Die praktische Informatik lebt vom Ausprobieren verschiedener Lösungswege; dem Experimentieren mit Programmkonstrukten und Algorithmen, und allgemein vom Selbermachen. Darauf baut das didaktische Konzept des Buchs auf: Anhand einer Vielzahl an Aufgaben kann der Student die präsentierten Konzepte selbst erfahren, mit Ihnen arbeiten und so die eigentlichen Probleme der Informatik wirklich verstehen.
The practice of computer science thrives on trying out different solution pathways, experimenting with program constructs and algorithms, and, in general, from hands-on exploration. A do it yourself approach lies at the heart of this textbook’s teaching method: by solving numerous exercises, the student personally experiences and works with the concepts to be imparted, and comes to truly understand computer science problems.
Eine praktische Einführung in die Informatik mit Bash und Python:Oldenbourg Wissenschaftsverlag Tobias Häberlein
O´Reilly´s bestselling book on Linux´s bash shell is at it again. Now that Linux is an established player both as a server and on the desktop Learning the bash Shell has been updated and refreshed to account for all the latest changes. Indeed, this third edition serves as the most valuable guide yet to the bash shell. As any good programmer knows, the first thing users of the Linux operating system come face to face with is the shell the UNIX term for a user interface to the system. In other words, it´s what lets you communicate with the computer via the keyboard and display. Mastering the bash shell might sound fairly simple but it isn´t. In truth, there are many complexities that need careful explanation, which is just what Learning the bash Shell provides. If you are new to shell programming, the book provides an excellent introduction, covering everything from the most basic to the most advanced features. And if you´ve been writing shell scripts for years, it offers a great way to find out what the new shell offers. Learning the bash Shell is also full of practical examples of shell commands and programs that will make everyday use of Linux that much easier. With this book, programmers will learn: - How to install bash as your login shell - The basics of interactive shell use, including UNIX file and directory structures, standard I/O, and background jobs - Command line editing, history substitution, and key bindings - How to customize your shell environment without programming - The nuts and bolts of basic shell programming, flow control structures, command-line options and typed variables - Process handling, from job control to processes, coroutines and subshells - Debugging techniques, such as trace and verbose modes - Techniques for implementing system-wide shell customization and features related to system security
Informatik:Eine praktische Einführung mit Bash und Python Tobias Häberlein
Informatik:Eine praktische Einführung mit Bash und Python De Gruyter Studium Tobias Häberlein
Learn Docker infrastructure as code technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller. It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies-Python, Jupyter, Postgres-as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms. What Youll Learn Master interactive development using the Jupyter platform Run and build Docker containers from scratch and from publicly available open-source images Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type Deploy a multi-service data science application across a cloud-based system Who This Book Is For Data scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers Joshua Cook is a mathematician. He writes code in Bash, C, and Python and has done pure and applied computational work in geo-spatial predictive modeling, quantum mechanics, semantic search, and artificial intelligence. He also has 10 years experience teaching mathematics at the secondary and post-secondary level. His research interests lie in high-performance computing, interactive computing, feature extraction, and reinforcement learning. He is always willing to discuss orthogonality or to explain why Fortran is the language of the future over a warm or cold beverage.