Originally published at: https://www.datacamp.com/community/blog/web-development-data-science

The data science field is still hot and the programming languages that have been used for it are too: R popularity has been increasing every year and especially Python gaining more and more importance in the data science industry. This is not only because this general-purpose language stands out for its readability and has a relatively low and very gradual learning curve, but mostly also thanks to the tools and the concepts that were originally built by scientists and sysadmins. Because, even though there seems to be a culture gap between those who use Python for scientific purposes and those who use it for more conventional purposes such as system administration and web development, their strength is that they can work together. And this has been proven in recent years: the solid foundations of the Python language have been enriched with the creation and development of packages that help data scientists and data science teams tackle complex data problems.

As a result, there are users who look to move more towards using Python for data science. And those who are learning data science often wonder what else they can do with this popular programming language.