There will always be arguments in the programming community about which languages are ‘the best’. In truth, a lot of it depends on personal preference. However, some languages are more useful than others for certain tasks, and when it comes to data science there are some languages that have a few advantages over others.
This area of computing is growing steadily, with IBM estimating the demand for both data scientists and data engineers will grow 39% by 2020. It’s a lucrative field to enter into, with the same IBM report finding that the average salary is around $80,000. However, to get there you’ll need to know your way around a few programming languages that will enable you to manage big data.
You won’t need to learn all five of the following options, although knowing more than one will give you a big advantage when hunting for jobs. It’s worth looking at the specific field you want to go into and seeing which languages they’re more likely to use. Here are the best choices for anyone looking to go into data science:
One of the best options for you to learn is R. It’s an open-source language designed specifically for statistical analysis, making it perfect for big data. With over 10,000 data packages available and visualization libraries such as ggplot2 and plotly that let you graphically plot your data, it’s easy to see why so many people in data science use R as their language of choice.
Of course, there are a few downsides, the main one being that it’s geared towards statistical analysis that you won’t be able to use it for much else, so it’s not particularly versatile. It’s also known to have a steep learning curve, although the fact it’s open-source means there’s plenty of help and advice available on forums.
One of the most well-known languages on this list, Python is also the preferred choice for 44% of data scientists, making it the most popular language out there. The reasons are fairly clear: Python is versatile, easy to learn and comes with a large number of libraries. It’s also commonly used in data science, meaning there’s a dedicated online community that can help you learn the ropes.
Of course, the popularity of Python means you’ll be facing a lot of competition for jobs. This doesn’t mean you should avoid learning it. Instead, you should consider taking on a secondary language as well so you can better stand out from the crowd.
One of the best analytics engines out there when it comes to large-scale data processing is Apache Spark, and many data science projects will use this. It’s written in Scala, which makes it the best choice for interacting with it. It’s not the only option, but when 71% of Spark users also know Scala it’s clear there’s a strong link between the two.
The language has a reputation for being very difficult to learn, and it’s certainly challenging. However, it shares a lot of syntax with Java, so if you’re familiar with that language then you should be able to understand Scala. It also shares similarities with C, C++ and Python, so while it might not be a good choice for the first language you learn, it could make a good supplementary option.
This is one of the newer coding languages out there, and it’s quickly growing in popularity. Part of this is its reputation for being as easy to learn as Python, but with an impressive level of performance. Some estimate it to be as much as 30 times faster than Python, with tests confirming its speed.
Being such a new language, Julia might not be as in-demand as some of the other options on this list. It was also criticized after its stable release showcased several problems. However, it’s a useful language to learn for conducting large-scale data science tasks due to its ability to solve problems rapidly.
In many ways, SQL is the base level of data science, as the language was designed for querying and editing information stored in databases. Some consider it the most important skill to have when it comes to this field, and understanding it will certainly help your career. However, it might not actually be as useful as some might think.
While SQL is mentioned in around 73% of data science job adverts for entry-level positions, it’s only present in 46% of ads for senior roles. This indicates that it’s not as useful for higher-level jobs, so it’s not necessarily going to serve you as well as Python or R in the long term.