A Quote by Hilary Mason
Data science is the combination of analytics and the development of new algorithms.
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We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.
Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
The company started in the early 90s or late 80s. We were a behavioural science company. We didn't pivot into data analytics till 2012. So, all the data that we collected pre-2012, which was done by the British company SBL group, was collected through quantitive and qualitative research on the ground.
Incorporating science, technology, engineering, analytics and medicine to athletes' training and development not just at elite level but basing it right at the grassroots level is important.
The creative folks intuitively design what's best for the user, while data folks provide great insights. The true unicorns are those who can go end-to-end designing, building, measuring, analyzing, and iterating with a combination of user intuition and deep analytics.
Data is the new science. Big Data holds the answers. Are you asking the right questions?
The key to a solid foundation in data structures and algorithms is not an exhaustive survey of every conceivable data structure and its subforms, with memorization of each's Big-O value and amortized cost.
Computer science only indicates the retrospective omnipotence of our technologies. In other words, an infinite capacity to process data (but only data -- i.e. the already given) and in no sense a new vision. With that science, we are entering an era of exhaustivity, which is also an era of exhaustion.
I feel like if we can use the combination of basically data-driven hunches and bet on really first-class talent to deliver the shows, that I think we could do as well as the networks do, who basically have a 75 to 80 percent failure rate for new shows anyway - even after all that development and pilot work.
AIs are only as good as the data they are trained on. And while many of the tech giants working on AI, like Google and Facebook, have open-sourced some of their algorithms, they hold back most of their data.
At Deloitte, our programs for veterans are bringing new approaches to the table. For instance, we're helping veterans' organizations use data analytics to sift through streams of information about veteran needs.
Data-intensive graph problems abound in the Life Science drug discovery and development process.
Economics is not an exact science. It's a combination of an art and elements of science. And that's almost the first and last lesson to be learned about economics: that in my judgment, we are not converging toward exactitude, but we're improving our data bases and our ways of reasoning about them.
You have to imagine a world in which there's this abundance of data, with all of these connected devices generating tons and tons of data. And you're able to reason over the data with new computer science and make your product and service better. What does your business look like then? That's the question every CEO should be asking.
More data beats clever algorithms, but better data beats more data.
Alternative explanations are always welcome in science, if they are better and explain more. Alternative explanations that explain nothing are not welcome... Note how science changed those beliefs when new data became available. Religions stick to the same ancient beliefs regardless of the data.
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