Ebook: Practical Python Programming for Data Scientists

Data Science plays a very vital role in shaping up the process of transitioning data into information and into knowledge. As business enterprises, organizations, governments, IT companies, and service providers are keenly becoming data-driven, the role and responsibility of data scientists are bound to go up significantly. Python is emerging as the leading programming language for Data Science projects. The aim of the book is to clearly explain how Python simplifies and speeds up the realization of next-generation Data Science applications.

Data Science (DS) is a fast-emerging field of study and research. It leverages integrated data analytics (big, fast, and streaming analytics) platforms and Artificial Intelligence (AI) (machine and deep learning (ML/DL), computer vision (CV), and natural language processing (NLP)) algorithms extensively to extract actionable insights out of burgeoning data volumes in time.

Due to the ready availability of several libraries for facilitating the development of data science services, Python is turning out the programming language of choice for data science. The following libraries are enabling data science applications and are made available in Python:

1. NumPy: This is a library that makes a variety of mathematical and statistical operations easier and faster. This is also the basis for many features of the Pandas library.
2. Pandas: Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. This is one of the gamechangers for the tremendous success of data science projects.
3. Matplotlib: This is a visualization library that makes it quick and easy to generate charts from data.
4. Scikit-Learn: This is the most popular library for machine learning (ML) work in Python.

The book starts with a couple of chapters on data science and machine learning (ML) topics. Thereafter, the chapters are focusing on the fundamental and foundational aspects of Python programming language. All kinds of language constructs are accentuated and articulated for the benefit of programmers with all the practical details. There are dedicated chapters for producing machine learning applications. The gist of the book is to clearly explain how Python simplifies and speeds up the realization of next-generation data science applications. All the specific libraries towards data science are given the required thrust in order to empower our esteemed readers with all the right and relevant information. This book is being prepared with the intention of empowering data scientists with all the vital details about programming using the Python language.