Python since the ecosystem is so big. In my experience, as a data scientist. You only need to engage with a very small part of Python’s tools and then reach further as you explore new possibilities. For the new data scientists, you only need five libraries: Numpy, Scikit-learn, Pandas, Scipy, and Seaborn.
Most of us simply use one
The other depending on what we feel c level contact list comfortable. What each language can provide to tackle a problem. Python users can import R functions with ease and vice-versa. It’s not a question of which one will eventually win over the other. But rather on which language you should focus your time and effort primarily. In my humble opinion. The massive expansion we are seeing with Python I find very little reason to tell someone to start with R.
Any data scientist worth
A dime should have a good understanding you wouldn’t have a chance to try things of both languages.There is a saying amongst upcoming programmers: “you need to start coding by age 5 so that by the time you are done with college you have the experience for an entry-level job.” That would be a joke if it weren’t for the fact that it’s painfully true. Being hired as a young software developer is about as easy as fighting a raging gorilla with your hands tied to your back.
I could point to hundreds
That place job ads for entry-level positions email data asking for the experience of an average or seasoned programmer but looking for people in their early 20s. In turn, a young and talented candidate ends up discouraged and passing up opportunities that would have been perfect for their skill level. But why are companies so obsessed with overqualified candidates? The question isn’t that simple to answer.