Thanks @JORGE4900 for the kind words.
Challenge #18 posed an interesting conundrum: the challenges have presented "solutions" that everyone works towards with the goal of reproducing them. This is a key difference between developing a software solution and predictions. Software solutions can take many paths to one goal while predictive modeling can take many paths with multiple predictive solutions, or probabilities. Development is about certainty--get me from here to there with this software--while predictive modeling is about taking many paths to a probabilistic answer, whether that's a value between 0 and 1 or a value prediction such as baseball wins.
I'd like to see Alteryx support the rise of the "citizen data scientist" and focus on more tools that emphasize the predictive modeling tools and newer functionality in the data science community such as natural language processing and deep learning, consistent support for cross-validation (today it's built into some tools and not others), fix the problems with the wonderful but flawed Model Comparison tool, add support for hierarchical (and even non-numeric) clustering and cluster validation--does this data actually support 4 clusters?--and many other areas.
As a practicing data scientist and leader of a team of 5, Alteryx can become one of the most powerful tools in our belts. It already is, but Alteryx support for the "process" of data science can make it even more powerful.
