Now that you're proficient in many areas of data science with Python it's time to share your code and data with others. In this course you'll learn the fundamentals of sharing your data science assets. You'll learn how to leverage Anaconda Projects to package data, code, and conda environments into a single archive for other data scientists to run. You'll learn the basics of creating Python packages that provide importable modules. Finally, you'll learn how to write Conda recipes for your packages, build them, and share them on Anaconda Cloud.
Anaconda Projects allow you to package code, data, and Conda environments for others to use easily. Starting with with simple data science applications you'll create Anaconda Project archives that enable reproducible data science.
In this chapter you'll learn how to transform your Python scipts into modules and packages. You'll learn how to use setuptools to specify important metadata like version numbers and licenses.
Now that you have prepared your Python package using setuptools in this chapter you'll learn how to write a Conda recipe. Conda recipes describe the required Conda packages to build and run your package. You'll then build cross-platform packages and upload them to Anaconda Cloud.