DS 101: Alteryx for Citizen Data Scientists

How to leveThese days most companies are moving towards the concept of “Citizen Data Scientists” by giving tools to subject matter experts (SME) to create their own machine learning models. The advantage of this approach is that it provides a meaningful interpretation of the results and nothing gets lost in translation between the data scientist and the SME.rage the power of Alteryx to analyze your data without the need for programming

These days most companies are moving towards the concept of “C_itizen Data Scientists_” by giving tools to subject matter experts (SME) to create their own machine learning models. The advantage of this approach is that it provides a meaningful interpretation of the results and nothing gets lost in translation between the data scientist and the SME.

While some citizen-data-scientists get trained in Python and R to develop models, these languages require some level of coding that some people may not be comfortable with. Data modeling platforms like JMP, Knime and Alteryx provide alternatives to programming and help you develop machine learning models simply by working with their inbuilt functionalities. In this article let’s discuss modeling with Alteryx.

The approach we will use here is the ‘SEMMA_’ approach. ‘_SEMMA’ _stands for _Sample, Explore, Modify, Model, and Assess. It is a list of sequential steps developed by the SAS Institute, one of the largest producers of statistics and business intelligence software. As with my previous articles, I will be using the Boston Housing Property 2019 dataset to model with Alteryx.

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