1. Wrap-up
Congratulations, we've reached the end of this course! Let's take a moment to bask in what we've learned.
2. Recap - chapter 1
In chapter 1, we learned how to use lazy evaluation to build up a task graph using Dask, rather than running each calculation as soon as it appears in the script. This allowed us to run these calculations in parallel.
3. Recap - chapter 2
In chapter 2, we learned how to use Dask to analyze big structured data, like arrays, and DataFrames, which won't fit into our available memory. We discovered that what we already know about NumPy arrays and pandas DataFrames can be used on the Dask equivalents.
4. Recap - chapter 3
In chapter 3, we learned how to use Dask bags to perform almost any calculation which can't be expressed in structured data.
5. Recap - chapter 4
In chapter 4, we learned how to choose the type of parallelism used, and also how to use Dask for machine learning tasks.
6. Next steps
We couldn't cover all the methods of all the Dask collections. You can find more of these in the documentation.
7. Congratulations!
Congratulations again; if you have absorbed all the things in this course, you are well on your way to being an advanced Python user.