Technology

Data Science With Sam

This is an educational podcast focused on bringing academia and industry experts together in a common forum and initiate discussion geared towards data science, artificial intelligence, actuarial science and scientific research.DISCLAIMER: The views and opinions expressed in this podcast are solely those of the host(s) or guest(s) and do not necessarily reflect the policy or position of any organization. The podcast is intended to provide general educational information and entertainment purposes only. RSSVERIFY

Latest Episodes:

EP 2: Data & Actuarial Science Aug 09, 2022

Show notes

This coffee chat session features a seasoned Actuary who shared her viewpoints on the importance of data and data management in Actuarial risk assessment study. Guest speaker: Mary Pat Campbell, FSA, MAAA Follow her blog to learn more about Actuarial Science and statistics/mathematics: https://marypatcampbell.substack.com/

After watching this video, you will gather information on the following topics:

a) The importance of data in Actuarial risk assessment initiatives,

b) a brief overview of Actuarial Standards of Practice(ASOP) and it's significance in managing data quality and data integrity issues in Actuarial world,

c) Mary's opinion on how Data Scientists could provide additional support to Actuaries which might pave the way for future collaboration in Insurance industry.

Subscribe to my podcast to gain access to future coffee chat sessions featuring academia and industry experts. #datascience #coffeechat #science #research #data

EP1: Data & Science Sep 28, 2021

Show notes

This podcast features two research scholars from scientific community who shared their perspectives on how data plays a crucial role in research area.

Guest speakers:

Efrosini Tsouko, PhD from Baylor College of Medicine

Mausam Kalita, PhD from Stanford University

By listening to this video you will gather information on the following topics:

a) the importance of data in scientific research,

b) valuable insights about the data handling practices in research areas related to molecular biology, genetics, organic chemistry, radiology and biomedical imaging,

c) future of AI and machine learning in scientific research.