As a data scientist, you’re most likely extremely proficient in Python, R, and other coding skills, and they have always done the trick for you. So why bother with an analytics solution? Productivity, flexibility, efficiency, and faster results — that’s why.

The reality is, regardless of your skills, you’re most likely spending hours on ETL, workflow management, and production deployment, all of which are not really the core of a data scientist’s work.

We won’t bore you with the 101 of analytic models; instead, watch data engineers and analytic experts from Alteryx as they show you how to:

  • Speed up the pre-modeling data ETL stages
  • Increase productivity in the modeling stages
  • Enhance your value to the organization in post-modeling and production stages

The role of a data scientist puts you at the center of driving value for the organization with some of the most sought-after and precious resources in the organization.

Watch to see how a solution like Alteryx can deliver performance-enhancing efficiencies in your work and help you put the "science" back into data science.


  • Ulrich Bombka-82.jpg
    Ulrich Bombka

    Data Engineers

“The largest number of cases that keep data scientists occupied are quite 'simple,' so why not enable other people, like controllers or business analysts, to do their own research and analytics and free up the data scientists?”  

— Ulrich Bombka, Co-founder, Data Engineers

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