Preface

Data analysis has a rich history in natural, biomedical, and social sciences. In almost every area of industry, data analysis has gained popularity lately due to the hype around Data Science. Data analysis and Data Science attempt to extract information from data. For that purpose, we use techniques from statistics, machine learning, signal processing, natural language processing, and computer science.

A mind map visualizing Python software that can be used for data analysis can be found in first chapter of this book. The first noticeable thing is that the Python ecosystem is very mature, diverse and rich. It includes famous packages such as NumPy, SciPy, and matplotlib. This should not come as a surprise since Python has been around since 1989. Python is easy to learn and use, less verbose than other programming languages, and very readable. Even if you don't know Python, you can pick up the basics within days, especially if you have experience in another programming language. To enjoy this book, you don't need more than the basics. There are plenty of books, courses, and online tutorials that teach Python.