The PACM and DSA Newsletter allows you to learn more about applied math and data science by including interesting journal articles, interactive dashboards, upcoming PACM and DSA events (such as career panels, seminars, and conferences), significant news articles, and featured profiles of faculty, alumni, and industry professionals. By providing you with timely and relevant information we help you to stay up-to-date with the fields of applied math and data science.
All models are subject to limitations, including models used to predict the spread, death rate, and heard immunity surrounding COVID-19.
In this article, published by SIAM News, they discuss 5 myths of mathematical models and explain how to calculate hospital capacity, how many people need to be immunized for heard immunity, and how to calculate the average number of secondary infections. Additionally, they discuss limitations to current models including the lack of consideration for healthcare system capacity.
Think about the models for flattening the curve or heard immunity, what factors were taking into consideration and what were left how. How will this impact how reliable this model is?
DSA Chair, Joaquin Carbonara has selected an article and a short video to share that he believes would be beneficial to read.
A science news release: "New Machine Learning Theory Raises Questions About the Very Nature of Science", explains how "A novel [Data Science] computer algorithm, that predicts the orbits of planets, could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars." The lead scientist says, “I would argue that the ultimate goal of any scientist is prediction.... You might not necessarily need a law. For example, if I can perfectly predict a planetary orbit, I don’t need to know Newton’s laws of gravitation and motion. You could argue that by doing so you would understand less than if you knew Newton’s laws. In a sense, that is correct. But from a practical point of view, making accurate predictions is not doing anything less.” The subtle issue here is that the scientist is replacing Newton's "story" by a Data Science "story".
A short presentation by Scott Francis from the web conference "Data Changemakers" titled: "Why People Aren't Using Your Data Insights and What to do About it". On the other end of the broad spectrum of Data Science, Scott Francis in his presentation mentions "the hero's journey", used in reference to Campbell's monomyth, as a venue and possible future schema of data storytelling and visualization for business intelligence and other areas where Data Science is becoming indispensable.
Science, I would argue, focuses on the world outside of our mind and claims to eliminate human bias caused by human "weaknesses". On the other hand, "The hero's journey" focuses on human's feeling and intuition and fully embraces it. The two points of view have been widely regarded as opposite and disjoint. Is Data Science revealing a new, deeper, and less siloed common place for human intelligence and discovery? Science "stories" are called theories and they purport to find the truth. On the other hand, Data Science, like mindfulness and meditation, only purports to experience the truth. Wouldn't it be ironic if they would turn out to be one and the same?
Buffalo State Data Talk
Have you listened to Buffalo State Data Talk? The podcast that introduces you to how data is used and explores careers that involve data.
Check out the short descriptions of our released episodes and listen to one today (each episode is around 25 minutes long)
Episode 1: Achintya Pillai talks about data science for manufacturing at the Tesla Gigafactory Episode 2: Jessica Weitzel talks about non-profit evaluation and starting a data analytics career with an english background Episode 3: Jeff Rathman the CEO of Silo City IT talks cybersecurity Episode 4: Bill Bauer discusses using data science skills in biomedical research at Hauptman Woodward Episode 5: Katherine Aiken from UB athletics, talks sports technology Episode 6: Chris Bole from BlueCross BlueShield describes what it is like working as a healthcare analyst Episode 7: John O'Hara, a Buff state alumni, discusses working in marketing analytics at Disney Episode 8: Peter Yacobucci shares his experiences analyzing supreme court decisions
Listen to all the episodes of here, or wherever you listen to podcasts.
The History of Data Exchange
How do we move data from one location to another? Well it started with CSV, then XML, and JSON.
Tim Sehn shares how database growth fueled the need for data exchange and how the adoption of the internet created the need to update the way we exchange data. Although the article ends with the promotion of the companies data exchange product Dolt, the 'History of Data Exchange' is an interesting and quick read, closing with a discussion on the need for a way to collaborative on data distribution.
A clear visualization can make data easier to comprehend and more efficient for the reader to digest. Check out this article from the New York Times on electric cars and see how their data vis helps the reader understand the point that author is trying to make.
Madhur Anand and Chris Bauch use game theory to model vaccine prioritization in an attempt to answer, 'To save the most lives, who should get the vaccine first?'
Dr. Bauch combines game theory, a mathematical way of modeling how people make strategic decisions within a group, and epidemiological modeling. Dr. Anand and Dr. Bauch's model takes into consideration human behavior and therefore can accurate predict disease spread and vaccine protection.