Top 8 Programming Languages for Data Science
Programming languages of the future for data scientists
ChatGPT is a game changer for data science. No secret that data scientists use this tool to analyze data and fix errors.
However, you need to know at least one programming language to perform complex projects in machine learning and deep learning, which are subfields of data science.
Fortunately, there are many programming languages you can use in data science. Note that these languages have advantages and disadvantages.
The first language I learned 13 years ago was R programming. At that time, R was the more popular language. But in the last decade, Python has become the most used language in data science and AI. After learning R, it was a piece of cake for me to switch to Python.
After gaining experience in data science, I realized that it was more beneficial to use a language suitable for the project.
Today, we’ll go over the 8 most used programming languages in data science and discuss their pros and cons.
Let’s dive in!
1. Python
Python is a silver bullet to overcome any data science task.
The most used programming language for AI, data science and automation is undoubtedly Python. This is because its syntax is simple. You can write your code in Python just as if you were speaking to someone in English.
To carry out your data science projects, you don’t need to code from scratch. Python has awesome libraries to easily implement your project.
Let me briefly explain these libraries.
Pandas and NumPy are king when it comes to data manipulation. Matplotlib and Seaborn are great for data visualization. Scikit-Learn, TensorFlow, PyTorch are excellent for your machine learning analysis.
My first advice to those just starting a data science career is to learn Python. After learning this language, you can jump into NumPy, Pandas, Matplotlib and Scikit-Learn libraries.
2. SQL
SQL is the key to manipulating data in databases.
We now live in the age of big data, right? This big data lives in databases. Python is great for data analysis, but SQL is king when it comes to effortlessly working with data in databases.
With SQL, you can manipulate the data in the database and even perform simple data analysis. You can leverage relational databases such as MySQL and PostgreSQL to work with SQL.
3. Bash
Bash is great for creating pipelines on the command line.
Most people think of Bash as a traditional programming language.
Let’s take a step back and think. Imagine you have a large text file on your computer. It’s hard to manually search or filter your file, isn’t it? You can easily do this with bash.
With Bash, you can seamlessly build pipelines to perform tasks like data extraction, data transformation, and data loading.
4. R
R is a free software environment for statistical analysis and data visualization.
When it comes to data manipulation, data analysis and data visualization, R is king. While its name may be short, its capabilities are truly remarkable.
It has many built-in functions and libraries. Let me explain a few of these libraries.
ggplot2 is a very useful tool for data visualization. dplyr is a versatile data manipulation library that allows you to filter, summarize, organize and transform data. tidyr helps you reshape and organize data. caret is a comprehensive library for machine learning projects that provides tools for data preprocessing, model training, and evaluation.
In short, R is very powerful for data cleaning, data preprocessing, data visualization and statistical analysis.
5. Julia
Julia executes like C, however reads like Python.
Looking for speed in data science? Then the Julia language is for you. For high-performance numerical operations, Julia is king.
As mentioned, the most widely used programming language for data science today is undoubtedly Python. But Python is slow because it is an interpretative language. Most of the Python libraries used for deep learning are written using C++.
This is where Julia comes in. Julia is a compiler language, so it is as fast as C which close to machine language.
PyTorch, the machine learning library for Python, was written using C++ 52.6% and Python 36.6%. Flux.jl, a machine learning library, is written with Julia 100%.
Julia’s syntax is also easy to learn, as it is inspired by Python and R.
In summary, Julia is a modern programming language for data scientists: it’s fast and easy to use.
6. Rust
Rust is a language that enables anyone to create reliable and high-performance software.
Rust is unknown to most data scientists, but stands out as an alternative choice in data science due to its outstanding performance and memory security.
With no runtime or garbage collector, Rust provides exceptional efficiency when dealing with large datasets.
But remember, Rust is relatively new to data science and has fewer libraries compared to Python. This means that you may have to write most of your code from scratch for your data analysis projects.
To be honest, I haven’t used Rust in my data science projects, but from my research, Rust seems to offer opportunities for creating data pipelines and handling big data.
7. C++
C++ is a cross-platform language that can be used to build high-performance applications.
C++ was designed as an extension of C, and both languages have almost the same syntax. It is awesome for your high performance and complex projects.
Note that C++ is not used as much as Python or R in data science projects, but it can be preferred for projects that require speed.
Plus, since C++ has low-level memory management, you can easily work with big datasets.
8. Scale
Scala perfectly integrates features of object-oriented and functional languages.
If you’re looking for a cleaner language with fewer words than Java, Scala is for you. Scala runs on the Java Virtual Machine (JVM) and supports the Java language.
One of the strengths of Scala is that it can combine object-oriented programming with functional programming.
As you know, Spark is a great framework for working with big data. It’s written in Scala. This means that Scala is Spark’s native language. However, you can also use Python or R to work with Spark.
You don’t have to know Scala to be a data scientist. But if you want to pursue a career in data engineering or databases, knowing Scala will set you one step ahead of other candidates.
Mojo
Mojo is seen as the language of the future in AI because it doesn’t require C++ or CUDA.
As a bonus, I’d like to mention another programming language: mojo.
It is a new language that combines the performance of C with the simplicity of Python. Mojo is said to be 35,000 times faster than Python.
Let me explain to you how this happens.
As you can see below, while Python performs the operations one by one, Mojo carry-outs these operations by distributing them to multiple cores.
Remember that this language is under development.
Wrap-Up
In this article, I covered 8 programming language for data science. To implement your projects, you need to learn at least one programming language.
Remember, every language has advantages and disadvantages.
If you are new to data science, I recommend learning Python first. When it comes to performing statistical data analysis and data visualization projects, you can learn R or Julia.
If you need high performance and memory management, C++ and Rust are good options for you. Bash is very useful for automation and data pipelines. SQL is a great language for dancing with data in databases.
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