While there are many online courses to learn Python for Machine learning and Data science, books are still the best way to for in-depth learning and significantly improving your knowledge. Python is a universal language that is used by both data engineers and data scientists and probably the most popular programming language as well. All the Data Scientists I have spoken and many in my friend circle just loves Python, mainly because it can automate all the tedious operational work that data engineers need to do. To make the deal even sweeter, Python also has the algorithms, analytics, and data visualization libraries like Metaplotlib, which is essential data scientists. In both roles, the need to manage, automate, and analyze data is made easier by only a few lines of code.
It might be time for data scientists to learn a new programming language. Particularly if they have a need for speed. Last week, the lead developers behind the open source programming language Julia announced the 1.0 release of their project. This signals that the language, which is optimized for data analysis and machine learning, is no longer a work in progress. Julia code written in the 1.0 version will still work even when new versions are released--by contrast, code written in version 0.4 was not guaranteed to work under version 0.6.
Nothing is quite so personal for programmers as what language they use. Why a data scientist, engineer, or application developer picks one over the other has as much to do with personal preference and their employers' IT culture as it does the qualities and characteristics of the language itself. But when it comes to Big Data, there are some definite patterns that emerge.