Introducing data science and Python

Data science is a relatively new knowledge domain, though its core components have been studied and researched for many years by the computer science community. These components include linear algebra, statistical modelling, visualization, computational linguistics, graph analysis, machine learning, business intelligence, and data storage and retrieval.

Being a new domain, you have to take into consideration that currently the frontier of data science is still somewhat blurred and dynamic. Because of its various constituent set of disciplines, please keep in mind that there are different profiles of data scientists, depending on their competencies and areas of expertise.

In such a situation, what can be the best tool of the trade that you can learn and effectively use in your career as a data scientist? We believe that the best tool is Python, and we intend to provide you with all the essential information that you will need for a fast start.

Also, other tools such as R and MATLAB provide data scientists with specialized tools to solve specific problems in statistical analysis and matrix manipulation in data science. However, only Python completes your data scientist skill set. This multipurpose language is suitable for both development and production alike and is easy to learn and grasp, no matter what your background or experience is.

Created in 1991 as a general-purpose, interpreted, object-oriented language, Python has slowly and steadily conquered the scientific community and grown into a mature ecosystem of specialized packages for data processing and analysis. It allows you to have uncountable and fast experimentations, easy theory developments, and prompt deployments of scientific applications.

At present, the Python characteristics that render it an indispensable data science tool are as follows:

• Python can easily integrate different tools and offer a truly unifying ground for different languages (Java, C, Fortran, and even language primitives), data strategies, and learning algorithms that can be easily fitted together and which can concretely help data scientists forge new powerful solutions.
• It offers a large, mature system of packages for data analysis and machine learning. It guarantees that you will get all that you may need in the course of a data analysis, and sometimes even more.
• It is very versatile. No matter what your programming background or style is (object-oriented or procedural), you will enjoy programming with Python.
• It is cross-platform; your solutions will work perfectly and smoothly on Windows, Linux, and Mac OS systems. You won't have to worry about portability.
• Although interpreted, it is undoubtedly fast compared to other mainstream data analysis languages such as R and MATLAB (though it is not comparable to C, Java, and the newly emerged Julia language). It can be even faster, thanks to some easy tricks that we are going to explain in this book.
• It can work with in-memory big data because of its minimal memory footprint and excellent memory management. The memory garbage collector will often save the day when you load, transform, dice, slice, save, or discard data using the various iterations and reiterations of data wrangling.
• It is very simple to learn and use. After you grasp the basics, there's no other better way to learn more than by immediately starting with the coding.