Data Scientist Tools & Software
Python, notebooks, editors, version control, containers and data tools for data science.
Who this toolkit is for
For data scientists who explore data, build models and share results.
Essential (4)
Core tools that are broadly useful.
Python
PythonOpen Source
Why you may need this: The most widely used language for data science.
View official installation instructions
Notebooks
JupyterLabOpen Source
Why you may need this: Explore data and share results in notebooks.
View official installation instructions
Code Editors
Why you may need this: Edit code and notebooks with Python extensions.
Flatpak (verified)flatpak install flathub com.visualstudio.code
Version Control
GitOpen Source
Why you may need this: Version code and experiments.
View official installation instructions
Recommended (2)
Useful for particular parts of the work or course.
Containers
Docker DesktopFreemium
Why you may need this: Reproduce environments for experiments and deployment.
View official installation instructions
Data & Database Tools
- DBeaver Community
Open Source
Why you may need this: Query data stored in databases.
Flatpak (verified)flatpak install flathub io.dbeaver.DBeaverCommunity
Specialist / Optional · Show specialist tools (2)
Consider these for particular modules, tasks or environments.
Code Editors
- RStudio Desktop
Open Source
Why you may need this: An IDE for R users.
View official installation instructions - R
Open Source
Why you may need this: Statistical computing with R.
View official installation instructions
Build your Data Scientist setup
Build My Setup lets you add or remove apps, combine roles, see why each app is included, and get a single install command where verified packages exist. Nothing is installed automatically.