Everyone wants to pursue a career where they will be in high-demand. This want stems from the reasoning that high-demand translates into great pay and steady work. Today, the big data space is full of this type of employment, as companies of all sizes need to both collect and analyze information to make predictions and decisions.
This is exactly what a data scientist does – find information, make connections, create helpful data visualizations and help the company they work for operate efficiently.
In order to do all that effectively, it is necessary to understand the right programming languages. According to statistics, up to 91 percent of data scientists will use the following four languages on a daily basis:
R is a type of statistic-based language that is commonly used by data miners. This is an open-source and object-oriented implementation of the language called S, and it isn’t too hard to learn.
If you are interested in learning how you can develop statistical software, then it is good to get to know R. This allows you to easily manipulate, as well as graphically display various data.
You will find there are several schools that offer classes for R, which will not only teach you how to program with this language but will also review how to apply it in the context of data science and analysis.
Similar to R, the SAS language is mainly used for purposes of statistical analysis. It is an extremely powerful tool to help transform the data from various spreadsheets and databases into readable formats, in addition to graphs and visual tables.
This was originally developed by academic researchers, and it is considered one of the most popular analytic tools used all around the world for both organizations and companies of all types. This is more of a larger corporation type of software that isn’t usually used by any smaller individuals or companies that are working alone.
Even though SAS and R are commonly believed to be the “big two” in the world of analytics, Python is also considered a contender. One of the main advantages it offers is the many libraries and the statistical functions it offers.
Because Python is another type of open-source language, the updates are quickly added to it. Another factor that you need to consider is that Python is one of the easiest to learn because it is simple and available in many resources and courses.
The above languages are in the same family and have mainly the same functions. The letters “SQL” actually stand for Structured Query Language. This is a language that doesn’t have anything to do with statistics. Instead, it focuses on handling various information in relational databases.
This is considered the most available and widely used language and is also open source. As a result, if you are an aspiring data scientist, then you should not skip learning this.
By learning SQL, it will equip you to create SQL databases, and manage the data in them. You will also be able to use the relevant functions related to this language. There are several online courses that will cover all the basics of this language, and that can be done pretty easily.
The Bottom Line
At the very least, you need to learn SQL and choose a minimum of one statistic language. However, if you have the time – and you want to increase your marketability as a data scientist further, then there is no reason you should not attempt to learn all four of these languages.
Remember, if you decide to invest time and energy in these, then you don’t need to rush anything. Make sure to get plenty of practice and fine tune your skills. Once you do this, you can enjoy your new secure job.
When you're ready to look for a role that includes your new skills, contact ICS. We have plenty of open jobs for the right talent. We'd love to help you find a job that matches with your career goals. Search our jobs today!