Grow your software engineering discipline, incorporating and mastering design, development, testing, and deployment best practices examples in a realistic Python project structure. Key Features Understand what makes Software Engineering a discipline, distinct from basic programming Gain practical insight into updating, refactoring, and scaling an existing Python system Implement robust testing, CI/CD pipelines, and cloud-ready architecture decisions Book Description Software engineering is more than coding; it’s the strategic design and continuous improvement of systems that serve real-world needs. This newly updated second edition of Hands-On Software Engineering with Python expands on its foundational approach to help you grow into a senior or staff-level engineering role. Fully revised for today’s Python ecosystem, this edition includes updated tooling, practices, and architectural patterns. You’ll explore key changes across five minor Python versions, examine new features like dataclasses and type hinting, and evaluate modern tools such as Poetry, pytest, and GitHub Actions. A new chapter introduces high-performance computing in Python, and the entire development process is enhanced with cloud-readiness in mind. You’ll follow a complete redesign and refactor of a multi-tier system from the first edition, gaining insight into how software evolves—and what it takes to do that responsibly. From system modeling and SDLC phases to data persistence, testing, and CI/CD automation, each chapter builds your engineering mindset while updating your hands-on skills. By the end of this book, you'll have mastered modern Python software engineering practices and be equipped to revise and future-proof complex systems with confidence. What you will learn Distinguish software engineering from general programming Break down and apply each phase of the SDLC to Python systems Create system models to plan architecture before writing code Apply Agile, Scrum, and other modern development methodologies Use dataclasses, pydantic, and schemas for robust data modeling Set up CI/CD pipelines with GitHub Actions and cloud build tools Write and structure unit, integration, and end-to-end tests Evaluate and integrate tools like Poetry, pytest, and Docker Who this book is for This book is for Python developers with a basic grasp of software development who want to grow into senior or staff-level engineering roles. It’s ideal for professionals looking to deepen their understanding of software architecture, system modeling, testing strategies, and cloud-aware development. Familiarity with core Python programming is required, as the book focuses on applying engineering principles to maintain, extend, and modernize real-world systems.

Data Engineering with Scala and Spark guides you through building robust data pipelines that process massive datasets efficiently. You will learn practical techniques leveraging Scala and Spark with a hands-on approach to mastering data engineering tasks including ingestion, transformation, and orchestration. What this Book will help me do Set up a data pipeline development environment using Scala Utilize Spark APIs like DataFrame and Dataset for effective data processing Implement CI/CD and testing strategies for pipeline maintainability Optimize pipeline performance through tuning techniques Apply data profiling and quality enforcement using tools like Deequ Author(s) Eric Tome, Rupam Bhattacharjee, and David Radford bring decades of combined experience in data engineering and distributed systems. Their work spans cutting-edge data processing solutions using Scala and Spark. They aim to help professionals excel in building reliable, scalable pipelines. Who is it for? This book is tailored for working data engineers familiar with data workflow processes who desire to enhance their expertise in Scala and Spark. If you aspire to build scalable, high-performance data solutions or transition raw data into strategic assets, this book is ideal.

This in-depth guide covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. In this book, you'll implement numerical algorithms in Kotlin using NM Dev, an object-oriented and high-performance programming library for applied and industrial mathematics. Discover how Kotlin has many advantages over Java in its speed, and in some cases, ease of use. In this book, you’ll see how it can help you easily create solutions for your complex engineering and data science problems. After reading this book, you'll come away with the knowledge to create your own numerical models and algorithms using the Kotlin programming language. What You Will Learn Program in Kotlin using a high-performance numerical library Learn the mathematics necessary for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solutions Build solvers for industrial optimization problems Perform data analysis using basic and advanced statistics Who This Book Is For Programmers, data scientists, and analysts with prior experience programming in any language, especially Kotlin or Java.

Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You’ll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. What You Will Learn Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solution Build solvers for industrial optimization problems Do data analysis using basic and advanced statistics Who This Book Is For Programmers, data scientists, and analysts with prior experience with programming in any language, especially Java.