Cloud-scale Python enablement for data teams
Mock curricula for teams scaling pandas, Dask, Xarray, ML, and batch jobs on cloud hardware without platform drag.
Jordan Lee
Senior Data Platform Engineer
Active courses
4
Three courses and one certificate are in progress.
Completion
52%
Demo progress is seeded and updates locally.
Team rank
#3
Based on the mocked leaderboard.
Featured curriculum
A compact sales-demo catalog, backed by the Databricks bundle.
From pandas to Dask DataFrames
Teach teams when Dask DataFrame helps, how partitions work, and how to keep familiar pandas workflows scalable.
Parquet, Partitioning, and Cloud Data Layout
Design cloud data layouts that make Dask DataFrame and Xarray workflows efficient and predictable.
Task Graphs and Custom Workloads
Teach delayed, futures, map_partitions, graph size, and when to customize beyond high-level collections.
Dask DataFrame Practitioner
Move pandas-style workflows into partitioned, efficient Dask DataFrame pipelines.
Open path
Coiled Cloud Operator
Operate Dask clusters and Python jobs on cloud hardware with observability and cost control.
Open path
Cloud ML with Dask and Coiled
Scale batch prediction, HPO, experiment tracking, and GPU development without rewriting local workflows.
Open path