I had two main reasons for writing this book. When I first started learning data science,
I could not find a centralized overview of all the important topics on this subject.
A practitioner of data science needs to be proficient in at least one programming
language, learn the various aspects of data preparation and visualization, and also
be conversant with various aspects of statistics. The goal of this book is to provide
a consolidated resource that ties these interconnected disciplines together and
introduces these topics to the learner in a graded manner. Secondly, I wanted to provide
material to help readers appreciate the practical aspects of the seemingly abstract
concepts in data science, and also help them to be able to retain what they have learned.
There is a section on case studies to demonstrate how data analysis skills can be applied
to make informed decisions to solve real-world challenges. One of the highlights of
this book is the inclusion of practice questions and multiple-choice questions to help
readers practice and apply whatever they have learned. Most readers read a book and
then forget what they have read or learned, and the addition of these exercises will help
readers avoid this pitfall.
The book helps readers learn three important topics from scratch – the Python
programming language, data analysis, and statistics. It is a self-contained introduction
for anybody looking to start their journey with data analysis using Python, as it focuses
not just on theory and concepts but on practical applications and retention of concepts.
This book is meant for anybody interested in learning Python and Python-based libraries
like Pandas, Numpy, Scipy, and Matplotlib for descriptive data analysis, visualization,
and statistics. The broad categories of skills that readers learn from this book include
programming skills, analytical skills, and problem-solving skills.