Python is an ideal back-end language when it comes to data science, because of its emphasis on code readability and its extensive libraries and frameworks. Since the language is so simple and consistent, developers are able to write systems fast. This is how they can focus on the problems of machine learning, without being bothered by possible bottlenecks that are often associated with complex programming languages. Moreover, Python possesses an extensive library set, specifically made for data science, like Keras, TensorFlow and Scikit-learn.
What does a Python developer do?
What is mostly important for a Python data scientist is their knowledge of the different libraries. First of all, they know how data can be collected, think of data scraping from websites using selenium, for instance. Python data scientists also know how to deal with (huge amounts of) data. Basic knowledge of SQL is useful, and knowledge of data libraries like Pandas or Numpy is required. Obviously a Python data scientist knows which techniques can be implemented, so they are experienced with several machine learning techniques (Naive Bayes, Linear Regression, Neural Networks) and frameworks (scikit-learn, TensorFlow, PyTorch). Lastly, data science also includes the communication of findings, of which data visualisation is an important element. Data scientists use mostly matplotlib.