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5 Powerful IDEs to Use When Writing Code for Data Science 1

5 Powerful IDEs to Use When Writing Code for Data Science

Data science, by its very nature, is dynamic and iterative. Delivering value is a combination of several factors, and taking a code-oriented approach is generally the right strategy. This applies to both analyses and reporting models, making processes a lot more efficient and reliable. However, coding with a Python IDLE doesn’t work well for data science projects as it simply isn’t practical. This is when powerful Integrated Development Environments or IDEs help get the job done.

Python is a good starting point to develop code because it lends itself quite liberally to data science. It facilitates easy readability, enables statistical analysis, and has packages for machine learning and data visualisation. Besides this, it offers a range of IDEs to choose from, each with their own pros and cons. As such, it can be a challenge to identify one to work with, as the best solution is one that is flexible and allows for easy organization and collaboration.

To help the shortlisting process, take a look at these 5 powerful IDEs to use for data science.

Scientific Python Development Environment (Spyder)

Spyder is an open-source IDE that is suited for both first-timers as well as experienced data scientists. It was designed for data science, and is very beginner-friendly. For instance, to smoothen the learning curve, it has the online help feature, which allows you to search information online related to specific libraries. What’s more, it has all the key libraries for data science, which are:

  • Matplotlib
  • IPython
  • SciPy
  • NumPy

The other notable features of Spyder that make it a powerful IDE are:

  • Real-time code analysis: This feature highlights problem areas in the code.
  • Variable editor: Shows functions, modules, and variables in the current IPython console session and allows you to edit objects.
  • Profiler: Helps you improve your code and highlights areas that need to be optimized to improve performance.
  • Graphical debugger: Makes bug locating and testing a lot easier and intuitive.


This is a text editor developed by GitHub, with a customizable option for writing code in Python. Its main benefit for data scientists is that it can work with numerous plugins. As a result, it can be an exceptional editor for data science applications. For instance, it is possible to add the Data Atom package, which enables you to execute SQL queries.

Additionally, Atom works across a range of platforms, which includes MacOS, Linux, and Windows. It is free to use, and its integration with Git is among the key reasons why it is an incredible tool. The community offers constant improvements to plugins and enhancements that data scientists can add to the IDE and customise it for smoother operation.


Created by a company known as JetBrains, PyCharm is the ideal IDE for any data scientist working with scripts. It has a range of features that increase productivity, aid debugging, and allows code restructuring. PyCharm also works excellently in a team setting as it has a version control system that enables tracking. Everyone involved with the project can see the changes made and factor it into the pipeline.

Additionally, PyCharm offers a range of customizability. You can personalise the IDE to suit your preferences by choosing your own colour schemes, key binds, plugins, and themes. For a better idea of its capabilities, take a look at some of the top features of PyCharm.

  • The code editor in PyCharm is one of the best, as it has provisions such as:
    • Auto-code generation
    • Code analysis
    • Auto-indentation
    • Syntax and error highlighting
  • It simplifies navigation throughout the project with the search functionality. You can locate the usage of a symbol across the project and within scripts.
  • PyCharm allows you to create isolated environments for different dependencies in projects. You can choose environments such as Conda, Pipenv or Virtualenv when starting a new project.
  • Refactoring allows you to restructure code, which includes renaming, changing the method signature, and more.
  • PyCharm allows you to debug and observe results through a graphical interface. This makes it much easier to catch errors.

Visual Studio by Microsoft

Visual Studio or VS can prove useful when building data science applications because it supports IPython and Conda. The IDE isn’t normally recognised for Python programming but can be quite effective in the right hands. It works on MacOS and Windows, and has a marketplace for extensions. For an overview of its features, take a look at this list.

  • Code editor: Uses the IntelliSense syntax provision to offer auto-completion functionality, along with hints for classes and function, code colouring, and a quick-info tooltip.
  • Integration with Git repositories: You can access local Azure and GitHub repositories, clone them, manage branches, and make changes.
  • Unit testing: Capability is enabled through Pytest and Unittest frameworks.
  • Interactive REPL window: This feature is ideal for experimenting with Python code. It allows you to enter arbitrary code and test it for immediate results.
  • PyLint integration: Aids error checking in Python and drastically improves the coding standard and pattern.

Jupyter Notebook

There are many reasons to use Jupyter Notebook as a data scientist, but its main strength is that it is more than an IDE. It also features the functionality of an educational or presentational tool, and this can be invaluable. Some enthusiasts believe that this IDE is a key part of any Python data scientist’s toolkit. Jupyter Notebook’s incredible visualization capabilities and the ability to insert code with HTML, LaTeX, and markdown are key reasons for such a statement.

Its notable features are that:

  • It offers a very intuitive data science environment.
  • It utilizes visualization libraries such as Seaborn and Matplotlib.
  • It enables creation of blogs from notebooks
  • It facilitates easy sharing of code files

Picking the right environment to develop code in is a time-consuming process, but one that’s well worth it. Choose one that complements your strengths as a professional as this greatly enhances your ability to do your job, and perform it well. With adequate experience and mastery over such tools, you become a viable candidate for positions at leading companies. The Talent500 platform aids these efforts and can align your profile with perfectly suited openings at leading Fortune 500 companies in the world. To take the next step, sign up for Talent500 today!

Naveen Benny

Naveen Benny

Expert AI practitioner building Deep Learning systems. Inquisitive about Computer Vision and GANs. Loves yoga and is always-on meditative mode. Leads the data science team at Talent500.

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