This Monday, February the 12th, we launched a public beta of Datalore - an intelligent web application for data analysis and visualization in Python, brought to you by JetBrains. This tool turns the data science workflow into a delightful experience with the help of smart coding assistance, incremental computations, and built-in tools for machine learning.
Intelligent code editor
Data science is an art of drawing insights from the raw data, and to make good predictions, you need to write code. To make machine learning-specific coding an enjoyable and easy experience, Datalore provides smart code completion, inspections, quick-fixes, and easy navigation.
To make your coding routine easier, we introduce Intentions - context-aware suggestions that appear depending on what you’ve just written. Click on the appropriate Intention, and Datalore will generate new code for dataset upload, train/test split, graph design, and much more.
Fine-tuning machine learning models comes with multiple edits. Suppose you adjust a few model parameters and want to see how it affects its predictions - and you want these results right now. Datalore follows dependencies between various computations in the workbook and minimizes recalculations caused by new changes. This way, the output at the right side of the screen always reflects your latest ideas.
Machine learning tools
Data analysis starts with Python necessities: numpy, pandas, and sklearn built-in libraries. On top of that, we developed two advanced visualization libraries: datalore.plot, inspired by the "grammar of graphics" ideas and their R implementation ggplot, and a datalore.geo_maps which enables the addition of interactive maps to your analysis. There are built-in datasets (Iris, MNIST, Titanic, and more) for beginners to explore or a handy File Manager to upload original datasets as .csv-files.
We also enable real-time remote access to the workbook and code editor. To show your model to colleagues and get their insights, just share the workbook link. Team members can edit and add code on the go while discussing ideas.
Different computational instances
Datalore allows access to various computational resources depending on what task you are working with. Simple algorithms run on small computational agents, while deep learning algorithms require more powerful agents. Contact us via our forum if you want to work with larger instances.
Go to datalore.io and try it!
We are excited and anxious to get your feedback via the Datalore forum - it's the quickest way to share your opinion. Leave a post about issues that you have encountered and features that you would like to see, and get in contact with our team and other users. You can also find us on Twitter. We are still under development and look forward to your insights to make Datalore even more awesome.