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Signed-off-by: pryce-turner <[email protected]>

docs/user_guide/advanced_composition/nested_parallelization.md

@@ -185,6 +185,6 @@ While the top-level dynamic will be exactly the same as the previous example, he
 
 You can nest even further if needed, or incorporate map tasks if your inputs are all the same type. The design of your workflow should of course be informed by the actual data you're processing. For example if you have a big library of music that you'd like to extract the lyrics for, the first level can loop through all the albums and the second level can go through each song. 
 
-If you're just churning through an enormous list of the same input, it's typically best to keep the code simple and let the scheduler handle optmizing the execution. Additionally, unless you need the features of a dynamic workflow like mixing and matching inputs and outputs, it's usually most efficient to use a map task. This has the added benefit of keeping the UI clean.
+If you're just churning through an enormous list of the same input, it's typically best to keep the code simple and let the scheduler handle optimizing the execution. Additionally, unless you need the features of a dynamic workflow like mixing and matching inputs and outputs, it's usually most efficient to use a map task. This has the added benefit of keeping the UI clean.
 
 You can also choose to limit the scale of parallel execution at a couple levels. The `max_parallelism` attribute can be applied at the workflow level and will limit the number of parallel tasks being executed, this is set to 25 by default. Within map tasks specifically, you can indicate a `concurrency` argument which will limit the number of mapped tasks within running at any given time.