diff --git a/content/en/learn.md b/content/en/learn.md index 8b3f83211e..7fdf4c00d1 100644 --- a/content/en/learn.md +++ b/content/en/learn.md @@ -3,7 +3,7 @@ title: Learn sidebar: false --- -The official NumPy documentation lives [here](https://numpy.org/doc/stable). +**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). *** @@ -30,7 +30,7 @@ There's a ton of information about NumPy out there. If you are new, we'd strongl * [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) * [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* -You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy". Most books there are about the "SciPy ecosystem", which has NumPy at its core. +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. **Videos** diff --git a/layouts/partials/data-science.html b/layouts/partials/data-science.html index c3993f55d8..775f7d695a 100644 --- a/layouts/partials/data-science.html +++ b/layouts/partials/data-science.html @@ -47,7 +47,7 @@

For high data volumes, Dask and Ray are designed to scale. Stable production - environments rely on data versioning (DVC), + deployments rely on data versioning (DVC), experiment tracking (MLFlow), and workflow automation (Airflow and Prefect).