With so many software types to choose from, it can be challenging to know which one to choose. If you’re looking for a data science tool, PyCharm is a good choice. Thanks to its student license, you can even access the software for free if you’re still studying.
PyCharm is good for data science. The software works well with multiple scripts and comes with the Python console. Student licenses are available, and PyCharm has many great features, including a debugger and excellent project maintenance tools.
If you’re looking for more information on PyCharm, and whether it’s a useful tool for data science or not, you’ve come to the right place. The following article contains advice on Professional PyCharm, what features you get with PyCharm, and much more.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is PyCharm?
PyCharm is an Integrated Development Environment (IDE). IDEs are coding tools that allow you to test and to debug your code, as well as writing it. However, some of the features of IDEs like PyCharm appear in iNotebooks, like Jupyter Notebook.
The most significant benefit of using an IDE instead of writing code in Word or Notepad is the capacity to run your code from within the same program. Nevertheless, some word processors now offer plug-ins that will test and debug code.
Experienced coders and data scientists who already work with Python or Anaconda will find PyCharm easy to use. PyCharm uses libraries such as NumPy and Matplotlib, so if you use those frequently, you’ll find that PyCharm has everything you need.
Is PyCharm Good?
PyCharm is one of the best IDEs available, and it’s made by JetBrains, the team behind IntelliJ IDEA.
PyCharm can handle multiple scripts, has excellent debugging capabilities, and works well for users who already work with Python, Anaconda, and more.
PyCharm is an excellent choice for data science, but you may have to pay for the Professional Edition to access the data science tools.
What Features Does It Have?
The Jetbrains software, PyCharm, is packed full of useful features to make your work easier. PyCharm features include:
- Supports Javascript, CSS, SQL, and more
- Code editor
- Error highlighting
- Debugger
- Git, SVN, Mercurial integration
- Customization of interfaces and keybinding
- PyCharm plug-ins library
Is PyCharm Suitable for Beginners?
There is a learning curve to using PyCharm, but this is true of most software for new users. On the one hand, PyCharm is great for those already experienced working with Python, which may alienate newcomers to the Python programs. However, everyone has to start somewhere, and with its functionality geared towards easily maintainable code, PyCharm could be an excellent place to start.
If you’re unsure whether PyCharm is right for you or not, download the Community Edition for free. It won’t have all the features, but it’ll give you a chance to test it out and determine how steep the learning curve would be for you.
A useful guide can go a long way to getting you up to speed with new software. We recommend Learn Python Quickly: A Complete Beginner’s Guide to Learning Python, Even If You’re New to Programming (available on Amazon). It comes in print, digital, or audiobook and covers everything you need to know to get started programming with Python.
There’s an excellent video on setting up PyCharm by Tech With Tim here:
PyCharm is mostly an excellent choice for data science, but like all good things, it has some downsides.
Pros of PyCharm
- Integration with Git, Mercurial, Python, Anaconda
- Supports several other scripts
- Excellent debugger
- Vast plug-ins library
- Community Edition available for free
- Student license available
- Project maintenance tools
Cons of PyCharm
- Scientific tools not included in Community edition
- May be challenging to learn to use
- Requires a large amount of memory
If you’ve never used Python for data science projects before, Python Data Science Handbook: Essential Tools for Working with Data on Amazon will guide you through the process. It’s available in print or digital and covers iPython, Jupyter, NumPy, Pandas, Matplotlib, and Scikit-Learn.
PyCharm has some excellent data science features, such as visualization of data plots, scientific libraries, and Conda integration. However, you will need the Professional Edition to access PyCharm’s data science tools. You will also get Jupyter Notebook with your software, allowing you to integrate this with PyCharm.
PyCharm Community Edition
The Community Edition is free, and you can get the Professional Edition free on a student license. That being said, you can also use the Community Edition for work just fine, in case you do not find value in investing in the Professional Edition.
What You Get With Community PyCharm
There is a significant difference in how much you get with Community PyCharm, compared to the Professional Edition. Some of the main features of Community PyCharm include:
- Debugger
- Code inspections
- Core Python support
- Python console
- Git, Github
- Mercurial
However, this is a small list, and the Community Edition features many functions and libraries. You can check out the full list here.
PyCharm Professional Edition
If you want full functionality or need a feature that’s not included in the Community Edition, the Professional Edition is well worth the cost.
Data scientists will find PyCharm worth paying for to get access to its scientific tools. None of these are available in the free version.
What You Get With Professional PyCharm
Professional PyCharm includes everything available in the Community Edition as well as the following:
- Code coverage
- Cython
- Duplicate code detection
- Python profiler
- UML diagrams
- Platform tools like profiler integration
- Perforce
- Many web development tools, such as JavaScript Debugger
- Python web frameworks
- Remote development and containers
- SQL
- Scientific tools like SciView
You can view the full list of Professional PyCharm features here.
How Do You Get a Student License for Professional PyCharm?
You can apply for a student, or educational license (teachers may also apply), here. Student licenses apply to middle and high school students, as well as undergraduate and graduate students. You may also be eligible for a free license if you are a vocational student. You must be enrolled in a full-time course that lasts for a year or longer.
Licenses last one year and must be renewed via a link in your account.
Do I Need to Install Python to Run PyCharm?
PyCharm works through Python, so you will have to install the parent software first. The PyCharm program acts as a complement to Python, but it can really enhance your data science projects. The Python console is included with PyCharm Community and PyCharm Professional.
- Does PyCharm work on non-Python projects? PyCharm has a lot of plug-ins for non-Python projects, such as SQL and CSS. If you usually work with several computer languages, PyCharm works well as a single hub for you to work in.
- Can you adapt the code? The Community Edition of PyCharm is open source, and you can modify it to suit your needs. You can view the source code through Github. Open source code is publicly accessible code, which you can view, modify, and distribute however you want.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
In this article, I examined PyCharm and listed its features. I looked at whether PyCharm is good for data science or not, what you get with Community vs. Professional PyCharm, and how to get a student license. I also listed some of the pros and cons of PyCharm and explained that you could adapt the code for the Community Edition.
To sum up, the free version of PyCharm is useful, but most of the data science tools you’ll need are restricted to the Professional Edition. However, if you are a student or teacher, you can access PyCharm Professional for free.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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