Python Programming in Jupyter
Programming Basics for Data Science and Machine Learning in Python
Python is one of the most important languages nowadays, especially when it comes to data science and machine learning. This course provides a basic introduction to programming basics and essential libraries of Python while Jupyter serves as an interactive and easy-to-use development environment within your web browser. This course does not require any programming skills in advance. However, it is recommended to add more comprehensive materials to your course program if you are a complete beginner to (scientific) computing or if you want to learn certain advanced topics in Python programming. We also provide links to other online resources throughout the course.
This course uses Jupyter notebooks, where the code and its result are shown as web content here in OPAL. There are also exercise notebooks which only can be downloaded, because you are asked to solve the programming tasks given in these notebooks. A guide on how to install Python and Jupyter will be given.
This course is still in the making and notebooks or sections may change. Do not hesitate to give us feedback what you like or how we can improve the course content.
- Components can be pre-fabricated and traded independently of specific applications. This fosters reuse and thus can decrease development cost and time-to-market, while increasing the quality of the resulting software.
- Component-structured applications can more easily be adapted to new requirements, because individual components can be exchanged largely independently of the rest of the system.
- Black-box component models provide design-time components that are also available at run time.
- Grey-box component models merge design-time components. Therefore, tightly integrated systems can be built with them.
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