When performing a tiny edit to the question, I looked at tags, and found that here, so far, only one tag contains
I consider the fields of signal processing, and image analysis, as quite composite. Almost everybody (my grand-mother as well) has some insight of what a chemist, a biologist is. In my time, explaining to my family what signal processing meant was a pain in the ASIC. I had to explain what a sensor was, and how a digital signal was composed. Then, after losing patience, I'd say I deal with mp3 and photoshop. Everybody then understands, but my so-called science suddently vanishes.
Signal processing, and image analysis, are composite. But not similar. They borrow from different other fields. In addition to the interest in low-level efficiency, I believe that a huge part of DSP is dedicated to more or less structured data, with the aim of extracting more or less "constructed" features.
Data science is an other umbrella term that encompasses even more than the two latter. From the wiki page:
[It draws] from many fields within the broad areas of mathematics,
statistics, information science, and computer science, including
signal processing, probability models, machine learning, statistical
learning, data mining, database, data engineering, pattern recognition
and learning, visualization, predictive analytics, uncertainty
modeling, data warehousing, data compression, computer programming,
artificial intelligence, and high performance computing.
Because it is fashionable, because with the advent of social media, people now know that there are data. And sounds, signals and images have becoming no more that "some data". Almost everybody gets what a data is: bits, unstructured. Or not.
This is funny: a lot asked me (in the past) to define a signal, no one ever asked me what a data is (while the question is more fundamental).
And everybody produces data, while few produced signals. The time was ripe. With the production of even more types of data, less structured, the features to be extracted became less and less "contructed", less "low-dimension" and hand-crafted, with interaction with machine learning.
Now, some of the features used in deep learning are less fine-designed than the usual ones, and the huge human-created tagged data compensate for that.