We have many different DataHub deployments tailored to meet the specific needs of different departments, courses and capstone projects. This ensures that students and faculty have the tools and resources they need to succeed.
These Hubs offer various user interfaces such as the classic Jupyter Notebook, JupyterLab, RStudio, Linux Desktop, and Visual Studio Code, catering to the diverse needs of users. They also support multiple programming languages including Python, R, and Julia. Additionally, other services are available like PostgreSQL databases and autograding on a case by case basis. Some examples are documented here.
Here is a summary of the different JupyterHubs available:
Language-Based or Generic Hubs
DataHub
Description: The primary JupyterHub for UC Berkeley, widely used across many foundational courses.
Tools Supported: VSCode, Jupyter Notebook and JupyterLab.
Usage: Supports a wide range of courses across disciplines like Economics, Environmental Science, Music.
RStudio/Shiny
Description: Tailored to support R-based courses and projects, and features RStudio.
Tools Supported: RStudio and Shiny, and contains many R packages.
Usage: Frequently used in advanced data analysis courses, research projects, and capstone projects in social science courses.
Julia
Description: Customized for Julia-based courses.
Tools Supported: Julia packages and Linux desktop applications.
Usage: Primarily used in math-based courses such as Math 128A, Math 128B.
Departmental Hubs
EECS
Description: Collaboratively designed to support EECS based courses.
Usage: Used by courses such as EECS 16A, EECS 16B.
Tools Supported: Python, VSCode and graphical applications through a Linux desktop environment.
Biology
Description: Customized to support biology based courses which involve large datasets and complex computation.
Usage: Used by courses in Integrative Biology, Molecular Biology, and other Biology related departments.
Tools Supported: Includes shared read and read-write directories to store large datasets and provides higher compute for data-intensive operations.
CEE
Description: Tailored to support Civil Engineering courses
Usage: Specifically designed for Civil Engineering course Civ Eng 70 to allow students to do Geospatial analysis using QGIS.
Tools Supported: Allows students to work with spatial data.
Astro
Description: Primarily supports Astronomy department courses
Usage: Used by instructors in the Astronomy department to conduct labs and homeworks.
Tools Supported: Supports python libraries specific to astronomy workloads; Shared directories and 4 GB RAM to support computation with large datasets
ISchool
Description: Used by the School of Information to support their Data Science courses.
Usage: Used by Data Science courses such as DataSci 241, DataSci 271, DataSci 200 etc.
Tools Supported: Supports Python/R packages, VSCode, RStudio, Quarto (for report generation) and shared read/write directories to store large datasets.
Public Health
Description: Used by the Public Health department to support their courses.
Usage: Courses such as PH 142, PH 251, PH 252 use this hub
Tools Supported: Primarily a R based deployment supporting RStudio and Jupyter R kernel
Course Specific Hubs
Prob 140 Hub
Description: Supports computational workflow for Data 140 (Probability for Data Science) course offered by Data Science Undergraduate Studies (DSUS).
Tools Supported: Vanilla deployment supporting Python packages specific to Prob 140 course.
Data 8 Hub
Description: Supports computational workflow for Data 8 (Foundations of Data Science) course offered by Data Science Undergraduate Studies (DSUS).
Tools Supported: Vanilla deployment supporting Python packages specific to Data 8 course.
Data 100 Hub
Description: Supports computational workflow for Data 100 (Principles and Techniques of Data Science) course offered by Data Science Undergraduate Studies (DSUS).
Tools Supported: Supports Python packages specific to Data 100 course.
Data 101
Description: Supports computational workflow for Data 101 (Data Engineering) course offered by Data Science Undergraduate Studies (DSUS).
Tools Supported: Data 101 hub has PostgresDB and MongoDB containers and supports Python packages. MongoDB and PostgreSQL containers can be configured to meet the specific needs of courses or projects. This includes setting up custom databases, user roles, and access permissions.
Data 102 Hub
Description: Supports computational workflow for Data 102 (Data, Inference and Decisions) course offered by Data Science Undergraduate Studies (DSUS).
Tools Supported: Supports Python packages specific to Data 102 course which includes packages specific to machine learning
Stat 20 Hub
Description: Supports computational workflow for Stat 20, an introductory statistics course.
Usage: Extensively used in Stat 20.
Tools Supported: Primarily a R based deployment supporting RStudio and Jupyter R kernel. It also supports Quarto for dynamic report generation.
Workshop Hubs
Workshop Hub
Description: Supports external workshops conducted by Data Science Undergraduate Studies (DSUS) and Demography department
Usage: Allows folks who are external to UC Berkeley to easily access Jupyter notebooks through username and password based authentication
Tools Supported: Supports JupyterLab and Notebook
High School Hub
Description: Designed to support synchronous workshops conducted by UC Berkeley instructors. These workshops teach basic data science skills for high school students during the summer. Allows for users to login with Google credentials from authorized domains.
Tools Supported: Supports JupyterLab, RStudio, Shiny and VSCode applications.
D Lab Hub
Description: Supports synchronous workshops in Python and R conducted by D-Lab during the fall and spring semesters
Tools Supported: Supports JupyterLab, R Studio, Shiny and VSCode applications.
Mooc-based and External tools-based Hubs
EdX
Description: Supports courses offered via EdX by UC Berkeley instructors.
Usage: Learners across the world who are enrolled to EdX courses offered by Berkeley’s data science instructors will use this hub to complete their assignments
Tools Supported: Allows global access through LTI authentication.
Gradebook
Description: Features a gradebook application.
Tools Supported: Supports JupyterLab and RStudio for arbitrary grading activities.