Python is one of the most preferred languages out there. Its brevity and high readability makes it so popular among all programmers.
So here are few of the tips and tricks you can use to bring up your Python programming game.
Are there more jobs for Python data scientists than for an R data scientist?
There are places one might be better than the other — Python has great machine learning libraries, for example, and R has strong stats packages.
That said, you shouldn’t look at it as being a “Python data scientist” versus an “R data scientist.” Rather than thinking about which hammer to use, look at the nail — what does your specific project require? The toolsets you need will vary, so it’s important to be flexible.
Data science in general continues to be a growing field, so jobs will certainly continue exist. Being successful in your job hunt isn’t about which language’s flag you raise. Instead, here are a few tips to help your search:
Keep learning. In many ways, curiosity — that insatiable hunger for knowledge and understanding — is the first and foremost trait of a data scientist. In order to set yourself up for getting a job, you’ll want to learning by working on projects. Projects are what you’ll be creating in a data science job, and building them on your own is a good way to practice. You can consult this list of places to find datasets for your projects.
Build a portfolio. Projects are a great way to practice your skills, but it’s when you show them off that they become extremely valuable in your job search. By posting your portfolio publicly, you raise your profile, and allow other people to see your work and skills.
Share your work. As you raise your profile in the data science world, you’ll find it easier to access new opportunities. One way to raise your profile is to share your work with others in relevant contexts. There are quite a few data science and programming communities that appreciate tutorial or project walkthrough blog posts.