In this chapter, we have discussed predictive data analytics, modeling and validation, some useful datasets, time series analytics, how to predict future events, seasonality, and how to visualize our data. For Python packages, we have mentioned prsklearn and catwalk. For R packages, we have discussed datarobot, LiblineaR, andeclust. For Julia packages, we explained EQuantEcon. For Octave, we have explained ltfat.
In the next chapter, we will discuss Anaconda Cloud. Some topics include the Jupyter Notebook in depth, different formats of the Jupyter Notebooks, how to share notebooks with your partners, how to share different projects over different platforms, how to share your working environments, and how to replicate others' environments ...
