The most important skill for a data scientist is communication, especially regarding the ability to convey insights to stakeholders.

“I I mean when I when I talk about data science I often say like to me the most important skill of a data scientist is communication right”

Successful AI projects require deep alignment and understanding of specific business problems by the technical teams involved.

“I think the right approach is to take technical people and make sure that they understand the the specificity like the the idiosyncrasies of the business so that they can yeah and then they'll figure out all that other technical stuff right”

Data-driven communication is essential to provide an objective, factual, and protective analysis for the black community.

“I think I lead with data and I think that we live in a world, you know, the algorithms kind of dominate uh the conversations and a lot of viral videos, viral takes, viral tweets. um sometimes they're just viral because they're salacious or they're popular or they or they're just the popular opinion.”

Data professionals have a responsibility to use their work to help others make smart decisions and take actions.

“my view is that anyone who is working with data your responsibility is to not only help people interpret that and understand it but help them make smart decisions and actions based on it because you know that piece better than anyone else”

A career in data science is not just about coding, but about applying technical skills to solve specific problems for individuals.

“being a data scientist is not only about coding but it's also about coding in order to solve a particular problem for a specific person.”

Data scientists should prioritize the end-to-end process of scoping, data acquisition, and problem formulation over individual algorithm selection.

“I told Ken I was like hey like honestly like when I'm working day-to-day the algorithms don't really matter that much. It's like your endto-end process of like scoping the problem getting the right data making sure you're actually solving the right problem. That endto-end process I find it's more im”

Parents should prioritize understanding which data is based on real causal links versus simple correlations when evaluating parenting advice.

“So one is trying to help them understand which of the pieces of data they're seeing are real and which are not. So there's a lot of confusion of correlation and causation.”

Valid data science requires a focus on model evaluation and data preparation rather than just selecting complex models.

“I'm convinced that in many application setting what's most important is not the model you use but how you use it and how you evaluate it”

Automating repetitive data analysis tasks using machine learning can bridge the gap between data collection and business decision-making.

“really what this is is taking some of the most painful and repetitive parts of analysis and accelerating them with ml things like automated feature engineering feature selection recommendations to go and help go beyond kind of what's going on with their data and use all of their data to understa”