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. Its components include linear algebra, statistical modeling, visualization, computational linguistics, graph analysis, machine learning, business intelligence, and data storage and retrieval.
Data science is a new domain and you have to take into consideration that currently its frontiers are still somewhat blurred and dynamic. Since data science is made of various constituent sets of disciplines, please also 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 quick start.
In addition, 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, Python really completes your data scientist skill set. This multipurpose language is suitable for both development and production alike; it can handle small- to large-scale data problems and it is easy to learn and grasp no matter what your background or experience is.
Created in 1991 as a general-purpose, interpreted, and 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 development, and prompt deployment of scientific applications.
At present, the core Python characteristics that render it an indispensable data science tool are as follows:
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.
Python can easily integrate different tools and offers a truly unifying ground for different languages, data strategies, and learning algorithms that can be fitted together easily and which can concretely help data scientists forge powerful solutions. There are packages that allow you to call code in other languages (in Java, C, Fortran, R, or Julia), outsourcing some of the computations to them and improving your script performance.
It is very versatile. No matter what your programming background or style is (object-oriented, procedural, or even functional), you will enjoy programming with Python.
It is cross-platform; your solutions will work perfectly and smoothly on Windows, Linux (even on small-sized distributions, suitable for IoT on tiny-PCs like Raspberry Pi, Arduino and so on), and Mac OS systems. You won't have to worry all that much 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). Moreover, there are also static compilers such as Cython or just-in-time compilers such as PyPy that can transform Python code into C for higher performance.
It can work with large in-memory 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 various iterations and reiterations of data wrangling.
It is very simple to learn and use. After you grasp the basics, there's no better way to learn more than by immediately starting with the coding.
Moreover, the number of data scientists using Python is continuously growing: new packages and improvements have been released by the community every day, making the Python ecosystem an increasingly prolific and rich language for data science.