Hi #development Twitter & #GCDigital. I'm thinking of creating a gentle #Python-based learning session on how applications work for senior executives. I need your help.
My hypothesis == w/ hands-on practice of some simple programming fundamentals, senior folks can better understand modern applications, how they are developed & work + the value of #opensource. Maybe some agile.
This, in turn, leads to understanding the art of the possible, better questions on work being done, potentially practical support for building broader #programming & #DataScience capacity in their organizations & more open access to #opensource tools.
Saturday Keynote at #ODSCWest Carrie Grimes Bostock - Data Science & Automated Decision Making. Automation has inputs, model(s) and potential outputs for action.
What is the goal of your models? Interpretability is important, but not the only thing. Other measures exist, but understanding your model is important to prevent outcomes, override recommendations, and to connect models to other systems - or stacks of models & systems. #ODSCWest
Example: placing racks in the Santa Clara data centre. Considerations, restrictions on space, power, future use of space, etc. Also: People install racks. They need to be able to intervene based on info that the placement model doesn't have. #ODSCWest
Effective #data-driven teams: different in existing organizations than start-ups. Need to find the best places to build in data value without overturning everything else. Challenges: personal-level analytics, resource allocation, security, privacy. #ODSCWest
Business & data teams will see problems differently. Data teams need to be comfortable with trade-offs in business. Metrics can be ill-defined. Speed can be better than perfection. Important to understand adequate accuracy of models. #ODSCWest Oh yeah, and security.
Need an evolving understanding for what tools are available today to get your work done. If you want your business people to work with your #datascience team, they need to understand what data is and what it can do. Also helps to avoid chasing buzzwords and bling.
Industry starting to confront #ethics in terms of daily applications of #AI algorithms. #Bias based on data - in the case of courts, potentially hundreds of years of biased decisions as part of the training set.
"I'm just an engineer" not an appropriate response. People using #AI products might not understand what or how decisions and predictions are being made, but will be making real world decisions based on them. YOU and your team might be the only ones who do.
First up: @asuonline will be hosting an educational analytics conference in March 2019 focusing on what we can learn from learner data and how to structure learning to better meet learner needs. #ODSCWest
#AI differential - being able to tailor products to specific users == better able to predict value to and from users. E.g. cost to deliver value to user vs. cost to company to deliver that value. Allows ruthless competitive focus - @DataRobot#ODSCWest