Tag: Soumava Dey

Principal Component Analysis

Mary Pat Campbell
Data Science with Sam

Link:https://www.youtube.com/watch?v=Z6feSjobcBU&ab_channel=DataScienceWithSam

Article: https://sections.soa.org/publication/?m=59905&i=662070&view=articleBrowser&article_id=3687343

Graphic:

Excerpt:

In simple words, PCA is a method of extracting important variables (in the form of components) from a large set of variables available in a data set. PCA is a type of unsupervised linear transformation where we take a dataset with too many variables and untangle the original variables into a smaller set of variables, which we called “principal components.” It is especially useful when dealing with three or higher dimensional data. It enables the analysts to explain the variability of that dataset using fewer variables.

Author(s): Soumava Dey

Publication Date: 14 Nov 2021

Publication Site: Youtube and SOA

Coffee Chat – “Data & Science”

Mary Pat Campbell

Link:https://www.youtube.com/watch?v=S5GHsjgSl1o&ab_channel=DataScienceWithSam

Video:

Excerpt:

The inaugural coffee chat of my YouTube channel features two research scholars from scientific community who shared their perspectives on how data plays a crucial role in research area.

By watching 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.

Author(s):

Efrosini Tsouko, PhD from Baylor College of Medicine; Mausam Kalita, PhD from Stanford University; Soumava Dey

Publication Date: 26 Sept 2021

Publication Site: Data Science with Sam at YouTube