Yuli Vasiliev - Python for Data Science: A Hands-On Introduction

We live in a world of information technology (IT), where computer systems collect enormous quantities of data, process it, and extract useful information from it. This data-driven reality affects not only the way modern businesses operate but our daily lives too. Without the numerous devices and systems that employ data-focused technologies, it would be hard for many of us to maintain contact with society. Mobile maps and navigation, online shopping, and smart home devices are some common examples of data-focused technology for everyday life.

In the business world, companies often use IT systems to make decisions by extracting actionable information from large volumes of data. The data may arrive from various sources, in different formats, and may require transformation before its ready for analysis. For example, many companies that do business online use data analytics to drive customer acquisition and retention, collecting and measuring everything they can to model and understand their users behavior. They often combine and analyze both quantitative and qualitative user data from many different sources, such as user profiles, social media, and company websites. And in many cases, they accomplish all these tasks using the Python programming language.

This book will introduce you to the Pythonic world of working with data, without the taint of academic jargon or excessive complexity. Youll learn to use Python for data-oriented applications, writing code to power a ride-sharing service, generate product recommendations, predict stock market trends, and more. Through real-world examples such as these, youll gain practical, hands-on experience with the key Python data science libraries.

The easy-on-the-brain Python programming language is an ideal choice for accessing, manipulating, and gaining insight from data of any kind. It has both a rich set of built-in data structures for basic operations and a robust ecosystem of open source libraries for data analysis and manipulation of any level of complexity. Well explore many such libraries in this book, including NumPy, pandas, scikit-learn, Matplotlib, and more.

With Python, you can write concise and intuitive code with minimal time and effort, expressing most concepts in just a few lines of code. In fact, Pythons agile syntax allows you to implement several data operations with a single line of code. For example, you can write a one-liner that filters, transforms, and aggregates data all at once.

As a general-purpose language, Python is suitable for a wide variety of tasks. When you work with Python, you can seamlessly integrate data science with other tasks to create fully functional, well-rounded applications. For example, you could build a bot application that makes stock market predictions in response to natural language requests from users. To create such an application, youd need a bot API, a machine learning model to make predictions, and a natural language processing (NLP) tool to interact with users. There are powerful Python libraries for all of these.

This book is for developers looking to gain a better understanding of Pythons data processing and analysis capabilities. Perhaps you work for a company that wants to use data to improve business processes, make better decisions, and target more customers. Or maybe you want to develop your own data-driven applications, or simply expand your knowledge of Python into the realm of data science.

The book assumes you have some basic experience with Python and that youre comfortable following instructions to perform tasks such as installing a database or obtaining an API key. However, the book covers Python data science concepts from the bottom up, through hands-on examples that are all thoroughly explained. Youll learn by doing, with no prior data experience necessary.