Although this book is about software, the target audience is not necessarily programmers or computer
scientists. I’ve assumed the reader’s main line of work is research or R&D, in his or her field of interest,
be it astrophysics, signal and image processing, or biology. The audience includes the following:
Graduate and PhD students in exact and natural sciences (physics, biology, and chemistry) working
on their thesis, dealing with large experimental data sets. The book also appeals to students working
on purely theoretical projects, as they require simulations and means to analyze the results.
R&D engineers in the fields of electrical engineering (EE), mechanical engineering, and chemical
engineering: engineers working with large sets of data from multiple sources. In EE more
specifically, signal processing engineers, communication engineers, and systems engineers will find
the book appealing.
Programmers and computer enthusiasts, unfamiliar with Python and the GNU/Linux world, but who
are willing to dive into a new world of tools.
Hobbyist astronomers and other hobbyists who deal with data and are interested in using Python to
support their hobby.
The book can be appealing to these groups for different reasons. For scientists and engineers, the book
provides the means to be more productive in their work, without investing a considerable amount of time
learning new tools and programs that constantly change. For programmers and computer enthusiasts, the
book can serve as an appetizer, opening up their world to Python. And because of the unique approach
presented here, they might share the enthusiasm the author has for this wonderful software world. Perhaps
it will even entice them to be part of the large and growing open source community, sharing their own
code.
It is assumed that the reader does have minimal proficiency with a computer, namely that he or she
must know how to manipulate files, install applications, view and edit files, and use applications to
generate reports and presentations. A background in numerical analysis, signal processing, and image
processing, as well as programming, is also helpful, but not required.
This book is not intended to serve as an encyclopedia of programming in Python and the covered
packages. Rather, it is meant to serve as an introduction to data analysis and visualization in Python, and it
covers most of the topics associated with that field.

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