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“Having skills in statistics, math, and programming is certainly necessary to be a great analytic professional, but they are not sufficient to make a person a great analytic professional.” – Bill Franks, Chief Analytics Officer at Teradata
The coding skills that you need to get into data science will depend on the area of data science that you may end up working on. If you end up managing databases, it’s important that you know that as more enterprises start using data science, legacy skills, like SQL, will hang around. Larger companies will end up using SQL through their operations.
If you want to do more with the data that you have collected, it may be best if you expand your knowledge of SQL with a focus on skills such as managing, collecting, and storing data. This may seem obvious, but it is worth keeping this in the back of your mind as we look at the trends.
If you plan to use your data to perform visualization, analytics, and modeling, you will want to make sure you are strong in Java, Python, and R.
R has started to become the lingua franca for the pure data scientist, especially when it is used in scientific research and finance. It works as a procedural language, instead of an object-oriented language such as Java or Python. It will likely require more code to get the job done, but there is more that you can do with it. R also has a granular functionality that a lot of data scientists prefer to use, especially whenever it comes to having to deal with a lot of data.
This being said, Java is extremely scalable and fast. While R is able to present a lot more options when it comes to working with complex data issues, there are a lot of startups, as well as other businesses, who love Java for giving them more bang for their product development and developer training buck.
Python tends to fall in between things. It is able to do a lot, it’s scalable, and it’s fast. With a skills market that likes to have a good enough for enough uses, the best thing to turn to is Python.
The fresh areas for the most growth will circle around deep learning, AI, and machine learning. The number of people that work in data science who have these skills has doubled over the past three years, and now takes up almost a third of the industry. The great thing about Java, Python, and R is that they all plug into machine learning, so taking the time to learn any of these skills is time well spent.
When it comes specifically to deep learning, Google TensorFlow has changed quickly to get a strong leadership position, and it is followed closely by Keras. A little bit of an interesting note about TensorFlow. It is written in C++ which, until just a few years ago, was the leading language of data science. TensorFlow actually runs on a Python interface that works on a C++ foundation, which means you won’t have to understand C++ coding in order to use TensorFlow.
This is the kind of dynamic that is taking place within the world of data science and analytics. As data starts to become ubiquitous, the uses will start to become innumerable, and the amount of code-based solutions will grow exponentially. This will, in turn, drive a market for data science tools that will help to simplify the coding process.
The main point? View this like it is college: while you may pick out a programming language for your major, it doesn’t hurt to create a working knowledge of several others as your minors. Things in the data science world change fast, and these are very exciting times for data scientist and coders.
