Top 5 programming languages for data science
According to the U.S. Bureau of Labor statistics, data science is growing and in 2026 data science will grow about 28%. Which means that there will be around 11.5 million new jobs in this field. So is data science important and growing? Yes it is. So you know that the field is growing but what are the best languages to learn for data science? I am going to sum up the top 5 programming languages that are the most important for data science. I will also give links where you can learn the programming language.
Python
Python is booming at this moment. It surpassed the popularity of java, ruby and PHP.
Although it has a slow processing time if you compare it to java and Javascript. It is easy to learn. It’s syntax looks like plain English.
But Python is mainly popular in data science because it’s wide range of uses in data science. Python has a lot of strong library’s which you can use for data science. The more popular libraries in Python are Keras, Scikit-learn, Matplotlib and tensorflow.
Python is also great to use for collecting data such as web scraping, analysing the data and after that modeling the data. You can make a full data science project just by using Python and its libraries.
Python is best used for making scripts and automating tasks like web scraping. Which is really useful for data science
Pros and Cons
A pro is that you are not the first person to use Python for data science, there is a large community waiting for you to help you. Furthermore there are great libraries that you can use with great documentation.
A con is as said before, the speed. Python is slow for computation in comparison with Java or Javascript.
Learn it: I personally used datacamp for learning Python data science. This is because not only you learn Python but also SQL and R.
Costs: 21,66,- per month
I know freecodecamp is a good platform. I used it before for learning other skills. And some of my friends who are into data science recommended me this as a free course.
Costs: Free
Java
Java is also a good language to use. It can make anything from scrap and is very powerful. The computation time of java is way faster than any other language.
Some people say that Java is a beginner’s language but I disagree. Java can be quite hard to understand. It has a lot of components that you need to learn before you can make an application for data science.
A plus part of this is that you can make any app you want. There are almost no restrictions too it.
Java is best used for making complete applications/apps. You can build almost anything from scratch.
Pros and Cons
A pro is that Java is very fast at computing. This way you can scale your application easily.
A con is that Java is quite a hard language to understand and learn. So if you are a beginner wanting to learn to program and learn Data Science you will have a hard time learning that with Java.
Learn it: This is one of the best payed course for learning Java data science.
Costs: 13,99,-
Again a free code camp link where you can learn Java.
Costs: free
Javascript
With Javascript you can create interactive web pages. It is also the most popular language to learn. You can use Javascript for creating amazing visualisations. Nonetheless Javascript is more of an aid than a primary data science language.
Javascript is best used for web development
Pros and Cons
A pro is that you can create amazing visualisations
A con is that there aren’t that many libraries in Javascript that support data science
Learn it: I learned Javascript from this course. Codecademy has a good reputation so this one is definitely worth a try.
C/C++
I am going to refer to C/C++ too, just C. C is one of the older programming languages. C is just in almost everything because C is the building blocks at which most languages are built. That being said, C is very useful for data science because they can compile data quickly.
C is best used for projects that have massive scalability and performance requirements.
Pros and Cons
A pro is that as earlier said C is really fast at compiling. A gigabyte of data can be compiled in less then a second which is really useful for data science.
A con is that C is quite difficult to understand, this is because it has a low-level nature.
Learn it: C can be learned at a free website which is called. You can find almost everything about C and it is totally free.
Costs: free
SQL
SQL is probably more important than anything. This is because SQL is built for databases. And if you work with data you probably work with databases. If you want to make queries or retrieve data or update data. That is mostly being done by SQL.
Although you can not make applications with SQL nor can you make visualisations. It is a really important programming language to learn.
SQL is best used for retrieving data, updating data and deleting data.
Pros and Cons
A pro is that SQL is an easy to learn language you can probably get it under control within a day or 2.
A con is that SQL has difficult interfaces so it is not user friendly for you to make a query.
Learn it: A good way to learn SQL is by just following someone else do it. I always like Youtube because everything is free on there. Even the SQL course.
costs: free
Conclusion
To my conclusion the best programming language for data science is Python and SQL. Python is good for making scripts, retrieving data from a website, creating applications. And together with SQL to use the data from a database you can build really powerful applications. I will sum up all the pros and cons in a table:
