Home / Skills / data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
What it is, and how people use it
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. It sits on the Dev Agents & Programs shelf of the catalog, maintained by @donnemartin.
Why builders keep it installedDev Agents & Programs benefits
These CLIs and dashboards solve one job well and install with a single command. Value shows up in the first hour, not after a config weekend.
Most of them ship an MCP server, skill or hooks - so the same tool you use manually becomes an action your agent can call safely.
Star counts here track repos people actually run daily: the issue history shows bug classes fixed under production pressure, not demo-week polish.
Every tool on this page ships under an open license and can be self-hosted. Your usage data and your configs stay yours.
Contributorsloading from GitHub…
Languagesestimated from snapshot
- Python 87.8%
- Shell 12.2%