учебник по программированию

DuckDB Up and Running, Fast Data Analytics and Reporting, Wei-Meng Lee, 2025.
    
   This book serves as a comprehensive guide to understanding and mastering DuckDB, providing readers with the knowledge and tools to harness its full potential in diverse applications. From the initial installation to advanced querying techniques, each chapter guides you through a well-structured learning path. The intent is to equip both beginners and experienced users with the skills necessary to leverage DuckDB effectively, whether for personal projects or within larger organizational frameworks.

DuckDB in Action, Needham M., Hunger M., Simons M., 2024.
    
   This book covers DuckDB—a modern, fast, embedded analytical database. It runs on your machine and can easily process many gigabytes of data from a variety of sources, including JSON, CSV, Parquet, SQLite, and Postgres. DuckDB integrates well into the Python and R ecosystems and allows you to query in-memory data frames without copying the data. You don’t need to spin up cloud data warehouses for your day-to-day data processing anymore; you can just run DuckDB on your data, locally or in the cloud.

Django Python for Web Development, Wetherby A., 2023.
    
   Django, a high-level Python framework, is designed to facilitate fast and efficient web development. It adheres to the "don’t repeat yourself' (DRY) principle, aiming to simplify the development process by minimizing redundant coding tasks. This recapitulation of basic Django concepts will provide clarity on its architecture, primary functionalities, and core components, making it a favored framework among web developers.

Digital Image Processing, An Algorithmic Introduction, Burger W., Burge M.J., 2022.
    
   This book provides a modern, self-contained introduction to digital image processing. We designed this book to be used both by learners desiring a firm foundation on which to build as well as practitioners in search of detailed analysis and transparent implementations of the most important techniques. This is the third English edition of the original German-language book.

Designing Data intensive application in Pytho, Empowering Curious Minds, Aarav Joshi, 2024.
    
   Data-driven decision-making has emerged as a critical factor in gaining a competitive edge. Companies now rely on data analytics to gain insights into customer behavior, optimize operations, and predict market trends. By leveraging data, organizations can make informed choices, reduce risks, and identify new opportunities for growth.
Python has become a dominant force in the data ecosystem, offering a rich set of libraries and frameworks for data manipulation, analysis, and visualization. Its simplicity, versatility, and extensive community support have made it a go-to language for data scientists and engineers alike.

Data Pipelines with Apache Airflow, De Ruiter J., Cabral I., Geusebroek K., Harenslak B.
    
   Thank you for purchasing the MEAP of the second edition of Data Pipelines with Apache Airflow, Second Edition.
When we released the first edition of the book it was based on Airflow 2.0. Since then, Airflow has significantly evolved in many aspects including the addition of many new features and functionality. On top of that, Airflow 3.0 is just around the corner, so it is important to be ready and up to date for its arrival.
Despite Airflow’s maturity, new users are still getting onboard every day. With its continuously growing ecosystem and its vast set of features, it can be quite a daunting tool for those just starting out. While Airflow’s versatility is most of the time an asset, it can sometimes lead users to build suboptimal workflows as it can be hard to figure out the right way to use its features. For this reason, we decided to update the book to guide new and experienced users to make the most of what Airflow has to offer.

Data Mining with Python, Theory, Application, and Case Studies, Di Wu, 2024.
    
   This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers gain practical skills to implement Data Mining techniques in their work.

Data Engineering for Machine Learning Pipelines, Narayanan P.K., 2024.
    
   This book is designed to serve you as a desk reference. Whether you are a beginner or a professional, this book will help you with the fundamentals and provide you with job-ready skills to deliver high-value proposition to the business. This book is written with an aim to bring back the knowledge gained from traditional textbook learning, providing an organized exploration of key data engineering concepts and practices. I encourage you to own a paper copy. This book will serve you well for the next decade in terms of concepts, tools, and practices.