NSF Data Science Program with Career Support and Connections to Industry

Carol Shubin

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Conference: 2021 ASEE Virtual Annual Conference Content Access
Location: Virtual Conference
Publication Date: July 26, 2021
Start Date: July 26, 2021
End Date: July 19, 2022
Conference Session: NSF Grantees Poster Session
Tagged Topics: Diversity and NSF Grantees Poster Session
Page Count: 7
DOI: 10.18260/1-2--37529
Permanent URL: https://peer.asee.org/37529
Download Count: 299

Paper Authors

biography

Carol Shubin California State University Northridge

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Carol Shubin is a professor of mathematics at CSUN and the PI of NSF Data Science Program with Career Support and Connections to Industry. She is interested in partnering with other universities that want to start a data science program. She has been the PI or co-PI in several other STEM educational projects funded by the NSF or NASA and served as a Fulbright Scholar in Rwanda.

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Abstract

Data Science Program with Career Support and Connections to Industry, supported by NSF IUSE, is an interdisciplinary workforce training program that encompasses a summer Bootcamp, year-long research projects, biweekly seminars, and career support. This program recruits 25 nontraditional and underrepresented students who have some coding experience, particularly in python, and some knowledge of introductory statistics. This paper gives some lessons learned on how to design, implement, manage, and assess a data science program that is hopefully useful to others who want to develop a similar program.

We offer a 6-week intensive summer Bootcamp that was held physically in summer 2019 and virtually in summer 2020. The topics covered include coding in python, data cleaning, exploration, and transformation, feature engineering, filtering, wrapper and embedded methods, machine learning, and SQL. Students are exposed to Github, BASH, natural language processing, and GIS. The Bootcamp culminates with a choice of week-long projects designed with various levels of difficulty.

During the Bootcamp, students are introduced to advisors and their year-long projects. Projects are either supervised by faculty or industry. Topics range from computer vision of biological or astronomical datasets, modeling and mapping the spread of COVID-19, NASA Mars Rover and Ecological forecasting, analyzing Soraya ticket subscriptions, working at Nflux (an AI start-up), applying machine learning to either cancer or quantum modeling or sentiment analysis projects, analyzing phone usage, capturing bot voice, and analyzing LA City Zoo data.

The data science program participants attend a biweekly seminar series during the academic year. The seminar focuses on career preparation workshops and hosts speakers who can give information about their industry, their interview process, and criteria for employee selection. Students create a LinkedIn page, create resumes that are critiqued, and engage in mock interviews. Students are sent information about current job opportunities and internships regularly. We also discuss the process of establishing connections with local employers and working with the student career center.

The paper highlights some of the adjustments that were made during the COVID-19 pandemic including a fully virtual version of the program. Assessment and dissemination are also discussed.

Citation
Format

Shubin, C. (2021, July), NSF Data Science Program with Career Support and Connections to Industry Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37529

TY - CPAPER
AB - Data Science Program with Career Support and Connections to Industry, supported by NSF IUSE, is an interdisciplinary workforce training program that encompasses a summer Bootcamp, year-long research projects, biweekly seminars, and career support. This program recruits 25 nontraditional and underrepresented students who have some coding experience, particularly in python, and some knowledge of introductory statistics. This paper gives some lessons learned on how to design, implement, manage, and assess a data science program that is hopefully useful to others who want to develop a similar program.

We offer a 6-week intensive summer Bootcamp that was held physically in summer 2019 and virtually in summer 2020. The topics covered include coding in python, data cleaning, exploration, and transformation, feature engineering, filtering, wrapper and embedded methods, machine learning, and SQL. Students are exposed to Github, BASH, natural language processing, and GIS. The Bootcamp culminates with a choice of week-long projects designed with various levels of difficulty.

During the Bootcamp, students are introduced to advisors and their year-long projects. Projects are either supervised by faculty or industry. Topics range from computer vision of biological or astronomical datasets, modeling and mapping the spread of COVID-19, NASA Mars Rover and Ecological forecasting, analyzing Soraya ticket subscriptions, working at Nflux (an AI start-up), applying machine learning to either cancer or quantum modeling or sentiment analysis projects, analyzing phone usage, capturing bot voice, and analyzing LA City Zoo data.

The data science program participants attend a biweekly seminar series during the academic year. The seminar focuses on career preparation workshops and hosts speakers who can give information about their industry, their interview process, and criteria for employee selection. Students create a LinkedIn page, create resumes that are critiqued, and engage in mock interviews. Students are sent information about current job opportunities and internships regularly. We also discuss the process of establishing connections with local employers and working with the student career center.

The paper highlights some of the adjustments that were made during the COVID-19 pandemic including a fully virtual version of the program. Assessment and dissemination are also discussed.

AU - Carol Shubin
CY - Virtual Conference
DA - 2021/07/26
PB - ASEE Conferences
TI - NSF Data Science Program with Career Support and Connections to Industry
UR - https://peer.asee.org/37529
DO - 10.18260/1-2--37529
ER -