учебник по программированию

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.

Causal Inference на Python, Причинно-следственные связи в IT-разработке, Факур М., 2025.
    
   Сколько покупателей привлечет дополнительный доллар онлайнового маркетинга? Какие покупатели будут покупать только тогда, когда им дадут купон на скидку? Как определить оптимальную ценовую стратегию? Лучший способ выяснить, как имеющиеся в нашем распоряжении рычаги влияют на те бизнес-показатели. которые мы хотим получить, это causal inference.
Матеус Факур, старший специалист по анализу данных компании Nubank, рассказывает о практически неиспользованном потенциале causal inference для оценки последствий и эффектов. Менеджеры, специалисты по обработке данных и бизнес-аналитики познакомятся с классическими методами каузального вывода, такими как рандомизированные контрольные испытания (А/В-тесты). линейная регрессия, оценка склонности, синтетические элементы управления и разница в разнице. Каждый метод сопровождается примером из реальной жизни.

Bootstrapping Microservices, Davis A., 2024.
    
   This book, Bootstrapping Microservices, Second Edition, answers these questions and more! It’s your guide to building an application with microservices, using the latest tools. We’ll start from nothing and go all the way to a working microservices application running in production.
You won’t find much theory in this book. Bootstrapping Microservices, Second Edition, is practical and project based. Together, we’ll work through numerous examples of microservices, eventually getting to production, and covering everything you need to know to be a confident microservices developer.

Большие данные, Современные фреймворки и разработка приложений, Талия Д., Трунфио П., Мароццо Ф., 2025.
    
   В книге рассматриваются модели, системы и фреймворку специально разработанные для обработки и анализа больших наборов данных. Вы познакомитесь с основными парадигмами и механизмами, применяемыми в анализе больших данных, включая MapReduce, рабочие потоки, массовый синхронный параллелизм, передачу сообщений и SQL-подобные модели. В главах книги описаны примеры использования фреймворков Hadoop, Spark, Storm и MPI и рассмотрены вопросы выбора среды, наиболее подходящей для достижения целевых задач приложения.
Книга предназначена разработчикам приложений для работы с большими данными, исследователям и профессионалам бизнеса, основанного на данных. Читатель должен хорошо владеть такими языками, как Java, Python или Scala, и знать основные концепции параллельного и распределенного программирования.

Automate the Boring Stuff with Python, Al Sweigart, 2025.
    
   Automate the Boring Stuff with Python was a brilliant concept for a book when it first came out 10 years ago, and it remains a brilliant concept today. This is evidenced by the fact that it’s been one of the go-to resources for learning Python for as long as it’s been in print. If you need to solve a specific task right now, you’ll find many practical examples in this book that you can adapt to your own needs. If you’re interested in developing a more general understanding of programming, implementing a series of real-world projects like the ones you’ll find here is a great way to do so.

Applied Data Science Using PySpark, Kakarla R., Krishnan S., Dhamodharan B., 2024.
    
   This book is recommended for data science enthusiasts who want to unleash the power of parallel computing by working with big datasets. It’s also highly recommended for professionals who want to switch from traditional languages to open-source in a big data setting. It’s a value-add for students who want to work on big data as well.