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.
More data beats clever algorithms, but better data beats more data.
As a digital technology writer, I have had more than one former student and colleague tell me about digital switchers they have serviced through which calls and data are diverted to government servers or the big data algorithms they've written to be used on our e-mails by intelligence agencies.
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.
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.
I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.
Once you see the problems that algorithms can introduce, people can be quick to want to throw them away altogether and think the situation would be resolved by sticking to human decisions until the algorithms are better.
The Europeans have lots of data on the use of adjuvanted flu vaccine in the elderly, but I dont think anybody has really good data on adjuvants in children.
Learn when and how to use different data structures and their algorithms in your own code. This is harder as a student, as the problem assignments you'll work through just won't impart this knowledge. That's fine.
Algorithms and data should support the human decision, not replace it.
Data science is the combination of analytics and the development of new algorithms.
There's a whole company called Palantir that does nothing but derive and create algorithms riches to search through big data. We're not using their capabilities. For heaven's sake, some of this is just ineptitude.
The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.
I really dont feel like Im in any kind of contest. Except, maybe, with myself. Just want to learn and create and grow. Get better all the time with these filmmaking tools. I dont expect perfection from myself. Just progress.
When Spotify launched in the U.S. in 2011, it relied on simple usage-based algorithms to connect users and music, a process known as 'collaborative filtering.' These algorithms were more often annoying than useful.
If someone stole your keys to encrypt the data, it didn't matter how secure the algorithms were.