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NINTRODUCTION
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s 'RADUATEAND0H$STUDENTSINEXACTANDNATURALSCIENCESPHYSICSBIOLOGYANDCHEM-
istry) working on their thesis, dealing with large experimental data sets. The book also
appeals to students working on purely theoretical projects, as they require simulations
and means to analyze the results.
s 2$ENGINEERSINTHEFIELDSOFELECTRICALENGINEERING%%MECHANICALENGINEERINGAND
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.
s 0ROGRAMMERSANDCOMPUTERENTHUSIASTSUNFAMILIARWITH0YTHONANDTHE'.5,INUX
world, willing to dive into a new world of tools.
s (OBBYASTRONOMERSANDOTHERHOBBYISTSWHODEALWITHDATAANDAREINTERESTEDINUSING
Python to support their hobby.
The book can be appealing to these groups for different reasons. For scientists and engi-
neers, 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 he
or she must know how to manipulate files, install applications, view and edit files, and use
applications to generate reports and presentations. Background in numerical analysis, signal
processing, and image processing, as well as programming, is of help, but not required.
This book does not intend to serve as an encyclopedia of programming in Python and the
covered packages; nor does it try to be complete. It serves as an introduction to data analysis
and visualization in Python and covers most of the topics associated with that field.
How This Book Is Structured
The book is designed so that you can easily skip back and forth as you engage topics.
Chapter 1 is a case study introducing the topics discussed throughout the book: data anal-
ysis, data management, and, of course, data visualization. The case study involves reading GPS
data, analyzing it, and plotting it along with relevant annotations (direction of travel, speed,
etc.). A fully functional Python script will be built from the ground up, complemented with lots
of explanations. The fruit of our work will be an eye-catching GPS route.
If you’re new to data analysis and visualization, consider reading Chapter 2 first. The
chapter describes how to set up a development environment to perform the tasks associated
with data analysis and visualization in Python, including the selection of an OS, installing
Python, and installing third-party packages.
If you’re new to Python, your next stop should be Chapter 3. In this chapter, I swiftly
discuss the Python programming language. I won’t be overly rehashing basic programming
paradigms; instead I’ll quickly overview the Python programming building blocks.