Computer Science Data Science Facilities

This page describes primary software and systems for general use within the department. Individuals and research groups may, of course, have their own.

The major Hadoop-related tools are avaiable on all of our systems, though we don't have an actual Hadoop cluster. We believe you can get at least as good performance from running tools on the local system as on a small cluster, particularly if you use one of the large systems such as ilab1, ilab2 and ilab3.

Here are the tools discussed on this page.

(Zeppelin has had almost no use. It is currently not available, but can easily be restarted if anyone needs it.)

If you need additional tools, please contact We're willing to install more tools, but will want your help in verifying that they work.


Most data science within computer science is done in Python. We have Anaconda-based environments available on our Linux systems. They have the major packages used for data science already loaded. If you need additional packages, please contact


Jupyter is a "notebook." It's a web interface designed to make it easy to do quick analysis, primarily in python. (We have also installed kernels for Java, Scala, and R.) We don't recommend it for large programs, but many people use it for data analysis.

As of July 2024, our primary version of Python is 3.11, and Spark is 3.5.1, although we also have Python 3.9 and 3.10. We recommend using the newest one that will work with your software. Note that Spark 2 works with Python 3.7 but not 3.8, while Spark 3 only works with 3.8 and later.

While our copy of Jupyter has support for Scala, we strongly recommend using Zeppelin for Scala, particularly for classes. The Jupyter version of Scala uses more CPU than it needs to. In a class this can cause performance problems. The Java kernel for Jupyter looks better, though it hasn't had much testing. We have both a Java 17 kernel and Spark with Java 11. (There were issues getting Java 17 to work with Spark. We have a workaround, so Jupyter will shortly be fixed. There's a test version currently in

For more information on our copy of Jupyter, see Jupyter.


NOTE: We have supported Zeppelin in the past. However since there's been virtually no use, it is currently not running. If anyone needs it, it would be easy to bring it back.

Zeppelin is a different notebook system, from the Apache project. Its capabilities are similar to Jupyter's, but there is arguably better integration between the parts.

Unlike Jupyter, which is available both from the command line and the jupyterhub web site, Zeppelin is only available in a web version. Our is at

See Zeppelin Documentation for more information.


Spark is available from all of our systems. Spark will run standalone on the node where you run it. On systems such as there are enough cores to get reasonable parallelism.

If you want to write significant Spark programs, you'll probably be using the command line (and maybe an IDE). See Spark Programming for specifics in doing Spark programs here. The rest of this section describes use of Spark from Jupyter and the interactive commands.

The pyspark command-line program, and the Spark in Python3 session in Jupyter, set up a Spark context for you in python3. The following variables are defined:

The spark-shell command-line program, and the Spark in Scala session in Jupyter, set up a Spark context in Scala, with the following variables:

The Spark in Java kernel in Jupyter defines

See the Jupyter documentation for more details.

Graphics is avaiable within Jupyter / ipython using matplotlib. E.g "%matplotlib inline".

Python support for Jupyter is well documented. See The Jupyter Notebook.

The rest of this section has information on the Scala kernel for Jupyter, and spark-shell, since it's less well documented elsewhere. Here's the official documentation: Apache Torree Quick Start.

Graphics in Scala

We are currently unable to support graphics in Scala from Jupyter. We suggest that you prepare data in Scala, write it out, and then use Python to graph it. While there are graphics packages for Scala, they either haven't been updated for the current version of Scala or won't install. (If you find one that works, please tell us. We'd be happy to install it.)

Add classes to Scala

The Scala enviornment has access to a large set of Spark-related libraries, as well as other standard libraries such as Apache Commons. Try "ls /common/system/spark/jars/" to see them all. If you need more, in Jupyter, you can use "%classpath" to load them. See the FAQ for more information. They tell use to use

%AddDeps group-id artifact-id version
to load libraries. This command searches the Maven collection of libraries. Just about any library you could want is in Maven. Here's a search tool: Maven search That search will display the group ID, artifact ID, and latest version. For generic libraries you probably should use the most recent. With Spark-related libraries you may want to use version 3.5.1, so it matches the version of Spark we have installed.

In Spark-shell, the --jars and --packages options perform the same function. For --packages, arguments look like groupid:artifactid:version

Oddities in Scala


The Hadoop software is installed in standalone mode on all of our systems. The only real use would be for Map/Reduce jobs. Note that jobs will run in local mode. However on our larger systems, e.g. ilab1, ilab2 and ilab3, you can get a reasonable amount of parallelism if you adjust the number of tasks. (By default only 2 map tasks are run.)

See Map/Reduce on CS Systems for details.