7 Programming Languages for Data Science & Machine Learning

Learning how to apply coding knowledge is important for working in Data Science and Machine Learning. These tech fields require programming languages that can handle data collection, exploratory analysis, statistical analysis, machine learning model development, and many other assignments.

There are several programming languages with diverse features, tools, and libraries. This article describes seven programming languages: Python, SQL, MATLAB, R, C++, Julia, and JavaScript, showcasing their functions and responsibilities in each field, along with their advantages.

What Are the Most Suitable Programming Languages for Data Science & Machine Learning?

Python

Simplicity and comprehensibility are two things that characterize Python. This programming language is intuitive and performance-oriented through its organization around objects. It is very helpful for specialists, allowing them to create clear and logical code for various-sized projects.

In Data Science

Working in Data Science means handling large amounts of data. To make this data useful for business stakeholders, data scientists should evaluate and represent it. In this tech sector, Python has different responsibilities, which are the following:

• Data mining
• Data visualisation
• Data analysis
• Storage of large amounts of data

In Machine Learning

Python is a leader in Machine Learning and AI with its high-level libraries, for example, Scikit-learn and TensorFlow. Firms like Uber, Dropbox, Coca-Cola, and others use Python for various tasks in ML, including:

• Clustering
• Classification
• Dimensionality reduction
• Customer trends identification
• Creation of multi-step neural networks

Being in the top 3 most used programming languages, Python is essential for professionals in Data Science, with its over 130,000 libraries. It also has extensive libraries used for Machine Learning with tutorials and examples.

SQL

SQL, or Structured Query Language, is a programming language created to keep, retrieve, and manage data in relational databases. It is often used by Microsoft, Facebook, Accenture, and LinkedIn.

In Data Science

SQL is an obvious choice for experts in Data Science due to its ability to extract useful insights from data, as well as to store and manage it. It is commonly used for responsibilities that this domain demands:

• Extraction of valuable insights
• Data manipulation and aggregation
• Data transformation and filtering
• Data updating and deletion
• Common Table Expressions (CTEs)
• Creating and Managing Databases

In Machine Learning

For developers in Machine Learning, SQL offers robust frameworks for data evaluation, especially for large datasets. SQL can be integrated with the analytical and predictive skills of ML algorithms for task management, and has other activities, for example:

• Predictive modeling
• Classification
• Data retrieval and preparation
• Feature engineering
• Model training and evaluation
• Deployment and integration

This programming language is crucial for Data Science and Machine Learning, as it enables effective data retrieval & manipulation, offers a powerful framework for data management and analysis, and supports many other functions.

MATLAB

MATLAB is a programming platform for engineering and scientific functions. It includes a programming language, interactive apps, versatile specialized libraries, and numerous tools. It is used in various fields, including aerospace, electronics, Data Science, and Machine Learning.

In Data Science

This programming language can make Data Science easier with its versatile tools that can apply statistical, machine, and deep learning techniques with just a few lines of code. The following responsibilities are managed by MATLAB in Data Science:

• Data import and preprocessing
• Data representation
• Signal analysis and time series modeling
• Deep learning and neural networks
• Big data and distributed computing
• Domain-specific applications
• Integration with other tools and languages

In Machine Learning

MATLAB offers numerous opportunities for Machine Learning, including various apps and tools such as the Classification Learner App and Regression Learner App, both of which are used for data classification. It also has versatile functions in Machine Learning, like:

• Organization and preprocessing of data
• Clustering
• Classification and regression models
• Interpretation and evaluation of models
• Simplifying data sets
• Usage of examples to improve model performance

MATLAB has extensive libraries and tools suitable for tasks in these two industries. It is understandable why many experts in Data Science and Machine Learning use it for their projects.

R

The programming language that is open-source, free, suitable for data visualization, and statistical analytics is called R. It is used in many domains, including Data Science and Machine Learning, thanks to its ability to create graphs, store, manage, and process data.

In Data Science

This programming language is constantly developing and adapting to the evolving ecosystem of Data Science. It is an important tool for professionals in this domain to accomplish different functions and responsibilities:

• Statistical analysis
• Robust data visualisation
• Comprehensive data manipulation and cleaning
• Support for Machine Learning and AI
• Industry applications and use cases
• Integration with other tools
• Accessibility and open-source nature

In Machine Learning

R in Machine Learning allows specialists to create predictive models, find patterns, and search for insights leveraging statistical algorithms. The other operations include:

• Data cleaning
• Algorithms selection
• Model training
• Online fraud detection
• Virtual personal assistance

R is a good programming language for these two domains due to the benefits and functions it handles. It is also easy to integrate with other tools like C and C++, expanding its capabilities for higher efficiency.

C++

One of the most popular programming languages used in the world, which also helps various professionals in Data Science and Machine Learning, is C++. Its object-orientation allows code to be reused and can lead to resource savings.

In Data Science

C++ provides efficiency and performance with its libraries tailored for Data Science tasks. Even the most popular Python libraries used in this sector, like NumPy, SciPy, and Pandas, are actually invoking C++. Among the functions this programming language accomplishes are:

• Real-time data processing
• Large-scale simulations
• High-performance computing
• Systems programming
• Resource-intensive applications

In Machine Learning

In this tech field, C++ offers increased speed and performance, alongside the skill to integrate it with other codebases, resource saving, and compatibility across various sectors. C++ has the following tasks and responsibilities:

• High-performance computing (HPC)
• Cross-domain integration
• Embedded machine learning

Being one of the top-most used programming languages in the world, C++ is a highly efficient tool in the fields of Data Science and Machine Learning due to its high proficiency and increased speed.

Julia

Julia is considered a relatively new programming language, first released in 2012. It was mainly created for high performance on multiple platforms with support for interactive use and a reproducible environment.

In Data Science

Data Science actively uses Julia through its DataFrames.jl library, and is valued for its ability to handle massive amounts of data. The following operations are accomplished by Julia in Data Science:

• Data managing
• Data visualization
• Numerical simulation
• Computational speed
• Advanced computing packages

In Machine Learning

Julia is popular in Machine Learning and AI, thanks to the uncommon combination of efficiency, speed, and transparency, with other tasks to handle, for example:

• High-performance computing
• Integration with other programming languages, like R
• Machine learning frameworks
• Building neural networks

Julia is used by many important tech companies like Google, NASA, Microsoft, IBM, and many more. Aside from these major players, Julia is also leveraged by professionals in Data Science and Machine Learning due to its high performance and speed.

JavaScript

While JavaScript is a tool commonly used by Data Science and Machine Learning professionals due to its versatility and large ecosystem of libraries and frameworks, it remains the most critical component of the so-called presentational layer, things like web apps, mobile apps, and so on.

In Data Science

JavaScript is used by Data Scientists to build complex data visualisations and be part of R&D teams. It helps them become more communicative and versatile in their work. In Data Science, the main functions that JavaScript accomplishes are:

• Data visualization powerhouse
• Cross-platform flexibility
• Growing data science libraries
• Scalability and performance

In Machine Learning

One reason to use JavaScript in Machine Learning is its ability to run in a browser, easy working across various platforms, integration with web technologies, and its extensive libraries and frameworks, tailored for industry-related activities, including:

• Real-time image processing and recognition
• Natural language processing (NLP)
• Personalization and recommendations
• Speech and audio processing
• Gesture and pose recognition

JavaScript mainly helps Data Scientists to increase the communication of the data results. It is also favored in Machine Learning due to its front-end execution and accessibility to a large audience. These characteristics are very appealing to developers who work in these domains.

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What Are the Main Functions and Responsibilities in Data Science and Machine Learning?

The following table represents the main responsibilities and operations that the seven programming languages (Python, SQL, MATLAB, JavaScript, R, C++, and Julia) execute in Data Science and Machine Learning.

The Main Functions and Responsabilities in Data Science and Machine Learning

	Programming Language	Functions in Data Science	Responsibilities in Machine Learning
1.	Python	Data analysis functions and storage of large datasets	Data preparation, and Model Techniques & Lifecycle
2.	SQL	Preparation & analysis Database management and optimization	Data preparation and modeling
3.	MATLAB	Data handling and analytics Integration with other tools	Data organizing and modeling Interpretation and testing of models
4.	R	Data handling and visualization	Data modeling and search AI applications with real-world examples
5.	C++	Data processing, simulation, and modeling High performance and system development	High-performance and integration Embedded Machine Learning
6.	Julia	Data handling and visualization Simulation and modeling	High-performance computing and integration
7.	JavaScript	Data visualisation and performance	Real-time image processing, NLP Audio and speech processing

What Are the Main Perks of Each Programming Language in Data Science and Machine Learning?

In this table are described the main benefits of using the following programming languages: Python, SQL, MATLAB, JavaScript, R, C++, and Julia for the tech industries.

The Main Perks of Each Programming Language in Data Science and Machine Learning

	Programming Language	Benefits in Data Science	Perks in Machine Learning
1.	Python	Open source Versatility and scalability Extensive library and support from the community	Good library environment Flexibility Platform independence Easy to read and community support
2.	SQL	Easy and versatile Integration skills Understanding the datasets Managing large data volumes	No coding skills needed Advanced querying capabilities Integration with other languages and tools
3.	MATLAB	A large number of toolboxes Strong engineering credentials Tutorials and help from the community	Powerful visualization tools Integration with other languages Scalability and performance
4.	R	Open source Powerful visualization tools Offers essential packages Algorithms for future events prediction	Easy to read Strong modeling skills Large package ecosystem for every ML stage Active community support
5.	C++	Exceptional performance Control over system resources Strong concurrency support	Increased speed High performance Resource efficiency Compatibility across different platforms
6.	Julia	Can be faster than other programming languages like Python and R Versatile built-in data structures Easy to scale up Easy to read and write code	Easy to use High-speed code re-use and multiple dispatch Built-in package management Two-language problem
7.	JavaScript	Accesibility Compatibility with web apps Integration with the JavaScript ecosystem	Private Machine Learning Customized ML models Integration in web and mobile apps

Conclusion

Data Science and Machine Learning are tech fields that require high speed, performance, large data handling, and accessibility. These tasks are usually handled by programming languages. Seven of them discussed in this article: Python, SQL, MATLAB, JavaScript, R, C++, and Julia have various benefits for both these industries and can accomplish different operations.

To choose which one is more suitable for your project, you must carefully study every function and tool this programming language offers. To help you with this, you can look for companies that provide the required services on TechBehemoths.