Top 8 IDEs for Machine Learning and Data Science You Should Know
Finding the best match IDE with all needed features for your tasks when there are various similar tools on the market might seems challenging, especially if you are in data science or machine learning engineering.
Here we’re going to overview the most popular and efficient IDEs supporting R, Python, Scala langs, commonly used for data science.
If you are a data scientist or a machine learning engineer, then you should be familiar with the most efficient machine learning IDEs.
An IDE e.g. integrated development environments are tools that allow software developers to write, test, debug and work with code easier than most universal text editors. An IDE serves for the next purposes as code validation, a text editor, syntax highlighting, completion, contextual suggestions, debugging tool, method and class specification, resource management, and easy access to help.
IDEs generally have extensive collections of features. Because of this, they are beneficial for data science programming too. They make the lives of programmers considerably easier. What is the recommended IDE to learn machine learning? There is some contention as to the answer to that question, but we will endeavor to cover some of the top candidates.
In this article, we’ll briefly review several IDEs that are available for the four programming languages that data scientists most frequently use. Those are R, Python, Scala, and Julia.
RStudio
OS: macOS, Windows, Linux
First release date: December 2019
Last release date: April 2011
RStudio is the most feature-rich IDE for the R platform. It is open-source, but there’s also a commercial edition for desktops, including Windows, Mac, and Linux. You can also use it in a Linux server running RStudio Server or RStudio Server Pro.
It features syntax highlighting, smart indentation, and code completion. R code can be executed directly through the source editor. The developer can quickly jump to reading help, function definition, and documentation. You can also easily manage multiple working directories using the project feature. RStudio has integrated support for Apache Subversion and Git.
StatET Plugin for Eclipse
OS: macOS, Windows, Linux
First release date: December 2019
Last release date: September 2010
Eclipse is one of the most popular Java IDEs, and it allows the installation of plugins to support different programming languages. StatET is an Eclipse-based IDE intended for use with R. It features a set of tools for R coding and package building. This includes a fully integrated R console, package manager, object browser, debugger, data viewer, and R help system. Many local and remote installations of R are supported through it.
The code editor has syntax highlighting, auto-correction of line indentation, text folding of Roxygen comments, function definitions and other blocks, and auto-indentation with typing and pasting.
A visual debugger such as this one allows for the simple managing of breakpoints and conditional breakpoints. This debugger features a clearly presented call stack and traceback with direct access to variables of the selected frame. There is also access to the source code and instruction pointer, which in this case would be the R Editor, and you can refine your source code with it as well. The ease with which novices can master it makes it one of the best data science IDEs.
R Tools for Visual Studio
OS: macOS, Windows, Linux
First release date: March 2016
Last release date: March 2017
No list of the top 10 machine learning IDE candidates would be complete without mentioning this one. Visual Studio is a widely used IDE for .NET languages and C++ and other popular programming languages.
R Tools for Visual Studio (RTVS) is an open-source extension for Visual Studio developed under the MIT license. It is free of cost.
With Visual Studio, data scientists are capable of organizing and managing related files in a convenient structure. They can take advantage of useful templates for items such as R documentation, R code, R Markdown, SQL queries, and stored procedures.
RTVS can bind to both local and remote workspaces. This allows developers to develop R code locally with smaller data sets. They can then easily run the code on more powerful cloud-based computers with much larger data sets.
R-Brain
OS: macOS, Windows, Linux
First release date: 2017
Last release date: 2019
The next one IDEs for data science we are going to discuss is R-Brin.
Moreover, with R-Brain you can integrate various IDEs such as Jupyter Lab, Jupyter notebooks, Zeppelin, Rstudio, or Theia and deploy the application, no matter what the framework it uses, in a few clicks.
It supports an integrated cloud database and serves as an on-premises data science platform. It supports popular open-source languages. R-Brain is powered by Jupyter and offers an IDE, a console, a notebook, and a markdown structure that are all integrated into one environment with complete language support for both R and Python. It includes intelligent code completion, debugging, packaging, and publishing capabilities.
R-Brain has standard features set of the well-known Jupyter Notebook like an interactive notebook interface, terminal, text editor, file browser, rich outputs, and more. They all operate within a flexible user interface. It uses Docker container technology, so this solution can be deployed on-premises or in the cloud.
PyCharm
OS: Windows, Linux, macOS
First release date: 2010
Last release date: October 2019
PyCharm is developed by JetBrains, a company that has developed IDEs for different programming languages.
PyCharm’s code editor provides extensive support for Python. It’s possible that it could be named as the best Python IDE for machine learning. It features error detection, code completion, and automated code fixes. It also has a smart search feature which can jump to any class, file, symbol, or any IDE action or tool window. With one click, you can switch to the declaration, super method, usages, testing, implementation, and more.
PyCharm includes a vast collection of tools. Among them are an integrated debugger and test runner, a Python profiler, and a built-in terminal. You can integrate it with major version control systems, including Git, SVN, and Mercurial. It also has remote development capabilities, an SSH terminal, and integrations with Vagrant and Docker.
PyCharm supports integration with Jupyter Notebook. It has an interactive Python console and supports Anaconda as well. It also integrates with scientific packages including Matplotlib and NumPy.
Spyder
OS: macOS, Windows, Linux
First release date: 2009
Last release date: December 2019
Spyder is a scientific environment designed for use by scientists, engineers, and data analysts. It offers a set of functionalities such as editing, analysis, debugging, and profiling. It is a comprehensive development tool capable of data exploration, interactive execution, deep inspection, and superb visualization options. Its abilities can be further extended via plugins and API.
Spyder has a multi-language editor with a class browser, code analysis tools, automatic code completion, go-to definition, and horizontal and vertical splitting. The other pros about using Spyder is the great community support you can get along with entire complete documentation.
Scala IDE for Eclipse
OS: macOS, Windows, Linux
First release date: September 2017
Last release date: April 2010
What IDE does use machine learning production codes? Scala IDE for Eclipse would be one example. It provides complex editing and debugging support for the building of Scala and Scala-Java based applications. It allows references from Scala to Java and vice versa.
As with any modern IDE, it has code completion, semantic code highlight abilities, and go-to definition. It also catches compilation errors as you type.
Scala Plugin for IntelliJ IDEA
OS: macOS, Windows, Linux
First release date: February 2020
Last release date: September 2009
IntelliJ IDEA is another IDE from JetBrains. It is known for ergonomics and the intelligent coding assistance it provides for developers using Java, JavaScript, and other languages. The Scala plugin expands IntelliJ IDEA’s toolkit with support for SBT, Scala, Scala.js, Hocon, and Play Framework.
It features coding assistance, navigation, search, information about various types, and also integration with SBT and other build tools.
Geany
OS: macOS, Windows, Linux
First release date: October 2005
Last release date: September 2019
The best IDEs for data science with Python include much of what you’ll see with Geany. The IDE was created by Enrico Troger and officially released on October 19, 2005.
It has been written in C & C++ and designed primarily a Python machine learning but has support for over 50 program langs now.
Another advantage of Geany is that it is quite powerful while lightweight at the same time. One more thing about Geany’s editor, it supports highlighting of the Syntax and line numbering for your projects.
Rodeo
OS: macOS, Windows, Linux
First release date: 2016
Last release date: January 2017
Of all the IDEs for data science and machine learning, Rodeo is one of those that is the most versatile. This particular IDE uses IPython kernel and was authored by Yhat. It is famous for its ability to let users explore, compare, and interact with data frames and plots. Thought, Rodeo has not been updated for the last time.
Some of the other IDEs that are worth mentioning are Jupiter Notebooks, Juno, Atom, Sublime Text 3, and the Julia extension for Visual Studio Code. Each one of these has some attributes that make them well worth investigating.
If you are interested in data science, then you should be aware of the IDEs that we’ve mentioned. Which IDE for Python data science is for you?
Well, you might not find a use for all the ones on this list. The more of them with which you familiarize yourself, though, the more equipped you will be to construct a programming code that perfectly suits your purposes. More data science IDEs come to light every day, and it’s worth it as a programmer for you to stay up to date with them.
