The JetBrains Blog

Celebrate International Day of Education with a flashback to the top courses and projects of 2024 at JetBrains Academy!

Learn how to detect anomalies in machine learning using Python. Explore key techniques with code examples and visualizations in PyCharm for data science tasks.

Real-world data needs cleaning before it can give us useful insights. Learn how how you can perform data cleaning in data science on your dataset.

Learn how PyCharm streamlines data modeling with dbt, offering features like Git integration, smart autocompletion, AI-powered assistance, and more.

Want to dive into sentiment analysis? Learn how to analyze text and get insights into customer opinions, market trends, and more with Python libraries and tools!

Want to predict emotions in text more efficiently? Learn how LLMs can enhance sentiment analysis and discover how to use and fine-tune pre-trained models.

Uncover the 6 data science trends shaping 2024 and learn about the most popular machine learning trends and big data tools of the year.

Boost your machine learning and development productivity with PyCharm’s Hugging Face integration.

You might think there’s so much you need to learn before you can do data exploration with pandas, but I am going to show you two tools to help you go faster: Summary statistics and graphs.

Whether you’re starting a new project or expanding an existing one, as a data scientist, you’re always on the lookout for new material to explore. Knowing where to get data for data science projects can be challenging, and finding “good data” can be even more difficult. In this article, we’ll look at what makes “good data”, what format that data might be in, where to find it, and what the next steps are. 

Read this article to learn the difference between vanilla Jupyter notebooks and Jupyter Notebook support in PyCharm, a professional data science IDE.