A scientific project in PyCharm is created with a general structure that is common among projects in real life, including good practices such as a data folder that is excluded from version control, the README.md file, and the requirements.txt file. As you can imagine, having to manually create this setup for every project can prove to be difficult and time-consuming. This feature helps PyCharm users get right down to the development process after the project has been created so that they don't have to worry about taking care of miscellaneous details. This will allow us to be faster and more productive in our development workflow.

Additionally, PyCharm's Scientific Mode includes various features that support the development process of scientific computing or data science projects, namely the Documentation and SciView panels. In combination with this mode, you can also take advantage of other powerful features, such as code cells and the CSV plugin, to streamline various tasks and effectively improve your productivity in data science projects.

However, these features only mark the beginning of what PyCharm has to offer when assisting us in data-related projects. Building on these topics, in the next chapter, we will look into the usage of the SciView panel and Jupyter notebooks, which are a big part of the Python data science ecosystem within PyCharm.