Seidenberg’s Computational Intelligence Lab Hosts a Data Pipeline Workshop

On April 25, Seidenberg’s Computational Intelligence Lab (CI-Lab) hosted a Data Pipeline Workshop in the lab’s new space in Pace’s 15 Beekman building. The main goal of the workshop was to help participants build fully custom data pipelines and help them advance to that level after mastering simple examples. The workshop was designed for students who have a good foundational knowledge of Python and were familiar with Pandas. Students were required to have a Python interpreter and a code editor to perform the operations on the datasets themselves. With this approach, when the students tried to perform practical operations and experienced issues, they got immediate help and guidance from the CI-Lab Team.

An Introductory workshop was held the day before on April 24, and this session was specifically for the participants who needed a refresher or foundational knowledge to engage with the main workshop's content more effectively.

Participants of the main workshop learned how to create, manipulate and manage data within DataFrames, including reading from CSV files and modifying column names, and learned about the importance of cleaning and preparing data before analysis.

Different techniques of cleaning and dropping, deducing and replacing data, and handling synthetic data were discussed, as were more complex operations like concatenating DataFrames, filling null values, and generating dummy variables. The students also covered Data Augmentation methods to enhance the dataset.

The CI-Lab team used practical exercises to deepen the participants’ understanding of the theoretical concepts that were introduced, and in doing so perfectly showcased what the lab is all about: being Pace University’s venue for demonstrating its leading-edge analytics and technology.

If you are interested in collaborating with the lab, their team provides a working environment for faculty, staff, and students. You can engage in collaborative research with other faculty and students, drop in for a workshop, or listen to an industry expert share their daily involvement in data science, machine learning, or artificial intelligence.