Data science vs signal processing

1. Is signal processing part of data science?
2. Is data processing same as data science?
3. Is signal processing still relevant?
4. What is the difference between signal processing and machine learning?

Is signal processing part of data science?

The interaction of data science and technology with the world is via signal processing: detecting, transcoding, understanding and generating time-dependent and space-dependent signals in the broadest sense. This includes signals in optical, electrical, acoustic, chemical, biological, textual, and social media.

Is data processing same as data science?

Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output.

Is signal processing still relevant?

Analog signal processing is still relevant for many real world applications and is always the first step even when sampling and discretizing the signal for further digital processing.

What is the difference between signal processing and machine learning?

We see that machine learning can do what signal processing can, but has inherently higher complexity, with the benefit of being generalizable to different problems. The signal processing algorithms are optimal for the job in terms of complexity, but are specific to the particular problems they solve.