Generally, the craft of programming is the factoring of a set of requirements into a a set of functions and data structures.
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 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.
The most important precedents deal with the whole idea of symbolic programming - the notion of setting up symbolic expressions that can represent anything one wants, and then having functions that operate on both their structure and content.
In C there are no data structures: there are pointers and pointer arithmetic. So you have a pointer into a data structure.
If you go into a room of 100-plus people, and you want to be the prince of darkness, you can be it. But I don't operate in the dark, I operate in the light.
There is artistic beauty to the way biology functions, nature functions, and science functions. I am trying to bring that kind of understanding in the design space.
So much of the physical world has been explored. But the deluge of data I get to investigate really lets me chart new territory. Genetic data from people living today forms an archaeological record of what happened to their ancestors 10,000 years ago.
Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.
Size doesn't matter, fast data is better than big data
If you take 10,000 chimpanzees and cram them together into Wembley Stadium or the Houses of Parliament, you will get chaos. But if you take 10,000 people who have never met before, they can co-operate and create amazing things.
Smart data structures and dumb code works a lot better than the other way around.
I personally think there's going to be a greater demand in 10 years for liberal arts majors than there were for programming majors and maybe even engineering, because when the data is all being spit out for you, options are being spit out for you, you need a different perspective in order to have a different view of the data.
There will be many cases when researchers will need to look at data to come closer to a cure, in maybe five years, 10 years, 15 years. We can help make that data analysis easier. We can't let this wait. Dementia has potential to cripple our economy.
In the next 10 years, data science and software will do more for medicine than all of the biological sciences together.
Everyone has an interest in the economy: in how it functions, how well it functions, and in whose interests it functions.