Preface

	"Data analysis is Python's killer app."
	--Unknown

Data analysis has a rich history in the natural, biomedical, and social sciences. You may have heard of Big Data. Although, it's hard to give a precise definition of Big Data, we should be aware of its impact on data analysis efforts. Currently, we have the following trends associated with Big Data:

• The world's population continues to grow
• More and more data is collected and stored
• The number of transistors that can be put on a computer chip cannot grow indefinitely
• Governments, scientists, industry, and individuals have a growing need to learn from data

Data analysis has gained popularity lately due to the hype around Data Science. Data analysis and Data Science attempt to extract information from data. For that purpose, we use techniques from statistics, machine learning, signal processing, natural language processing, and computer science.

A mind map visualizing Python software that can be used for data analysis can be found at http://www.xmind.net/m/WvfC/. The first thing that we should notice is that the Python ecosystem is very mature. It includes famous packages such as NumPy, SciPy, and matplotlib. This should not come as a surprise since Python has been around since 1989. Python is easy to learn and use, less verbose than other programming languages, and very readable. Even if you don't know Python, you can pick up the basics within days, especially if you have experience in another programming language. To enjoy this book, you don't need more than the basics. There are plenty of books, courses, and online tutorials that teach Python.