When I meet other data scientists in the wild, my favorite question to ask is this: If you could get your hands on any dataset in the world, what would it be? For me, the answer is easy — dating app data. I've always been fascinated by how our
I've been using Jupyter notebooks for almost a decade to power everything from quick exploratory data analyses to end-to-end machine learning prototypes. While these notebooks have served their purpose, I'm sure that the majority of data scientists will agree that they also come with some headaches:
BigQuery is powerful — but it can also be expensive and slow if you don’t know what you’re doing. It's easy to accidentally scan terabytes of data when you only meant to query a few gigabytes. Mistakes like these can end up costing hundreds (and sometimes thousands)
Let's dig into what DuckDB is and why there's so much hype around it.
After working with BigQuery for almost five years, here are five features that I wish I knew about earlier in my career.
I'm back from a one year hiatus and I'm excited to bring this blog back to life.
Let's learn how to handle large data with Pandas, Polars, and DuckDB.
PyCon US is the largest Python conference in the world. This year, over two thousand people from around the world gathered at the David L. Lawrence Convention Centre in Pittsburgh, Pennsylvania to meet and talk about all things Python. PyCon runs over the course of one week - two days