📚 A small selection of books on Data Science and Big Data

Software Engineering for Data Scientists - This book explains the mechanisms and practices of software development in Data Science. It also includes numerous implementation examples in Python.

Graph Algorithms for Data Science - The book covers key algorithms and methods for working with graphs in data science, providing specific recommendations for implementation and application. No prior experience with graphs is required. The algorithms are explained in simple terms, avoiding unnecessary jargon, and include visual illustrations to make them easy to apply in your projects.

Big Data Management and Analytics - This book covers all aspects of working with big data, from the basics to detailed practical examples. Readers will learn about selecting data models, extracting and integrating data for big data tasks, modeling data using machine learning methods, scalable Spark technologies, transforming big data tasks into graph databases, and performing analytical operations on graphs. It also explores various tools and methods for big data processing and their applications, including in healthcare and finance.

Advanced Data Analytics Using Python - This book explores architectural patterns in data analytics, text and image classification, optimization methods, natural language processing, and computer vision in cloud environments.

Minimalist Data Wrangling with Python - This book provides both an overview and a detailed discussion of key concepts. It covers methods for cleaning data collected from various sources, transforming it, selecting and extracting features, conducting exploratory data analysis, reducing dimensionality, identifying natural clusters, modeling patterns, comparing data between groups, and presenting results