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Previous PIC Math Workshops on Data Science

About the Workshops

The PIC Math Workshops on Data Science were aimed at mathematics and statistics faculty members with little or no experience in data science. The workshops were held on the campus of Brigham Young University in Provo, Utah from May 29-June 2, 2017 and from June 18- 22, 2019. The workshops were supported by PIC Math which is a program of the MAA and the Society for Industrial and Applied Mathematics (SIAM).                

In these hands-on workshops, attendees were:

  • introduced to the field of data science/statistical learning/machine learning;
  • given an overview of techniques and software used to solve data science problems; and
  • taught how to guide undergraduate students working on real world data science problems

Additionally, in 2022, PIC Math supported a workshop on Interdisciplinary Data Science, held at Brigham Young University in Provo, UT from June 21-24, 2022. The in-person 4-day interdisciplinary workshop was for a team of 2-3 faculty members from the same institution. Members of the team attended jointly with one faculty member being in mathematics or statistics and the other(s) being in a different STEM discipline such as biology, economics, physics, chemistry, social sciences, engineering, etc. 


The purpose of this workshop was to initiate and grow a community for advancing interdisciplinary work in data science at colleges and universities. Attendees: 

  • gained an understanding of and strengthen their background in data science,
  • participated in a hands-on experience working on a data science problem as a group from various STEM disciplines and different institutions, and
  • implemented a joint interdisciplinary project at their institution with their colleague who is also participating in the workshop.

The workshop provided the faculty members with the tools, resources, experience, and network required to successfully integrate data science projects into the curricula at their institution. It fostered collaboration across different STEM fields and helped faculty better prepare undergraduate students for data-related industrial careers.

The 2022 workshop was co-sponsored by the Mathematical Association of America (MAA), Society of Industrial and Applied Mathematics (SIAM), and Transforming Post-Secondary Education in Mathematics (TPSE Math), and was funded by a grant from the National Science Foundation (NSF) DMS-1722275.

SIAM Logo   TPSE Logo   NSF Logo

 

Data Science

In this PIC Math Solving Real World Problems video, Dr. Jonathan Adler talks about his career path and how he used text analytics to help an online company distinguish between its business customers and private customers.

Data Science is the process of examining data sets in order to draw insights and conclusions about the information they contain. Many PIC Math undergraduate research teams have worked on data analytics and science problems. Here are some examples:

  1. Manhattan, New York: Students from Manhattan College received data from the animal shelter on abandoned pets during the period from 2012 to 2015. The students analyzed the data and identified trends about dogs and cats abandonment rates.

  2. Kansas City, Missouri: Box Office Analyst is a consulting firm for the movie theater industry. The company uses survey data to help theater owners decide which movies to play in their theaters. Students from Rockhurst University used the survey data to build a mathematical model that would predict opening weekend revenues for new movies.

  3. Youngstown, Ohio: The city has seen a dramatic decline in its city population and a shift in the location of the population over the past forty years. However, the police department was still using a division of the city into police beats that was created decades ago. Students from Youngstown State University received 2014 crime data from the police department, analyzed the data, and proposed two new models for more equitable divisions of the city into police beats. The police department adopted one of the proposed models.

BYU Masters students hired as interns as sports analysts for NBA teams BYU undergraduate students determine relative ratings for local pizza restaurants based on patrons' comments without a numerical rating WPI students explored a dataset of insurance claims to find patterns to inform preventive health care intervention strategies.

The workshops included an overview of data science, a taste of Python, a morning of overfitting and training, a plethora of free data resources, machine learning, and getting our hands dirty working in groups on a real-world problem.


  


Workshop Organizers 2017, 2019, & 2022

Michael Dorff
Department of Mathematics
Brigham Young University

Suzanne Weekes
Department of Mathematical Sciences
Worcester Polytechnic Institute

In 2022 only
Thomas Wakefield
Department Chair of Mathematics
Youngstown State University

Workshop Presenter 2017, 2019, & 2022

Dr. Randy Paffenroth
Department of Mathematical Sciences and Data Science Program
Worcester Polytechnic Institute

Additional Workshop Presenters in 2022

F. Patricia Medina
Assistant Professor of Computer Science
Yeshiva University

Maria (Mia) Barger
PhD Student of Data Science
Worcester Polytechnic Institute