diff --git a/examples/fundamentals/data_container.ipynb b/examples/fundamentals/data_container.ipynb
index 916a4e097..730075b52 100644
--- a/examples/fundamentals/data_container.ipynb
+++ b/examples/fundamentals/data_container.ipynb
@@ -80,7 +80,7 @@
    "source": [
     "## Types of Data Containers\n",
     "\n",
-    " PyMC offers two data containers, depending on your needs: {func}`pymc.ConstantData` and {func}`pymc.MutableData`. Both will help you visualize how data fits into your model, store the data in an `InfereceData` for reproducibility, and give access to labeled dimenions. As the names suggest, however, only `MutableData` allows you to change your data. When `X` is `MutableData`, this enables out-of-sample inference tasks. When `y` is `MutableData`, it allows you to reuse the same model on multiple datasets to perform parameter recovery studies or sensitivity analysis.  These abilities do, however, come with a small performance cost.\n",
+    " PyMC offers two data containers, depending on your needs: {func}`pymc.ConstantData` and {func}`pymc.MutableData`. Both will help you visualize how data fits into your model, store the data in an `InferenceData` for reproducibility, and give access to labeled dimenions. As the names suggest, however, only `MutableData` allows you to change your data. When `X` is `MutableData`, this enables out-of-sample inference tasks. When `y` is `MutableData`, it allows you to reuse the same model on multiple datasets to perform parameter recovery studies or sensitivity analysis.  These abilities do, however, come with a small performance cost.\n",
     " \n",
     " In past versions of PyMC, the only data container was `pm.Data`. This container is still available for backwards compatability, but the current best practice is to use either `pm.MutableData` or `pm.ConstantData`. "
    ]