NSDC 2023 Data Science Symposium | Northeast Big Data Innovation Hub

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2024 NSDC Data Science Symposium Coming Soon!

Read about the 2023 NSDC Data Science Symposium below.

The 2023 Data Science Symposium (DSS) is brought to you by the National Student Data Corps (NSDC), hosted by the Northeast Big Data Innovation Hub (NEBDHub).

The DSS will provide a virtual forum for undergraduate, graduate, and recently-graduated students (within the past 12 months) at higher education institutions around the world who are interested in data science to create and present their data science research projects, discover the best practices for academic research, connect with other data science students, and expand their data science knowledge and community.

The student participants, known as NSDC Explorers, will be able to choose a research topic that fits into a category that aligns with the Northeast Big Data Innovation Hub’s Focus Areas, or other areas of interest.

Congratulations to the NSDC 2023 Data Science Symposium Winners!

FIRST PLACE, GRADUATE COHORT

First Place for the Graduate Cohort is awarded to Sol Jung, a graduate student at the University of California, San Diego, for their project “Classification Model for Early Signs of Dementia.”

FIRST PLACE, UNDERGRADUATE COHORT

First Place for the Undergraduate Cohort is awarded to Lily Watson, Julia Vitale, and Pranav Parekh, undergraduate students at Vassar College, for their project “Maternal Hypertensive Disorders and Sudden Infant Death Syndrome in American Infants: A Case-control Study.”

SECOND PLACE, UNDERGRADUATE COHORT

Second Place for the Undergraduate Cohort is awarded to Mónica Estrella, an undergraduate student at the University of Connecticut, for their project “Perceived Moral Patiency of Social Robots: Differences Across Morphologies.”

THIRD PLACE, UNDERGRADUATE COHORT

Third Place for the Undergraduate Cohort is awarded to Jasmine De Leon, an undergraduate student at the Seton Hall University, for their project “TRACE-ing Intelligence Analysis.”

Call for Participation & Submission Forms

Students – Become an NSDC Explorer!

Mentors/Judges – Volunteer with the NSDC!

Student Submission Form

Key Dates

Students and Mentors & Judges Call for Participation Opens: March 31st, 2023

Upcoming Virtual Student Mentoring Sessions, scheduled for:

Tuesday, April 25th, 2023 at 10 am ET
Additional sessions to be announced

Student Submissions due: ~~July 31st, 2023, 11:59 pm ET~~

Extended deadline is August 14th, 2023, 11:59 pm ET

Student Winners announced: Late 2023

Student Winners will have the opportunity to present their research at a live webinar in Early 2024.

How to Participate

If you are a student who is looking to create and present a research project as an NSDC Explorer, we welcome you to participate in the NSDC 2023 Data Science Symposium! If you are a professional, researcher, faculty member or graduate student, we’d like to invite you to volunteer and participate as a mentor, judge, or both!

How to Participate as an NSDC Explorer

How to Participate as an NSDC Mentor

How to Participate as an NSDC Judge

Themes & Theme Challenges

The NSDC DSS provides Theme Challenges, or research ideas, that you can use to develop your own research project. The Theme Challenges will align with one or more of the following focus areas of the Northeast Big Data Innovation Hub, or will fall into a “miscellaneous” category if you choose another topical area for your submission. Data science can apply to many topical areas, and we want you to apply it where it aligns with your interest. We encourage you to research any approachable, appropriate topic that interests you.

Learn more about the Northeast Big Data Innovation Hub’s Focus Areas.

Data Education - Student with cap and gown

Join a Theme Challenge and use the following prompts, data sets, or tools to inspire your research:

Challenge 6 (Urban to Rural Communities) – General Bikeshare Feed Specification Research

Challenge 7 (Responsible Data Science/Urban to Rural Communities) – Motor Vehicle Crash Research

Using the R programming language, use the package {njtr1} to download geotagged motor vehicle crash data. These data are compiled by the New Jersey Department of Transportation each year and are gathered from local police departments to document the location of every motor vehicle crash within the state of New Jersey. Historical data is available since the year 2001 via the package and directly from NJDOT’s crash tables website if you’d prefer not to use R. See https://doi.org/10.1016/j.simpa.2021.100176 for details on the R package for working with these data.

Develop a set of spatial risk factors using readily available public data sources (POIs, bus stops, census socio-demographic data etc.) to train a machine learning model to predict which regions and/or roads in New Jersey might be more susceptible to motor vehicle crashes. Perform feature engineering and data aggregation on the motor vehicle crash data to develop a set of predictors for your model. It is also acceptable to integrate other freely available third-party datasets as predictors so long as you have appropriate permission to use the data.

The response variable in your model should be the total count, or the relative likelihood of crashes occurring within a grid cell in the state, following the sample model code and fishnet aggregation technique implemented by Steif (2021) in R: https://urbanspatial.github.io/PublicPolicyAnalytics/geospatial-risk-modeling-predictive-policing.html

Compare your model’s output to the kernel density of car accidents within the grid cells of the state to evaluate the accuracy of your model and new areas that are of high risk for future accidents. Justify your design choices and data transformations and include appropriate diagnostic visualizations and tables showing your model’s performance within the report.

Data: You are highly encouraged to incorporate many of the open datasets that are published by the State of New Jersey for this challenge. For example, the state government maintains data.nj.gov which publishes data for many state agencies. This website contains many road-related datasets that may be of interest in predicting the motor vehicle accidents. There is also the NJDEP open data portal that provides data on environmental hazards.

Challenge 8 (Responsible Data Science) – Cybersecurity Best Practices Research

Eligibility & Resources

All undergraduate and graduate students from public and private universities, colleges and community colleges are eligible to participate as NSDC Explorers in the NSDC Data Science Symposium. Recent graduates of undergraduate/community college programs and graduate programs (within the past 12 months) are also eligible to participate as NSDC Explorers.

All graduate students, researchers, faculty members, and industry/non-profit professionals are eligible to participate as NSDC Mentors and/or Judges in the NSDC Data Science Symposium.

Here are some resources that may help you develop a project, mentor students, or judge a submission:

NEBDHub Data Science Resource Repository (DSRR): A curated set of 700+ resources for learners, educators, researchers, career explorers, and professionals that promotes data science literacy.
NSDC Learner Central: A collection of free, open online data science resources for learners of all skill levels.
NSDC Video Library: A collection of videos on data science topics based on IBM’s OpenDS4All curriculum, as well as student-created SQL and R educational materials, data science use cases, and more.
COVID Information Commons (CIC) Research Explorer: Machine learning-generated visualizations of COVID-19 research, including 9,000+ NSF & NIH Awards
CIC COVID Awards and Principal Investigator (PI) Database: A searchable database of over 9,000 COVID-related awards from the National Science Foundation (NSF) and National Institutes of Health (NIH), and profiles of the PI who is the principal researcher on each project.
150+ CIC Global research resources: Datasets, groups and guides, and news and publications
118+ Webinar Recordings of past CIC Researcher Lightning Talks, and English and Spanish Transcripts, including browsable playlists of talks by theme.
NSDC Career Panels: Event Recordings and upcoming events
NSDC Slack Community: Find an NSDC DSS Channel here for all your immediate questions!
Office hours with mentors: Submit the Student Call for Participation form to receive updated information about dates and registration
Example Poster board
You are also free to use any external academic, government, and industry resources. Please cite all sources used.

Evaluation Criteria

Submissions will be evaluated by a committee of researchers and other experts on the basis of their intellectual merit and broader impact, two criteria used by the National Science Foundation (NSF) to evaluate project proposals. NSF describes intellectual merit as the potential of the proposed activity to advance knowledge, while broader impact is described as the potential of the proposed activity to benefit society and contribute to the achievement of specific, desired societal outcomes.

Project submissions should clearly communicate and demonstrate a thorough understanding of the objectives, methods, and implications of data science research and make insights and recommendations that contribute to greater societal goals.

Submissions will also be evaluated to ensure that they follow the submission requirements (a virtual, one-page poster and an abstract of 7 to 10 sentences). If your submission does not follow these submission requirements, you risk disqualification from the 2023 NSDC Data Science Symposium.

The top three projects in the Undergraduate/Community College Student Cohort and in the Graduate Student Cohort, chosen by the evaluation committee, may be invited to present their research and findings on a live Zoom webinar.

All participants will receive certificates of participation and may have their work published on the NSDC website.

FAQs

Is approval from a faculty advisor required for this challenge?

Can students use Artificial Intelligence Chatbots, such as ChatGPT, to assist in completing my project or writing my abstract?

Congratulations to the NSDC 2022 Data Science Symposium Winners!

The inaugural NSDC Data Science Symposium was launched in February 2022 to bring together students from around the world to broaden their data science community, expand their data science knowledge, and provide professional development opportunities for each student participant, or NSDC Explorer.

The student winners, representing Undergraduate teams from Case Western Reserve University and Smith College, were offered the opportunity to present their research at the Northeast Big Data Innovation Hub Student Research Symposium, a global, virtual event designed to highlight student research being completed across the NEBDHub’s various programs and challenges. Learn more about the NSDC 2022 Data Science Symposium Winners below.

FIRST PLACE WINNERS

First Place is awarded to Kiet Nguyen and Zhuldyz Ualikhankyzy, a team of undergraduate students at Case Western Reserve University, for their project “Prediction of Russian ruble (RUB) against US dollar (USD) in June 2022 amid Russian-Ukrainian war.“

SECOND PLACE WINNERS

Second Place is awarded to Michele Sezgin, Vivian Almaraz, Audrey Kim, and Salwa Ouachtouki, a team of undergraduates at Smith College, for their project “Visualizing Hampshire County Schools Health Data.“

Stay Connected with Us

Email us at nsdc@nebigdatahub.org with any inquiries or questions.

Some ways to stay connected with the NSDC community:

Join our Slack channel
Follow us on Twitter, Instagram, or LinkedIn
Subscribe to the Northeast Hub YouTube channel
Sign up for our NSDC mailing list
Check out the REAL Volunteer Program for more collaboration opportunities
Register to Start/Join an NSDC Chapter
Register for the next NSDC Career Panel and Q&A webinar