The data science industry is an emerging field. It currently has a high demand, especially with the market and the advancing studies regarding our activities with technology.

If you’re starting to get cold feet about taking on this industry, we’re here to help you take the first step: choosing the best data science programming languages for you.

We’ve gathered some of the most popular programming languages commonly used in data science. Pick up a few of these, and you’re on your way to becoming an excellent data scientist.

The Best Data Science Programming Languages

Data analysis and compilation are key ingredients in the field of data science. That’s where programming comes in.

Learning the best programming language will help you progress in data science. Certain programming languages may be able to do a lot in data science, while some may serve as an introduction to more complex programming languages.

Here are the top programming languages and a few of their frameworks you can learn. Note that you’re not limited to just one language. Be open to learning as much as you can.

1.) Native iOS Swift

Developed by Apple, Swift is a programming language used to create and grow the application ecosystem and increase customer retention of iOS.

However, as it was further developed, it expanded from the iOS ecosystem and can now work on Linux. The language has also become compatible with TensorFlow and interoperable with Python.

Its numerous libraries and data analysis features can help scientists use this language for data science through machine learning and AI.

This programming language is best for mobile developers curious about data science.

Pros

• Readable

• Easy to learn

• Open-source

Cons

• Only for newer iOS versions

• Weak cross-platform support

2.) Native Android Kotlin

If you’re looking for an easy code to learn for Android, Kotlin might help you get started. They’re a Google-supported open-source programming language specifically for Android development.

It requires less code to get results, making learning easy for those with zero programming knowledge.

It’s also compatible with Java, making combining existing codes between the two programming languages for data easier.

This object-oriented language can help close gaps between the different niches of data science for clearer and more concise results.

Pros

• Lightweight

• Faster compilation

• Easy to use

• Null Safety

Cons

• Young language

• Difficult to find references

• Library and documentation

3.) Dart Language

Dart is a client-optimized language for developing fast applications.

This programming language is easy to learn for any aspiring data scientist, especially if you already have a background in Java. However, your background may also affect your learning curve as a programmer and a data scientist compared to other languages.

This object-oriented and web-based language can be easily compiled, like JavaScript, which is important for faster and more reliable data analysis.

One of Dart’s famous software development kits is Flutter, a collection of tools, libraries, and frameworks that can help build cross-platform applications.

With the added features from Dart’s packages, you can use Flutter to create deep learning and machine learning algorithms and applications perfect for data analysis and machine learning.

Pros

• Easy to learn

• Null safety

• Reliable and quick compilers

Cons

• Low learning curve for Java users

• Limited courses and books

4.) JavaScript

JavaScript is one of the most popular programming languages to learn among web developers. While it isn’t a primary data science language, it is a great aid in the study.

It’s best known for web development because of its capability to build interactive web pages. However, this skill can still be used by data scientists for visualizing data.

It can be a language for data science, given it’s a versatile language that aspiring data scientists can easily learn.

It’s also a functional programming language, making it great for list-processing applications and symbolic computation.

JavaScript has made many frameworks that are constantly used in data science because of all its capabilities. Some examples are React Native, Nest.js, and Node.js. These can all be used to automate and streamline the complex problems of data science.

Pros

• Visualizations

• Easy with big data

• Easy to learn

• Has different toolsets

Cons

• Built-in functionality

• More for web development

• Lacks data science packages

5.) PHP

PHP is a general-purpose programming language best suited for web development. Its code can easily be embedded into HTML or exported to other languages, like Python.

It’s a simple programming language to learn, especially for beginners. It can also be a good gateway to other programming languages, given how its code is closely related to well-known languages for data science.

In terms of data science, it’s generally used for its many libraries for data analysis.

PHP’s most famous framework is Lavarel, which is commonly used to create virtual assistants and messaging applications for the web. However, it also has features that you can use for data analysis.

The framework can create amazing AI and machine-learning models thanks to PHP’s many libraries and packages.

Pros

• Great support and documentation

• Secure

• Strict Parameters

Cons

• Slow

6.) Java

Java is the most popular programming language for building large AI and machine learning applications. It’s easy to learn for beginners. It’s flexible as it can be both compiled and interpreted language.

Thanks to their Java Virtual Machine (JVM) ecosystem, it’s an easy entry to other languages for data science.

It can easily handle complicated tasks needed by data scientists like natural language processing, data analysis, data mining, and deep learning. It’s why Java is a popular language.

One of Java’s most famous frameworks for data analysis and machine learning is Elastic Search. Thanks to its simplicity and accessibility, any data scientist can pick up this framework.

This framework can easily find patterns and important information through machine learning features for faster analysis and forecast of your data.

Pros

• Fast language

• Portable

• Easy to learn

• Java Virtual Machine ecosystem

Cons

• Disciplined language

• Less support and networking opportunities

7.) R Language

When it comes to data science programming languages, the R programming language is already making a name for itself.

It’s a powerful scripting language that can handle high-volume data sets. Its features make it great for machine learning and big data analysis while allowing for statistical computing and graphics.

R language has statistical models and packages, which include neural networks, nonlinear regression, advanced plotting, and many more. It’s also good for data manipulation and visualization.

Pros

• Easy to learn

• Large amount of support

• Multiple packages

• Machine learning operations

Cons

• Lacks basic security

• Can’t imbed to web application

8.) Structured Query Language

SQL is a domain-specific language for data science best used for handling structured data. This feature makes SQL the perfect programming language for data analysis and statistical analysis.

It’s best used for relational databases for a data scientist to easily relate different data points. It’s also commonly used in business analysis and data manipulation.

Pros

• Non-procedural language

• Easy to learn

Cons

• Difficult interface

• Expensive

9.) Python

Python is known as the top data science programming language best known for its problem-solving and natural data processing.

It’s a high-level programming language best used for automation. It performs well in data analysis, modeling, and visualization, which are necessary for data science programming languages.

It’s great for accessibility compared to programming languages like the Statistical Analytical System (SAS) which may need a license.

Pro

• Popularity

• Support and resources

• Open-source tools

Con

• Speed

• Slow computation

10.) Julia

Julia is a rising multi-purpose programming language for scientific computing and numerical analysis.

It’s great for collecting business intelligence because of its features for time-series analysis, risk analysis, and more.

It’s also one of the programming languages for data science that supports parallel and distributed computing which is essential for the statistics of businesses.

The many functions of Julia for deep learning and numerical computing make it a well-known language for data analysis and science.

Pro

• Easy to learn

• Syntax inspired

Con

• Not a lot of support and resources

• Low talent pool

What Is Data Science?

Data science is the study of information to be used by companies and establishments. All this information is used to make business decisions, create strategies to improve and solve complete problems.

It covers machine learning, artificial intelligence, and deep learning algorithms.

With data science, you can automate and streamline many tasks and data for easy analysis using data science programming languages.

Why Should You Pursue Data Science?

As of now, the data science industry is still growing. That means high demand and many employment opportunities.

The salary of an average data scientist in the USA is significantly higher compared to a typical data analyst.

When you take on data science jobs, you’ll be open to opportunities while learning skills and earning a good income.

How Is Programming Used in Data Science?

Data science deals with big data. You’ll have to find a way to manage this data and make them easier to analyze.

That’s where programming languages for data science come in. By coding, you create an analytical model and algorithm to analyze and solve complex problems.

Your computer will do the job through neural networks and machine learning. You can also recycle analytical models from coding language for similar data management problems.

What Are the Different Types of Data Scientists?

Each data scientist specializes in and explores a different niche within the study.

We’ve gathered a few specific specializations you can look into.

These are only a few of the many types of data scientists. More jobs are opening up in the data science career path with how it grows every day.

1: Statistician

A statistician uses theoretical and applied statistics to improve a company’s goals and how they can improve.

The key skills for statisticians are confidence intervals and data visualization. They should be able to present their statistical analysis for others to understand.

2: Software Programming Analyst

A software programming analyst is responsible for mathematical calculations using programming.

They adopt new programming languages for data visualization and data analysis. They can also automize large data-related activities to reduce computational time.

3: Business Analytic Practitioners

These data scientists need a good background in business intelligence and data analysis to succeed in this niche.

Their job is to gather and analyze data to be used in important decision processes. These include high-level database design, return on investments, optimization, and much more.

4: Data Engineers

Data engineers are responsible for designing, building, and managing the information captured by the organization.

They create a data-handling infrastructure that analyzes and processes data in line with the organization

5: Machine Learning Scientists

Machine learning scientists explore innovative approaches and examine new algorithms for understanding data.

It allows for suggestions on pricing strategies and demand forecasting for businesses, to name a few of its benefits.

How Can I Get Started in Data Science?

Data science doesn’t need a 4-year course for you to get into the field. However, it does help to be highly educated in big data and math. These can all be managed through data science programming language.

Here are a few tips to get you started in building your data science career through programming languages.

1: Learn the Basics of Programming

All data science jobs would require some level of programming.

It would help if you had at least the basics of understanding how to program before getting into the field or a specific niche.

Know what programming languages are involved and the problems programmers face when writing code for applications.

2: Familiarize Yourself With Machine Learning Techniques and Algorithms

Machine learning is the most popular area in data science.

You need to understand how machine learning works and which algorithms are popular. Knowing how these are all used gives you a better understanding of the data science goals.

You don’t need to be a statistician or machine learning expert. But having the fundamentals, such as linear algebra and decision trees, on your belt should help you pick up the job.

3: Learn From Fellow Data Scientist Peers

Since data science is still an emerging career, you’ll find data scientists left and right. You can easily find a mentor to help you get through the first steps of starting your data science journey.

Learning along with others is also a great way to pick up skills and compare notes. It would be like working with your classmates in high school.

Look into projects and classes from nearby companies so you can hone your skills with fellow data scientists.

4: Ramp Up the Difficulty

You don’t stop just as soon as you gain data science skills. You’ll have to look for a challenge sometime soon.

As soon as you’ve got the basics down, try harder problems and techniques.

Once you’re used to general-purpose programming language, try picking up new data science languages. When you’re through with simple linear regression models, bump it up to logistic regression.

There’s always room to grow along with this developing field.

Frequently Asked Questions

You might have a few more questions about programming languages for data.

We’ve looked into the different libraries of questions to collect the commonly asked ones from streamlining your research.

Do You Need Coding Expertise to be a Data Scientist?

While companies don’t require expertise, a background in coding language is necessary to understand data science.

Tasks in data science can be challenging, especially if you don’t have prior experience in programming.

Get the basics and build on your skills as you progress.

Are C and C++ Used in Data Science?

Yes, you can use C and C++ in data science. These are under Objective-C, one of the earliest programming languages.

C and C++ can compile data quickly with a broader command of applications.

This object-oriented programming language is best used for projects with extremely large data and many performance requirements. C and C++ can easily compile a gigabyte of data, making them perfect for this job.

However, given that it’s a low-level programming language, it can be complicated for beginners to learn this language.

How Long Does it Take to Learn Data Science Programming?

The length usually depends on the person and their way of learning data science programming.

The average learning time for someone without experience in mathematical and statistical computing is 7 to 12 months of intensive studies to reach the entry-level.

However, learning doesn’t stop with boot camps and online classes. Those are all theoretical approaches to data science.

You’ll have to continue learning while you work. You might even be able to pick up more programming languages and skills to grow with data science itself.

The best way to master your skills and gain job-ready proficiency is by constantly practicing.

Conclusion

There is no single best programming language for data science. Each programming language can solve certain problems that other languages can’t.

When starting your data science journey, see which programming language would be best for you to learn.

Always be open to learning more languages and continue to build on your skills as you go.