Top Programming Languages for Data Science

Data science is an important instrument in the modern world. A lot of computers gather a lot of bytes of information about users behavior, weather, results of sports games and all other kinds of content that appears every second. However, there are a lot of questions about the quality of conclusions that are built according to the analysis of such data. For instance, some respectable magazine created a list of top universities in the country according to their special scoring. For example, they counted:

• the number of PhD professors,
• the average salary of those who graduated,
• the quality of IT technologies that this university uses,
• amount and percentage of graduates who opened their own successful business.

Is There A Way to Define Top Programming Languages?

Does this list seem like an objective to assess? The first answer is yes, but it becomes not so obvious if you start to think deeper. For the next year's rating, all other universities will try to improve only these indexes and ignore their other strengths. What will students do? They will try to enter only universities from the list. This vicious circle works in all spheres making famous and rich people and trademarks rise as quickly as possible.

On the other hand, clear statistics also works with data and gives us a common understanding of the world processes that are extremely important for physics, biology, medicine, space discovery and science prediction in general.

Programmers who are interested in data science must accept the responsibility for their job, because the simplest mistakes, model failure or wrong interpretation can lead to collapsing lives, businesses and decisions of the politicians. For those who are still excited about such a job, we want to suggest a list of top programming languages for data science.

If you are interested we recommend this Data Science Course as a start.

Top Languages for Data Science Programmers

When it comes to choosing a programming language for studying, students often choose not the most suitable one, but the easiest one. Why? Because they are not sure that they will be able to handle the complexity. It is not a wise way, as you limit yourself greatly. So, read the list, think of the language that answers your goal the most and if later you have problems with reaching your educational and professional goals — ask for assistance. It is only normal to write for help with programming assignments.

You can address a professional service, such as MyAssignmentLab, to get programming homework help online, if you stumble upon a coding assignment that is too difficult for now. The keywords here are “for now” — once programming experts jump into action, you will be able to proceed with your studies right away.

• Python. This non-specific data science language is the most popular all over the world. It is good for AI and machine learning that are close to data science a lot. Its libraries are ready to help with every task. Modeling, data collection and visualizing are common problems for Python programmers. At the same time, you will always find help in a huge community that is open and friendly for newbies.
• SQL. The best language for structured data is SQL. It doesn’t require deep programming knowledge because this language is non-procedural. Its best option is that SQL can find requested information among the huge datasets with billions of values that are truly hard to structure with classical code.
• JavaScript (JS). This object-oriented language is also popular among data specialists. It is exceptional for dashboards and visualization. It doesn’t have so many libraries like Python but let’s not forget that it is great for web applications, so you can add its data functions to the website to make it more useful.
• R. This special language was created by statisticians and for their needs. Those who have experience in statistics will recognize a lot of features and find it intuitive. It is not simple for learning but the best for those who dream exactly about data science. The biggest problem of R is a low level of security. It doesn’t contain any security that’s why it is a bad idea to embed it into any of the web applications.
• Julia. Julia is an easy to learn young language whose popularity is growing rapidly. As far as you can guess, the community of this language is not so huge compared with old languages. However, the situation changes extremely fast so you can become one of the leaders of Julia programmers. This language quickly implements mathematical concepts and perfectly works with matrices.
• Scala. Scala is another language that perfectly suits data science. It’s compiled Java bytecode that opens unlimited possibilities for data analysis. High-volume data sets are not a problem for this elegant language. It is a good choice for everybody who enters the big data world and plans to become a good programmer in this field.

Why Is Data Science So Important?

This list is not a full list but enough to choose if you are a beginner in data programming. Earlier we discussed the spheres where data is sometimes more important than knowledge and forgot about common daily tasks for data scientists that we feel like users. How do you think Facebook analyzes your preferences and shows news and advertising? Targeting advertising became a new word in sales. It follows you, predicts you and gives you exactly what you need at this moment.

Huge teams of data science developers work hard and create algorithms that are built on big data to sell you new smartphones or toys for your cat. It works right here and right now unlike long scientific research. It is not good or bad, it is just how it works in the modern world, and we must know it. So if you are worried that your work can be boring and only highly specialized then you can choose Google or Facebook and become a data programmer for them.

For a more structured and comprehensive approach to mastering these languages, the UNSW Master of Data Science program offers an in-depth curriculum that covers the essential programming languages and their practical applications in the field of data science.