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PIC Math Workshop on Data Science

About the Workshop

The PIC Math Workshop on Data Science is aimed at mathematics and statistics faculty members with little or no experience in data science. The workshop will be held on the campus of Brigham Young University in Provo, Utah from June 18- 22, 2019.  Applications for the workshop are due by March 15, 2019 and registration decisions will be made by early-April. The workshop is supported by PIC Math which is a program of the MAA.               

In this hands-on workshop, attendees will:

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

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 dramatics 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 workshop will include 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 your hands dirty working in groups of a real-world problem.

Applications are due on: March 15, 2019

Click Here to Apply Now!

Cost, Funding, and Travel

There is a $200 registration fee. However, the workshop will cover airfare up to $500, hotel costs during the workshop, and food for each selected participant. This program is organized by PIC Math, a program of the Mathematical Association of America (MAA) with funding from the National Science Foundation (grant DMS-1722275).

For those faculty accepted into the workshop, we will contact you to arrange your flight and hotel. Participants will arrive before the afternoon of June 18 and depart in the morning of June 22. Please do not book your own flight or hotel. If you do so, we will not be able to reimburse you for your travel costs.

Any questions can be sent to Michael Dorff at

Location: Brigham Young University, Provo, Utah
Dates: June 18 -22, 2019


Workshop Organizers

Michael Dorff
Department of Mathematics
Brigham Young University

Suzanne Weekes
Department of Mathematical Sciences
Worcester Polytechnic Institute

Workshop Presenter

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