Which programming languages should you pick up to start your data science journey?
Well, data science is obviously the most sought after field right now as it enables organizations to analyze and process data to obtain useful insights.
But since the battle between programming languages has always been a hot topic in the tech world. And given how fast technology is advancing, we have a new programming language or framework every few months.
This makes it harder for developers, analysts, and researchers to choose the best language that will get their tasks done efficiently while incurring the lowest cost.
Now, even though data science is highly trending, the demand for efficiency and high-performance results is skyrocketing.
And choosing the best programming language to tackle your day to day task is very important.
In this article, we’ll cover the best programming languages for data science.
But that’s not all.
I will also mention what you need to consider when you are choosing a programming language for your data science career path.
Let’s get started.
Frankly, Python is one of the best programming languages for data science because of its capacity for:
- Statistical analysis
- Data modeling
- Easy readability
Additionally, it is often the go-to choice for a range of tasks for domains such as Machine Learning, Artificial Intelligence, and deep learning.
Data science has been using Python for the longest time now and is expected to continue being the top choice for data scientists and developers.
For this reason, Python has grown a huge community base where you as a data scientist or a developer can ask questions and also answer queries for others.
We all know that Python has a lot of features that make it stand out from other programming languages, but do you know some of the key features?
Well, here are some of the key features that make this programming language stand out:
- Support for powerful Data Science libraries such as Keras, Scikit-Learn, Matplotlib, TensorFlow, and more
- Supports numerous file export and sharing options
- Comes with a strong community for getting support
Now, if you want to learn Python, you should check out my article on the best python courses on LinkedIn.
If we talk about data science, it is impossible not to mention R.
In fact, we can even say that R is one of the best programming languages for data science as it was developed by statisticians for statisticians.
As you may have already known, R is an open-source software environment that is primarily for handling the statistical and graphics side of data science.
Some of the statistical computing and analysis options provided by R include:
- Time series analysis
- Statistical tests
- Non-linear modeling
Apart from that, R is also popular and has an active community and many cutting edge libraries currently available.
Now get this, being a vector language, you can expect R to do many things at once like adding functions to a single vector without putting it in a loop.
Furthermore, R provides many options for creating excellent plots for data analysis.
Now, one of the most noticeable disadvantages of R is that it lacks inbuilt security.
This means that it cannot be embedded in a web browser for secure calculations. It is also difficult to use R like a back-end server for the purpose of building calculations
Standard Query Language is a domain-specific language used in programming and is designed for managing data held in a relational database management system.
As a data scientist, your role is to turn raw data into actionable insights, you will therefore primarily use SQL for data retrieval.
But wait, there’s more.
SQL serves a very crucial role in giving you the facts and statistics from a vast pool of data. And all this can be done with just a few queries.
Additionally, SQL has many popular databases that you can use. Some of these databases are:
- Microsoft SQL Server
You can also use SQL for managing huge amounts of data as it allows smoother management of the huge amounts of data.
This is one of the top programming languages for data science that a data scientist should have knowledge of because it excels at data visualization.
Now, if you thought that’s all.
You are terribly wrong.
This programming language is easier to learn and to use.
It also allows you to create visualizations for data analysis.
Now, this is one of the oldest programming languages on this list that is used for enterprise development.
Even though java is one of the oldest languages, it is also important in data science as most of the big data and data science tools are written in Java as Hive, Hadoop, and Spark.
Also, Java virtual machines are a popular choice for developers to write code for distributed systems, data analysis, and machine learning in an enterprise environment.
So, java offers several IDEs for rapid application development.
Additionally, java enables effortless scaling to build complex applications from scratch.
And finally, it is able to deliver results faster.
Even though java is a straightforward language, it consumes much memory since Java programs run on top of Java Virtual Machines (JVM). This could be problematic in systems without much internal memory.
C is the earliest programming language, but it still provides codebase for newer languages. one such example is R.
Now even though C++/C is among the more complicated side of programming languages for data science, it is increasingly being used to build tools that you can use for data science.
For example, TensorFlow, you might already know that its core is written in C++ while the rest of it is in Python.
But wait, there’s more.
C has the ability to deliver faster and better-optimized results when the underlying algorithms are also written in C.
You should also note that it is comparatively faster than other programming languages due to its efficient nature.
MATLAB is one of the best programming languages for data science that is most popular for its mathematical operations.
Look, data science deals with a lot in math, so having knowledge of MATLAB is important.
This programming language allows mathematical modeling, data analysis, and image processing.
With MATLAB you can actually tackle the trickiest statistical and mathematical problems with ease.
For its worth, MATLAB is primarily a mathematical computing environment designed for performing advanced numerical computations and comes with various tools that can help you carry out operations such as matrix manipulation, data and function plotting, and much more
This programming language also allows you to implement algorithms and user interface creation.
Additionally, MATLAB offers you built-in graphics for creating custom data plots and visualization.
Finally, it also enables seamless scalability.
This is an extension of java that was originally built on the java virtual machine (JVM). So you can easily integrate it with java.
However, the main reason why Scala is useful for data science is that it can be used along with Apache Spark to manage large amounts of data.
This means that when it comes to big data, Scala is the go-to language.
Now, the concurrency support between Scala and Apache makes Scala a perfect choice for building high-performance data science frameworks such as Hadoop.
Scala is stable, versatile, and can also deliver results comparatively faster under certain situations.
Additionally, Scala is surprisingly supported on various IDEs, such as IntelliJ IDEA, VS Code, Vim, and even in your browser.
Even though scala is one of the best programming languages for data science, you should note that one of its main disadvantages is that it has a limited developer pool.
This means that it could also be a hindrance for students who are trying to learn Scala and are looking for a mentor or guide.
If you want to learn Scala then check out my other article on the best Scala courses on Udemy.
Julia is a dynamically-typed multi-purpose programming language that is a suitable choice for numerical analysis and computational scientific analysis.
Even though it’s a high-level programming language, you can also use it as low-level programming if needed.
It also serves as an important tool for the data scientists because it supports both anonymous functions as well as higher-order functions.
Some of the notable features of Julia are:
- Focuses on delivering high-performance
- Built-in support for a package manager
- Offers data visualization, operations on multidimensional datasets, and robust tools for Deep Learning
- Support for parallel and distributed computing
Perl can handle data queries very efficiently as compared to some other programming languages as it uses lightweight arrays that don’t need a high level of focus from the programmer.
Plus, it is also quite similar to Python and so it is a useful programming language in Data Science.
In fact, Perl 6 is referred to as the ‘big-data lite’ with many big companies such as Boeing, Siemens, etc. experimenting with it for Data Science.
Perl is also very useful in quantitative fields such as:
- Statistical analysis
Now that you are already familiar with the top programming languages for data science, let’s look at what you need to consider when choosing a programming language for data science.
What To Consider When Choosing The Best Programming Language for Your Data Science Task
To have the best programming language for your data science task, you must ask yourself some questions.
Now, try answering the following 4 questions:
1. What is the scope of your project?
Look, this is an important question because you have to know the agenda for your project before picking up a language.
For instance, what if you want to simply solve a statistical problem through a dataset, perform some multivariate analyses, and prepare a report or a dashboard explaining the insights?
In this case, R might be a better choice because it has some really powerful visualization and communication libraries.
2. How experienced are you in the field of data science?
For a beginner in data science who has limited familiarity with statistics and mathematical concepts, Python might be a better choice because it lets you code the fragments of an algorithm with ease.
With libraries like NumPy, you can also manipulate matrices and code algorithms yourself.
As a novice, it is always better to learn to build things from scratch rather than hopping onto using machine learning libraries.
3. Which language is preferred in your organization?
Look at the industry you are working in and the most commonly used language by your peers and competitors. It might be easier if you speak the same language.
4. How much time do you have on hand, and what’s the cost of learning?
The amount of time you can invest makes another case for your choice.
Depending on your experience with programming and the delivery time of your project, you might choose one language over another to get started in the field.
Now that you know the top programming languages for data science, it’s time to go ahead and practice them!
Each of these languages come with their benefits, often offering better and faster results when compared with others.
For example, you may use Python for data analytics and also SQL data management.
Equipping yourself with more than one programming language can guarantee to help you overcome unique challenges while dealing with data.
If you are a budding Data Scientist, you should start with the programming languages mentioned above as they are the most in-demand languages right now.
And always remember, whatever your choice, it will only expand your skillset and help you grow as a Data Scientist!
Have you ever used any of these 10 best programming languages for data science before?
If yes, please share your experience in the comments below.