Data-driven science, or data science, exists as a science to quantify observations and experiences with hard, solid data.

Data science encompasses many scientific disciplines including maths, statistics, computer science and business. Data science is ever-evolving and demand for data-driven science experts is significantly on the rise.

A successful career in the field requires specific skill sets of which competency in one or more programming languages is essential.

With so many programming languages to choose from, if you're considering a career in data science, here's a list of the top six programming languages that will advance your opportunities as a data scientist. 


1) Python.

Python is not only one of the most straightforward programming languages to learn, it's also one of the most popular programming languages used in data science.

It's capabilities as a data analysis tool are rapidly growing and it's considered a powerful vehicle for medium-scale processing of data.

While Python might not be the highest achieving programming language for data miners, what it lacks in high-level performance, it makes up for in flexibility and its broad reach.

Python is both popular and effective but while it packs a hard punch for data statisticians, it might not be the best choice for data projects with higher level requirements. 


2) R.

What should you love more about R? The fact that it's free? The fact that it's commonly used among Wall Street statisticians? Or that with 20 years of being used as a programming language for data miners, it's the one that gets the job done the best?!

For those starting out, R. isn't just a programming language, it's an active community. You can always find new elements being developed in line with market trends and to meet the needs of the consumer.

R. falls over when data projects become large scale as it can be cumbersome when handling large scores of information. Regardless of that, it's visual capabilities are excellent for presenting research and findings, and it's been likened to an upscaled version of Microsoft Excel.


3) Java and JavaScript.

Java is effective in data science but it loses points on its reduced capability in visual skills compared to Python. While Python is a much better option for displaying data, Java is the base language of many data mining projects where substantial data systems are required.


4) C and C++.

C++ is one of the least popular languages in data science because of its complex nature and its difficulty to learn. That aside, it's still a useful programming language to know in data science so don't despair if you've got it in your toolkit!

While complicated to learn, C and C++ can be useful if you have more complex needs that R. can't meet such as projects that require real-time output.


5) Scala.

Scala is also Java based and while not quite as efficient as JavaScript, it still functions well when building more complex algorithms and if your system requirements need to be more sturdy.


6) Julia.

Julia is a new player in the game and while she can't yet keep up with data science veterans R. and Python, that's no reason to rule out her capabilities.

The Julia community is still in its infancy but, as a programming language, Julia is fast and can handle large-scale projects, she's genuine competition for R and Python in the future. Watch this space!

If you're looking at data science as a career, there's no way around R. and Python. If you're competent in other languages that doesn't mean you're at a disadvantage, but proficiency in these most popular languages will be beneficial to your success as a data scientist.


Did we miss any? Let us know in the comments below!

Publié 25 juin, 2017


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