ByteRot: Rise of the Scientific Programmer

http://byterot.blogspot.nl/2015/01/future-of-programming-rise-of-the-scientific-developer-bigdata-datascience-machine-learning-and-fall-of-the-craftsman.html

Python is a very popular language in the academic space. It is easy to pick up works everywhere and has some functional aspects making it terse. But that is not all: it sits on top of a huge wealth of scientific libraries and it can talk to Java and C as well. Industry innovations have started to come straight from the Universities. From the early 2000s where the academia seemed completely irrelevant to now where it leads the innovation.PySpark has come fully from the heart of Berkeley's University. Many of the contributors to Hadoop code and its wide ecosystem are in the academia.

We are now in need of people who can scientifically argue about algorithms and data (is coding anything but code+data?) and most of them could implement an algorithm given the paper or mathematical notation. And guess what, this is the trend for jobs with "Machine Learning":

Trend of jobs containing "Machine Learning" - Source: ITJobsWatch

And this is really not just Hadoop. According to the source above Machine learning jobs have had 41% rise from 2013 to 2014 while hadoop jobs had only 16%.

This Deep Learning thing is real. It is already here. All those existing algorithms need to be polished and integrated with the new concepts and some will be just replaced. If you can give interactions of a person with a site to a deep network, it can predict with a high confidence whether they are gonna buy, leave or indecisive. It can find patterns in diseases that we as humans cannot. This is what we were waiting for (and we were afraid of?). Machine intelligence is here.