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

Referring to the program, in this blog, we are here to guide you about the various programming languages ​​(or as they like to call it 'tools' in the tech world) which are essentially the tools of any data science. There are tools. Or studies related to machine learning. A very important question always comes up: "What language to use for data science?". Trust me as we say, there is a lot of debate between Python and R and which of them is more popular for data science. While we believe that both the languages ​​are equally important for any data scientist, there are other programming languages ​​which are important in data science and can be used as per the situation.

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

1. Python

Python is one of the best programming languages ​​for data science because of its potential for statistical analysis, data modeling and easy readability. Its extensive library support for data science and analysis is one of the reasons for Python's enormous success in data science. There are many Python libraries that contain a wide variety of functions, tools, and methods for managing and analyzing data. Each of these libraries has a specific focus with some libraries handling image and text data, data mining, neural networks, data visualization, etc. 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 because it was developed by statisticians, for statisticians. It is also very popular with an active community and many state-of-the-art libraries currently available. In fact, there are many R libraries that contain many functions, tools, and methods for managing and analyzing data. Each of these libraries has a special focus on managing image and text data, data manipulation, data visualization, web crawling, machine learning, etc., with a few libraries. 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 specially created to manage and retrieve data stored in relational database management systems. This language is extremely important for data science as it mainly deals with data. The main role of data scientists is to convert the data into actionable insights and hence they need SQL to retrieve the data from the database as and when required. There are many popular SQL databases that data scientists can use such as SQLite, MySQL, Postgres, Oracle and Microsoft SQL Server. BigQuery, specifically, is a data warehouse that can manage data analysis over petabytes of data and enables super fat SQL queries.

4. Matlab

MATLAB is a programming language for mathematical operations which automatically makes it important for data science. It allows mathematical modeling, image processing and data analysis. With a lot of mathematical functions 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. Apart from all this, MATLAB also has built-in graphics that can be used to create data visualizations with different types of plots.

5. Java

Java is one of the oldest programming languages ​​and it is also very important in data science. 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 in order to use Hadoop. In addition, there are many data science libraries and tools that are also in Java such as weka, mllib, java-ml, deeplearning4j, etc.


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1. Knime

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

2. Accord.net

Accord.net is a computational machine learning framework. It comes with an image as well as audio package. Such packages help to train models and build interactive applications. For example, auditions, computer vision, etc. Since .net exists in the name of the tool, the base library of this framework is the C# language. Accord libraries are very useful in testing as well as manipulating audio files.

3. Jupiter Notebook Note

Jupyter Notebook is one of the most widely used machine learning tools. Not only its a very fast processing language but it is also an efficient platform. Moreover, it supports three languages ​​viz. Julia, R, Python. Thus the name Jupiter is formed from the combination of these three programming languages. Jupyter Notebook allows the user to store and share live code as a notebook. One can also access it through GUI. For example, Win Python Navigator, Anaconda Navigator, etc.

4. Tensor Flow

TensorFlow is an open-source framework that also comes in handy for large scale numerical ML. It is a blender of machine learning as well as neural network models. Also it is a good friend of Python. The most prominent feature of TensorFlow is that 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 quick to use as well as flexible. This is because Pytorch has a good grasp on the GPU. It is one of the most important tools of machine learning as it is used in the most important aspects of ML, including Deep Neural Networks and Tensor Computation. 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 plethora of certifications to choose from that not only enrich your knowledge of data science and machine learning, but also enhance your practical experience with the above tools. You can use Python for data analysis and SQL data management. It is up to you to choose the right language for each individual project based on your objectives and preferences. And always remember, whatever your choice, it will only expand your skills 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 and run different programming languages. For example, some of them run on Python, some on C++ and some on Java.