeholders.
See My Excel Posts →The courses, platforms, and datasets I used to build real skills — not just certificates.
Where I completed my SQL and data analytics courses. Their datasets are the foundation of my portfolio projects.
Visit Site →Where all my project code lives. Every SQL query, notebook, and analysis is version-controlled and publicly available.
See My Repos →The IDE I use for all SQL work. Free, powerful, and connects to any MySQL database with no configuration needed.
Download Free →A goldmine of free, real-world datasets for practice projects. Great for building your portfolio when you don't have company data.
Browse Datasets →My go-to for SQL interview prep. Real questions from companies like Meta, Amazon, and Google — free to practice.
Practice SQL →I use AI tools as a coding partner — for debugging queries, understanding error messages, and exploring different approaches.
Read My AI Posts →Want to see these tools in action?
Every project in my portfolio uses at least two of these tools on real datasets.