2024 - Sustainable Industrial Processing Summit
SIPS2024 Volume 1. Abe Intl. Symp. / Oxidative Stress and Technological Innovations in Medicine
| Editors: | F. Kongoli, H. Inufusa, T. Yoshikawa, C.A. Amatore, H-Y. Chen, W-H. Huang |
| Publisher: | Flogen Star OUTREACH |
| Publication Year: | 2024 |
| Pages: | ## pages |
| ISBN: | 978-1-998384-04-4 (CD) |
| ISSN: | 2291-1227 (Metals and Materials Processing in a Clean Environment Series) |
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MATHEMATICAL AND STATISTICAL ANALYSIS OF DATA FOR SOUND SCIENTIFIC DISCOVERIES: THE CASE OF GLOBAL WARMING
Samuel Berger1;1DATA SCIENTIST AND CONSULTANT, Costa Mesa, United States;
Type of Paper: Regular
Id Paper: 510
Topic: 46
Abstract:
Think of data like a hidden treasure. Unlike digging up dirt to find a treasure, you have to learn how to dig up your treasure hidden away in the data. It really is not that hard when you break it down. Most scientific methods apply to data science that apply to physical science.
In this lecture I will attempt to demonstrate the similarities between these two fields of science by examining a well-known piece of data graphed to demonstrate a major universal world-wide problem for which all of you are concerned. Using this data as an example, I will attempt to show that the same methods and logic for a physical science experiment apply when examining data, and that when those methods are applied the results can be most illuminating and relevant.
Data Science focuses on mathematics and statistical relationships to help reveal the secrets in the data after developing a controlled environment by the filtering out of “bad data” (i.e., data collected that is not related due to lack of control or other intrinsic reasons) and make accurate analysis as raw data can prove deceptive not by design, but by the methods of collection, processing and interpretation.