There’s no better field to get into right now than data science.
Our research shows data scientists are the most in-demand professionals in the world. And the average data scientist in the United States today earns $114,000 a year.
But how do you get into it? What are the programming languages you should learn first to become a data scientist?
The short answer is to focus on any combination of Python, R, Java and SQL. Here are three LinkedIn Learning courses that teach those skills: Python 3 Essential Training, R Statistics Essential Training and SQL Essential Training.
The longer answer
The coding skills you need are dependent on what area of data science/analytics you might be working on. For help deciding on what path within data science is best for you, I recommend watching our course Data Science & Analytics, Career Paths & Certifications: First Steps.
If you want to manage databases, for example, know that as more enterprises adopt a data strategy, some legacy skills, such as for SQL, will linger. Large companies tend to use SQL throughout their operations.
If you’ve decided to do more with the data you have and are collecting, you may look to expand your SQL talent base with a focus on data skills like collecting, storing and managing data. Seems obvious, but worth keeping in mind as we take an overview of trends.
If you’ll be taking that data and doing analytics, modeling and visualization, you’ll need to strongly consider Python, R and Java.
R is becoming the lingua franca for pure data science, especially in finance and scientific research. It is a procedural language (as opposed to object-oriented languages like Python or Java) and it may take more code to do the job, but you can do more with it. There’s also a granular functionality to R that many data science specialists prefer, especially when it comes to working with large data sets.
That said, Java is fast and extremely scalable. Where R can arguably present more options for working with data (we’re talking really complex issues here), many startups – among other businesses – love Java for getting the most bang for their developer-training and product-development buck.
Python is arguably the in-between: it can do a lot, it’s fast, and it’s scalable. In any skills market that values a good-enough-for-enough-uses solution, the solution is usually Python.
The new areas for exceptional growth orbit around AI, machine learning and deep learning. The number of people in data science who list these as skills they’re using and learning has doubled each of the last three years, and is now nearly a third of the industry. The good news is that Python, R and Java all plug into machine learning, so investing in any of those skills is time well spent.
For deep learning specifically, Google TensorFlow has quickly attained a strong leadership position, followed by Keras. An interesting and illustrative note about TensorFlow is that it’s written in C++, which until a couple years ago was a leading programming language in data science. But, TensorFlow runs on a Python interface that sits on top of the C++ foundation, which means you don’t have to know C++ to code with TensorFlow.
It’s this kind of dynamic that’s taking place in the data science and analytics space: as data becomes ubiquitous, the uses for it become innumerable and the number of coding-based solutions grows exponentially. That in turn drives a market for tools that simplify the coding process wherever possible.
The point? Think of it like college: while you may have a chosen programming language as your “major”, it never hurts to develop a working knowledge of the other major players – your “minors”. Things are changing fast; these are exciting times rich with opportunity for coders in the data science and analytics space, but you absolutely have to keep up with the changes.
Looking for the most straightforward path to learning data science? Check out our Learning Path, Become a Data Scientist, which gives you the basic skills you need to get into the field.
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