Webinar on Pedagogy and Assessment – during COVID-19

Date of the event	22.07.2021
Title of the Event	Webinar on “Data Quality for Artificial Intelligence”
Organized by	Dept of Information Science and Engineering along with IQAC, MVJCE, Bangalore
Name of the Resource Speakers	Hima Patel, Research Manager, IBM Nitin Gupta, Advisory Research Scientist, IBM Naveen Panwar Advisory Research Engineer, IBM

Every day, organizations are experiencing some change in business due to partial or full automation of systems, processes, and tasks — threatening to replace human labor. Machine Learning solutions have even started demonstrating that they are better than human statisticians in data preparation, thus challenging the humans in highly qualified, intellectual tasks.

A Forrester Infographic indicates that Data Quality (DQ) is one of the topmost challenges to successful implementation of AI systems in enterprises. The main objective of this webinar was to explain the importance of data quality for modern data science workflows and data quality related problems that always surfaced in “historical data,” which may have been gathered from multiple sources with inconsistent standards and varying levels of accuracy.

With this mentioned objective in mind, the Department of Information Science and Engineering organized the webinar on “Data Quality for Artificial Intelligence” where 55 students from II & III year participated.

Resource speakers from the webinar were Hima Patel, Research Manager, IBM; Nitin Gupta, Advisory Research Scientist, IBM and Naveen Panwar Advisory Research Engineer, IBM.

The webinar started with a brief introduction on importance of quality data in Artificial Intelligence oriented applications by Hima Patel.

During the webinar following topics were explained by all the three resource speakers:

What is data quality for AI
Why is it necessary for modern data science workflows
Discussion on label noise
Class overlap

Discussion on class parity metrics

Followed by the theoretical session, a hands-on session also was conducted where students were given open-source datasets to analyze and interpret the usefulness of the data based on quality matrices.

Outcome of the Event:

Participants were able to understand data quality for AI, its importance in modern data science workflows and also got hands-on experience with open-source datasets.