Date: 10th March 2022
Venue: Virtual Mode (Zoom)
Submitted by : Aditya Tuteja and Raibata Basu
Faculty Mentor : Dr. Rajashree Jain
Event Description:
- Number of participants : 56
- Speakers :
- Dr. Arvind Betrabet (Data Scientist at IBM Cloud)
- Mrs. Shubhi Asthana (Research Senior Software Engineer at IBM’S Almaden Research Center in San Jose, CA)
Event Details →
The webinar was anchored by Vaishnavi Arora. She welcomed Dr. Rajashree Jain who spoke about the Women’s Celebration Week and about SICSR IEEE Pune Student Branch. Then we began the program.
Dr. Arvind Betrabet talked about data science as a multidisciplinary approach to extracting insights from a large set of data. It used to be called statistics and statistical analysis before the name was the point, so there were three ways, and what 3V’s is velocity, variety, and volume. And it started with these three aspects but has quickly climbed to 40 plus. V’s.
These are languages and frameworks that can be used within data science. Some on the left are paid. You have to pay for a license to make use of it. We have Sass, MATLAB, and excel. He gave a short overview of what I.B.M. research is about and now what they do. He gave an overview of what data analytics can provide. So data analytics brings together the analytical capabilities in the project like machine learning forecasting new trends. We are using data, and we can use data mining algorithms, we can do text analytics, optimization can even be able to strategize. Dr. Arvind Betrabet talked about To analyze, reporting, and then deploying that Data in a cloud environment. All that can be provided using the extensive library support available. It is straightforward to use predictive analytics or data analytics.
Ms. Shubi Asthana talked about services computing where A.I. has played a significant role. Serverless computing has played a significant role. She talked about her colleagues at I.B.M. Almaden have been working on outlining the test in the process. Here, different parties can encrypt that data and provide it to an aggregator model. The aggregator model does not do not have the power to decrypt the data, but it can train the model. She talked about how to adopt their technologies and how this would be taking up more than 1/5 of the mobile data traffic by 2020. 25% of the use cases depend on edge computing capabilities. They want to utilize data science and machine learning to understand how data can be leveraged for communication.
A token of gratitude was given to both Shubhi Madam and Arvind SIr at the end of the webinar. A vote of thanks was given.
Photographs of the event →
(Introductory remarks)
(During the session)
(All the participants)