5 Essential Data Science Languages
Read up about 5 programming languages every good data scientist should know!
First off, in case you’re unfamiliar with data science as a whole, check out this neat article I wrote!
But just knowing what data science is doesn’t guarantee a lengthy career in the field. Any data scientist worth his salt needs to know the core languages that form the building blocks of a successful data science portfolio. We here at the Data Science Library have compiled a short list for that exact purpose!
1. PYTHON
Python has been rapidly increasing in popularity recently, and for good reason. It’s straight-forward and easy to learn (especially for people with little to no coding experience), and has a huge community supporting it.
There’s a variety of resources out there for learning about Python. In case you’re a visual learner, check out this short video series on YouTube. If you want a written guide, there’s Python For You And Me, a Python guide made with beginners in mind. Google also has its own Python guide, also written for beginners but with extra coding challenges and videos interspersed throughout it. Lastly, the Python Software Foundation itself has its own Python tutorial, although it is much drier and dense than some of the previously mentioned guides.
If you want a more extensive guide to Python, check out the Data Science Library’s Python guide right here!
2. JAVA
Just like Python, Java is another incredibly popular programming language. It has a similar syntax to Python, an open-source ecosystem to support it, and a thriving community. For that reason, it’s recommended that either this or Python be your first coding languages (unless you already have prior experience).
The Data Science Library, unfortunately, doesn’t have any guides on Java, but there’s plenty of resources available online. If you want a beginner-focused guide, check out this one by the appropriately-named website JavaBeginnersTutorial. A similar guide, if slightly more complex, can be found here on the website BeginnersBook. For a more dense but informative guide, look no further than Oracle's Java tutorials. Oracle are the actual owners of Java, so they have the most up-to-date and encompassing collection of Java information.
3. R
R isn’t as popular as Python or Java, but is still worth looking into. It’s a statistical analysis language, so it is intrinsically linked to data science as a whole. Just like Python and Java, it has a dense amount of software linked to it and a vibrant community around it.
If you want to check out some videos on R, here’s a YouTube series by Google’s ‘Google Developers’ account that’s intended to bring beginners up to speed with actual developers. For written guides, be sure to check out this R guide made for students at the University of Washington. Since it was written for students, it goes to great depth about difficult topics and links to more learning material with it. There are also online programs, one by Microsoft and one by DataCamp, intended to help programmers learn on their own by practising and performing as if they were in an actual coding class.
If you want a more extensive guide to Python, check out the Data Science Library’s Python guide right here!
4. SQL
SQL stands for ‘Structured Query Language,’ which essentially means it revolves around interacting with databases. As a result, it’s a language that every data scientist will run into at some point, whether they want to or not. Better to get a head start now and be prepared for the future, rather than bump into it later and know next to nothing!
The Internet has a lot of sources for SQL tutorials, but one of the better ones would have to be SQL Problems and Solutions. It’s a kind of interactive online textbook, entirely dedicated to teaching new students how to use SQL. SQLZoo is another interactive tutorial site that is worth looking at, since it has a heavy emphasis on practising the code on your own. Lastly, Vertabelo has a series of online tutorials similar to CodeAcademy but much more colorful and fun to both look at and interact with.
5. JULIA
Julia is a relatively new language, faster than R or Python but not as popular as either of them. It isn’t hard to use or learn and could be worth learning for when it inevitably becomes essential in the future.
There isn’t as much on Julia as the other languages listed here, but that doesn’t mean there’s nothing. If you want a video series, check out ‘Into The Queryverse’ by data scientist David Anthoff. ThinkJulia and Introducing Julia are two online textbooks dedicated to thoroughly explaining every part of Julia, while this particularly huge online tutorial helps already knowledgeable programmers gain a solid foothold on using Julia.
Now that you’ve learned about some of these languages and where to start practicing with them, be sure to keep looking for more ways to increase your coding knowledge and add to your data science portfolio!
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