Introducing data science and Python

Whether you are an eager learner of data science or a well-grounded data science practitioner, you can take advantage of this an essential introduction to Python for data science. You can use it to the fullest if you already have at least some previous experience in basic coding, in writing general-purpose computer programs in Python, or in some other data analysis-specific language such as MATLAB or R.  

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 the 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 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 Data science Course and grasp no matter what your background.

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.

Python for data science, providing you with a straight and fast route to solve various data science problems using Python and its powerful data analysis and machine learning packages. The code examples that are provided in this book don't require you to be a master of Python. However, they will assume that you at least know the basics of Python scripting, including data structures such as lists and dictionaries, and the workings of class objects. If you don't feel confident about these subjects or have minimal knowledge of the Python language, before reading this book, we suggest that you take an online tutorial at Python training