Can data science tools replace data scientists before this field has even taken flight?
Many startups and established companies are building tools that enable users to enact data science by interacting with data at a high level. We will debate the proposition, "In the future, data scientists will be replaced by data science tools." The goal will be to explore the definitions and assumptions data scientists and people who develop tools about the line between best practices and human expertise.
Ultimately, the panel will explore what unique attributes data scientists and tools bring to the field of data science.
Questions Answered
What tools do data scientists need? More specifically, what tools improve productivity and can tools keep data scientists from making mistakes?
How do the tools that data scientists use define them? More specifically, how do they choose the tools, in which ways do they depend on them and do they credit tools with any of their successes.
What are the most human parts of data science, and what parts of data science can be mechanized without loss and what can be improved with mechanization? Essentially, what aspects of a data scientists bag of skills and tools can and can not be replaced with software?
How much and what type of data science expertise can be built into tools? What is your experience with tools that allow domain experts with little programming and minimal statistics experience or training to perform analysis that results in new products, better business decisions, new insights into customer or partner behavior?
What is the future of data and statistics tools in 6 months, 5 years and 10 years? How will these tools evolve, and what capabilities will be added? Predict a surprise feature or capability we will see in data/analysis tools in the far future.
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