Most Popular Tools & Languages for Machine Learning and Data Science

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Machine Learning and Data Science are undoubtedly the most sought career choices in the last 5 years. Not only because of its popularity but also because the technical knowledge that leads to the above career courses is something that is easily available. Yes, you read that right. There are thousands of information available online that not only teaches but also encourages the practical establishment of one’s computer science skills. This continuous practice can be done from your comfortable home chair and enhance a bright career out of it. However, it is very crucial to know that machine learning and data science are not only about computers or computer science. It has the equal contribution of mathematics that provides the logic for the execution of a certain program.

Mentioning the program, in this blog, we are here to guide you about the various programming languages (or as they like to call it in the tech world, ‘Tools’) that are essentially going to be at the forefront for any data science or machine learning related study. A very important question always comes up: “Which language to use for Data Science?”. Trust me as we say this, there have been a lot of debates between Python and R and which of them is more popular for data science. While we believe that both languages are equally important for any data scientist, there are also other programming languages that are important in data science and can be used according to the situation.

In this article, we aim to provide all information about the most popular tools/programming languages for Machine Learning and Data Science. Let’s start with the ones for data science.

1. Python

Python is one of the best programming languages for data science because of its capacity for statistical analysis, data modelling, and easy readability. Its extensive library support for data science and analysis is one of the reasons for the huge success of Python in Data Science. There are many Python libraries that contain a host of functions, tools, and methods to manage and analyze data. Each of these libraries has a particular focus with some libraries managing image and textual data, data mining, neural networks, data visualization, and so on. For example, Pandas is a free Python software library for data analysis and data handling, NumPy for numerical computing, SciPy for scientific computing, Matplotlib for data visualization, etc.

2. R

When talking about Data Science, it is impossible not to talk about R. In fact, R is one of the best languages for Data Science as it was developed by statisticians, for statisticians. It is also very popular with an active community and many cutting-edge libraries currently available. In fact, there are many R libraries that contain a host of functions, tools, and methods to manage and analyze data. Each of these libraries has a particular focus with some libraries managing image and textual data, data manipulation, data visualization, web crawling, machine learning, and so on. For example, dplyr is a very popular data manipulation library, ggplot2 is a data visualization library, etc.

3. SQL

SQL or Structured Query Language is a language specifically created for managing and retrieving the data stored in a relational database management system. This language is extremely important for data science as it deals primarily with data. The main role of data scientists is to convert the data into actionable insights and so they need SQL to retrieve the data to and from the database when required. There are many popular SQL databases that data scientists can use such as SQLite, MySQL, Postgres, Oracle, and Microsoft SQL Server. BigQuery, in particular, is a data warehouse that can manage data analysis over petabytes of data and enable super fats SQL queries.


MATLAB is a programming language for mathematical operations which automatically makes it important for Data Science. It allows mathematical modelling, image processing, and data analysis. With a lot of mathematical functions that are useful in data science for linear algebra, statistics, optimization, Fourier analysis, filtering, differential equations, numerical integration, etc, MATLAB is one of the most popular languages. In addition to all these, MATLAB also has built-in graphics that can be used for creating data visualizations with a variety of plots.

5. Java

Java is one of the oldest programming languages and it is pretty important in data science as well. Most of the big data and data science tools are written in Java such as Hive, Spark, and Hadoop. Since Hadoop runs on the Java virtual machine, it is important to fully understand Java for using Hadoop. Moreover, there are many Data science libraries and tools that are also in Java such as Weka, MLlib, Java-ML, Deeplearning4j, etc.

Machine learning is an astonishing technology. It is fascinating to build a machine that behaves like a human being to a great extent. Mastering Machine Learning tools will let you play with the data, train your models, discover new methods, and create your own algorithms.
Machine learning comes with an extensive collection of ML tools, platforms, and software. Moreover, ML technology is evolving continuously. Out of a pile of machine learning tools, you need to choose any of them to gain expertise. This article has a list of the top 15 machine learning tools that are widely used by experts.

1. Knime

Knime is an open-source machine learning tool that is based on GUI. The best thing about Knime is, it doesn’t require any knowledge of programming. One can still avail of the facilities provided by Knime. It is generally used for data-relevant purposes. For example, data manipulation, data mining, etc. Moreover, it processes data by creating different various workflows and then execute them. It comes with repositories that are full of different nodes which are then brought into the Knime portal. And finally, a workflow of nodes is created and executed.

2. is a computational machine learning framework. It comes with an image as well as audio packages. Such packages assist in training the models and in creating interactive applications. For example, audition, computer vision, etc. As .net is present in the name of the tool, the base library of this framework is C# language. Accord libraries are very much useful in testing as well as manipulating audio files.

3. Jupyter Notebook

Jupyter notebook is one of the most widely used machine learning tools of all. Not only its a very fast processing language but also it is an efficient platform. Moreover, it supports three languages viz. Julia, R, Python. Thus the name of Jupyter is formed by the combination of these three programming languages. Jupyter Notebook allows the user to store and share the live code in the form of notebooks. One can also access it through a GUI. For example, winpython navigator, anaconda navigator, etc.

4. TensorFlow

TensorFlow is an open-source framework that comes in handy for large-scale as well as numerical ML. It is a blender of machine learning as well as neural network models. Moreover, it is also a good friend of Python. The most prominent feature of TensorFlow is, it runs on CPU and GPU as well. Natural language processing, Image classification are the ones that implement this tool.

5. Pytorch

Pytorch is a deep learning framework. It is very fast as well as flexible to use. This is because Pytorch has a good command over the GPU. It is one of the most important tools of machine learning because it is used in the most vital aspects of ML which includes building deep neural networks and tensor calculations. Pytorch is completely based on Python. Along with this, it is the best alternative to NumPy.

Now that you know the top programming languages for data science, it’s time to go ahead and practice them. Ivy Professional School offers a lot of certifications to choose from that not only enriches your knowledge about data science and machine learning but also increases your hands-on experience with the above tools. You may use Python for data analytics and also SQL data management. It is upon you to make the correct choice of language on the basis of your objectives and preferences for each individual project. And always remember, whatever your choice, it will only expand your skillset and help you grow as a Data Scientist!

So, these were some of the most popular and widely used machine learning tools. All these show how advanced machine learning is. All these tools use different programming languages and run on them. For example, some of them run on Python, some on C++, and some on Java.

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