Eight best IDEs for data scientists – INDIAai

In-depth and nuanced coverage of leading trends in AI One
Latest updates in the world of AI
Information repositories on AI for your reference
A collection of the most relevant and critical research in AI today
Read the latest case studies in the field of AI
Curated sets of data to aid research initiatives
The best of AI brought to you in bite-sized videos
World-class policy developments and accepted standards in AI development
Roles spanning various verticals and domains in big data and AI
Latest events in AI locally and internationally
Pieces covering the most current and interesting topics
VCs, PEs and other investors in AI today
Top educational institutions offering courses in AI
Profiles of visionary companies leading AI research and innovation
India’s brightest and most successful minds in AI research and development
A glimpse into research, development & initiatives in AI shaping up in countries round the world
Read all about the various AI initiatives spearheaded by the Government of India
Latest initiatives, missions & developments by GoI to drive AI adoption
Follow INDIAai
About INDIAai
Subscribe to our emails
Home

By Dr Nivash Jeevanandam
Integrated development environments (IDEs) let programmers write code efficiently. It boosts developer productivity by merging software editing, building, testing, and packaging.
An IDE allows programmers to access multiple tools and information in one place. As a result, IDEs are the best coding tools for data scientists due to their user-friendliness and features like syntax highlighting, tool integration, keyboard shortcuts, and parsing. 
The top IDEs for data scientists are discussed here.
Jupyter notebook
Jupyter notebook is an open-source IDE for creating Jupyter documents, which can be created and shared with live code. It is also an interactive computational environment that is accessible via the web. The Jupyter notebook can support a variety of data science languages, including Python, Julia, Scala, R, and others.
Atom
Atom is a robust IDE for ML & DS experts and supports several languages besides Python. Features of the IDE include support for editing across platforms, a built-in package manager, intelligent auto-completion, a file system browser, and numerous tabs. In addition, the Atom interface and experience are highly customizable thanks to the regular updates made to its plugins, languages, libraries, and tools.
Spyder
Spyder is an open-source IDE founded and developed in 2009 by Pierre Raybaut. It is compatible with many Python packages, including NumPy, SymPy, SciPy, pandas, IPython, and others. Code introspection, code completion, syntax highlighting, horizontal and vertical splitting, and other features are also available in the Spyder editor.
Visual Studio Code
When it comes to programming in Python, Visual Studio Code is a popular option. Features like IntelliSense, which provides smart completions based on variable types, imported modules, and the declaration of functions, have made this IDE popular. Also, breakpoints, call stacks, and an interactive console makes it possible to debug code without leaving the editor in VS Code. Also, we can add new languages, themes, and debuggers to VS Code thanks to its adaptability and customization options. You may also use the integrated Git commands within the IDE. The Visual Studio Code IDE has both free and premium editions.
Sublime text
Sublime Text is an exclusive code editor we can use with the Python programming language. Sublime text is a text editor with many useful features, such as project-specific preferences, fast navigation, cross-platform plugin compatibility, etc. Sublime Text is a speedy text editor with a helpful community, but it costs money.
Rodeo
Yhat’s Rodeo is an open-source IDE designed for data science in Python. Therefore, Rodeo provides access to Python tutorials and reference cheat sheets. Rodeo’s features include syntax highlighting, auto-completion, simple manipulation of data frames and graphs, in-built support for IPython, etc.
JupyterLabs
The goal of the open-source online application JupyterLab was to create an environment similar to that of the Jupyter Notebook. Jupyter Notebook, which evolved from IPython in 2014, serves as the user’s document workspace. Data science, scientific computing, computational journalism, and machine learning users can use its adaptable interface to set up and rearrange workflows. Modularity permits additions that increase and improve functionality. This programme is excellent for use as a teaching or presentation tool because of its intuitive design and user-friendly data science interface.
Thonny
Thonny is an IDE for Python created at Tartu University. It was made for Python instructors and students just starting with the language. Thonny’s features include statement stepping without breakpoints, a straightforward pip UI, line numbers, access to live variables, and more.
About the author
Senior Research Writer at INDIAai
Share via
CICERO and the diplomacy of AI agents
Here is how to implement Yann LeCun’s autonomous AI
Join our newsletter to know about important developments in AI space

source

Note that any programming tips and code writing requires some knowledge of computer programming. Please, be careful if you do not know what you are doing…

Post expires at 2:54pm on Friday February 24th, 2023

Leave a Reply

Next Post

Top 10 Recession-Proof Deep Learning Skills for Engineers to Learn - Analytics Insight

Thu Nov 24 , 2022
source— Note that any programming tips and code writing requires some knowledge of computer programming. Please, be careful if you do not know what you are doing… Post expires at 2:54pm on Friday February 24th, 2023
%d bloggers like this: