Top 6 Programming Languages for Data Science in 2022 and Tips to Choose them

8 min readJul 5, 2022

While data science is a relatively new field, there are many programming languages that can help you accomplish useful things within the discipline. Each language has its own strengths and weaknesses, but it’s possible to determine which ones are best by evaluating four criteria: popularity, community support, ease of learning, and performance. This article will discuss these criteria in more detail and explore the top six programming languages for data science in 2022.

What is data science?

Data Science is a field that uses data to solve problems. In the context of Data Science, data can be defined as any set of measurements or numbers that are recorded through the use of instruments and sensors.

The aim of Data Science is to extract insights from data and apply those insights to make decisions. In order to do this, one needs to have an understanding of computer programming languages as well as statistical concepts such as probability distributions (e.g., Gaussian distribution).

What programming languages do data scientists use?

There are many programming languages that you can use to code data science algorithms and build your own models. Let’s take a look at the most popular ones, as well as which ones I recommend for 2022!

Python — This is one of the biggest languages in this field. It has been around for years, making it easy for new developers to learn. Python also runs on Windows, Mac and Linux operating systems, so it’s extremely versatile. One drawback is that Python cannot be used to build web applications; however, if you’re interested in desktop apps or standalone programs then this won’t be an issue for you at all because Python was built with those goals in mind (and they’ve done a great job!). I like how simple it is while still having plenty of functionality — it’s easy enough for beginners but still robust enough for experienced programmers too!

What makes a good data science programming language?

Be fast, robust, efficient and portable.
Be open source.
Be deterministic.
Be easy to learn and use.
Have a large community of users who can help you if you get stuck in any way (bugging them is better than bug hunting).

1. Python for data science.

Python is a general-purpose programming language that can be used to build anything from web applications to games. The language was designed with readability in mind, and it has become the most popular introductory programming language at many university computer science programs.

Python’s popularity as a first language means that you can find help learning the language online or at your local meetup group. In addition, the Python community is very active on Stack Overflow where you can ask questions about your code so people with experience in the community can help you figure out what’s wrong with it and how to fix it!

Python also has an active open source community which means there are lots of libraries available for free on GitHub (and other platforms). These libraries cover everything from machine learning algorithms to Natural Language Processing techniques like sentiment analysis or document classification — which makes it perfect for building models for data science tasks!

2. Java for data science

Java is a general-purpose, high-level programming language that can be used to develop applications for the web, desktop and mobile devices. It’s also popular for data science due to its large ecosystem of libraries and frameworks.

Java was developed by James Gosling at Sun Microsystems in 1991 and first released in 1995. Today Java is maintained by Oracle Corporation (formerly Sun Microsystems).

If you want to get started with Java for your next data science project, there are many resources available online including tutorials on how to program in Java from scratch and even online courses like this one from Udemy.

3. R for data science.

R is a programming language and software environment for statistical computing and graphics. It is used by a growing number of statisticians and data miners for developing statistical software and data analysis. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. R is an open source programming language that can be extended through user-submitted packages.

R’s popularity stems from its ease of use: You can begin using it immediately without having to learn how to program. If you do know how to program in another language or environment such as Python/Scala/C++ etc., then R might be a good next step since it has many features like advanced data manipulation tools (which are missing in Python), close integration with high performance systems like Hadoop MapReduce or Spark MLlib where you can run large scale computations directly over your dataset without having them limited by memory constraints which may exist on your local machine if running python code under Anaconda with numpy etc., making it ideal for big data analytics projects where processing times are critical factors influencing results accuracy/quality metrics such as precision/recall values computed from cross validation results with different size training sets etc., which require extensive computational resources before becoming available at runtime during training phase only — hence being able to write efficient code will allow more parallelism during execution phase once all preprocessing steps have been completed successfully without any errors thrown up by any interpreter(s) used during compilation phase itself!

4. SQL for data science.

SQL stands for structured query language, and it is a declarative programming language that allows you to query and manipulate data in a database. SQL is an example of a multi-paradigm language because it supports procedural, object-oriented and functional styles of programming. It uses a high-level language (SQL), so you can express queries using English-like statements.

In the context of data science, SQL plays an important role as it allows users to query datasets stored in databases without having to know how the underlying system works. This makes it easier for people with limited knowledge about computer science or software engineering concepts to use databases effectively for their projects.

5. MATLAB for data science.

MATLAB is a programming language used for data analysis and visualization. MATLAB functions as an interactive environment where you can develop applications to perform numerical computations, visualize data, create algorithms and models. It is used in science, engineering, and finance.

You can use MATLAB for:

Data analysis
Visualization of data
Development of algorithms and models

6. Julia for data science.

Julia is a high-level, high-performance dynamic programming language for technical computing. It provides a sophisticated compiler, quantitative analytics, parallelism, and an extensive mathematical function library. Julia’s syntax is familiar to users of other technical computing environments, but the language also incorporates approaches from languages like Python and Lisp. Julia has been designed from the beginning for parallelism and distributed systems.

7. GO for data science

GO is a general-purpose language built with the aim of creating easy-to-maintain, fast and efficient software. It is statically typed, compiled and runs on the GO runtime. As it is a garbage collected language, it does not require manual memory management by the programmer like C or C++ do.

GO uses interfaces to achieve modularity and code reusability in programs written using GO. The syntax of GO is similar to that of Java but has some minor differences as well.

GO also provides some unique features like concurrency support through goroutines (light weight threads) which makes it suited for distributed computing tasks such as data science where multiple processes need to run in parallel on a single machine or on multiple machines connected over a network connection without any hiccups due to resource contention issues between them

8. Scala for data science

Scala is a general-purpose programming language that runs on the JVM. It is a hybrid object-functional language and can be used as a multi-paradigm language because it supports not only object-oriented but also functional and procedural programming. Scala is statically typed and compiled, which makes it very fast compared to Python or R in most cases.

Scala has been around for more than 10 years now, but it took some time for developers to realize its potential in data science because of certain features like immutability and static typing that were not present in other languages such as Python or R. However, these features make Scala one of the best choices for data science today because they allow developers to avoid common issues such as unexpected errors due to mutable variables being passed around (which happens often in dynamic languages like Python) or having problems with type casting between different types of data (which happen when trying to convert strings into integers).

How to choose the best programming language for you as a data scientist (or aspiring data scientist)?

Choosing a programming language for data science is not easy. There are many options, and the choice of a programming language depends on your personal background, skills and the project you’re working on.

To choose the best programming language for you as a data scientist (or aspiring one), consider:

What industry are you in? What languages are most commonly used by your peers and competitors?
Do you know what type of project you want to use this language for? Is it an analytical project or more of an exploratory one?
Are there any resources available that will help with learning these languages? If so, which ones would be most useful in helping me get started with them now or later down the road when I am ready to learn more about using them effectively while completing my current tasks at hand..

When it comes to choosing the best programming language for you, one of the most important things to consider is what problem you want to solve.

For example, if your goal is to build a chatbot that can handle customer service inquiries and route them from there into a ticketing system, then Python might be the best choice for you. If your goal is instead to create an autonomous vehicle and train it using deep learning techniques so that it can drive itself safely on roads without human intervention (and all while maintaining an aesthetically pleasing appearance), then C# may be more appropriate.

If this sounds confusing or overwhelming, don’t worry: we have made sure each section below includes links at the bottom which will take you straight through to other articles where we explain these concepts in detail!

Conclusion

When it comes to choosing the best programming language for you, one of the most important things to consider is what problem you want to solve. Are you interested in creating an app that can be used by millions of people? If so, then Python might be a good option because it’s easy to learn and has great support from the community. If on the other hand you’re looking for something more challenging or advanced, Java would probably suit your needs better than Python because it has many more features including concurrency which makes multi-threading easier and faster (although this is still not as fast as C).