Summary

I hope this final chapter got you excited about all the directions you can take to further expand your data analytics toolbox. We took our first steps in Tableau and realized how similar it is, in its fundamental features, to Power BI. We have also gone through a friendly introduction to Python, the ubiquitous programming language in data science. As we integrated Python in KNIME, we have seen how to take the best from both the visual and coding programming worlds. As we did so, we took the opportunity to learn how to expand KNIME further by using its vast extensions base and leveraging the public KNIME Hub environment. Lastly, we got a quick tour through the attractive land of AutoML, being exposed to its promising ability to simplify the process of building high-performing machine learning models considerably.

In this chapter, we extended our toolbox by exploring new tools and approaches to run better data analytics in our everyday work. My advice is to make this a habit...