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Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges, Viviana Acquaviva
Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges, Viviana Acquaviva
Publications and Research
This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units.
Using Data Science To Create An Impact On A City Life And To Encourage Students From Underserved Communities To Get Into Stem, Elena Filatova, Deborah Hecht
Using Data Science To Create An Impact On A City Life And To Encourage Students From Underserved Communities To Get Into Stem, Elena Filatova, Deborah Hecht
Publications and Research
In this paper, we introduce a novel methodology for teaching Data Science. Our methodology relies on the outlook of the student body in our college. Our college is an urban, commuter, HSI (Hispanic Serving Institution) school with 34% Hispanic and 29% Black students. 61% of our students come from households with an income of less than $30,000+. Thus, many students in our college come from the communities that are underrepresented in the STEM fields and in the decision-making positions in the government (on the city level, state level, country level). However, in our methodology, we want to flip the situation …
Content Analysis Of Two-Year And Four-Year Data Science Programs In The United States, Elizabeth Milonas, Duo Li, Qiping Zhang
Content Analysis Of Two-Year And Four-Year Data Science Programs In The United States, Elizabeth Milonas, Duo Li, Qiping Zhang
Publications and Research
Data has grown exponentially in the last decade, and this growth has resulted in vast challenges for both business and IT domains (Hassan & Liu, 2019). This growth has given rise to the Data Science field, which has also grown exponentially in the last few years (Hassan & Liu, 2019; Song & Zhu, 2016). The Data Science field has its origins in the statistics and mathematics domain (Cao, 2017b), but is now considered a multidisciplinary field (Aasheim et al., 2015). Data Science warrants knowledge of data analytics, programming, systems, applications, informatics, computing, communication, management, and sociology (Aasheim et al., 2015; …