**6. Best practices to accelerate data science with Python**

People, process, and technology are contributing factors to data science complexity. Data scientists are expected to multiskilled resources knowing statistics, big data platforms, pipeline development, and deep learning neural networks. The primary process leveraged for data science is CRISP-DM which has not really changed since it was introduced in the 1990s. Lastly, there is so much available technology to use in data science it boggles the mind. While these problems will take time to solve, there are some best practices that can be leveraged to make data science less complex and more scalable in organizations. Best practices will be addressed in general as well as with Python.
