Took some notes at the Pydata meetup tonight.

Schaun Wheeler from Valassis Digitial
How Design Skills are necessary in Data Science

Good Implementation Requires Good Design

• Know it when I see it vs here are the rules you must follow them

Having vision in your design

• Know your tools
• Knowing how it all goes together

Many of the technical aspects are being automated

• Most fundamental tools are now part of an api Design is taking the end goal

• organizing
• breaking down
• difficulty is in the conceptual experience

Skills

• Domain Expertise is the hardest part to learning
• Communication is required
• Design is a spectrum

Anthropologist gather stories and interpret them in a way that is understandable to the reader.

• Iterative process
• Work through the variations
• Each round the difference is smaller and smaller
• Each step is a difference in how the designer sees the world vs how others see the World

The only way data has a voice is if the tool is built to enable that.

• Tooling for design is an unexplored field.
• Developing data analysis soft skills will require tools that leverage those skills.

A focus on design is the best chance that data science has to survive scrutiny from a skeptical public.

Lightning Talks

Triangle Computer Vision Image Recognition Meetup

Discuss Cutting Edge Technology

• Last speaker was from iRobot
• Learning in Computer Vision and Image Processing
• Currently going over a Udacity course

Jade
Triangle Machine Learning Day

Nick
“All explanations are wrong, but some are useful.”
Nick Haynes (2018)
Slides