Python. The basics of data science. Edition II

Original title: Python Data Science Essentials - Second Edition
Translation: Tomasz Walczak
ISBN: 978-83-283-2846-4
Release date: 2017
Format: 170x230
Number of pages: 320

Data science is a new, interdisciplinary field that operates on the borderline of linear algebra, statistical modeling, computational linguistics, machine learning and data accumulation methods. It is useful, inter alia, for business analysts, statisticians, software architects and people involved in artificial intelligence. Python is a particularly practical tool for these specialists, which provides an excellent environment for data analysis, machine learning and algorithmic problem solving.

This book is a great introduction to learning about data. Its authors will show you a simple and fast way to solve various problems in this area using Python and related data analysis and machine learning packages. Thanks to the reading you will go through the subsequent stages of modifying and pre-processing data, while learning the basic operations associated with loading data, transforming them, improving for analysis, exploration and processing. In addition to the basics, you will also learn about machine learning, including deep learning, graph analysis and data visualization techniques.

The most important issues presented in the book:

• configuration of the Jupyter Notebook environment
• the most important operations used in data science
• data streams and machine learning
• introduction to graphs and visualizations
• Python libraries and packages for data research

Learning about data - fascinating algorithms and powerful graphs!

Alberto Boschetti specializes in signal processing and statistics. He is a Ph.D. in telecommunications engineering. He deals with natural language processing, behavioral analysis, machine learning and distributed computing.

Luca Massaron specializes in statistical multivariate analyzes, machine learning, statistics, data mining and algorithmics. He is passionate about the potential that lies hidden in data science.