Students are encouraged to enroll in the Canvas course, DataJam 2019, to access resources and stay informed about the competition.
Students can self-enroll in the course by following this URL: https://canvas.csun.edu/enroll/NGX4GG. Alternatively, students can sign up at https://canvas.csun.edu/register and use the following join code: NGX4GG.
New to data science? Review Professor Wayne Smith's presentation, "Introduction to Data Science."
Team presentations will be evaluated using the following criteria:
Data Visualization: Teams generate presentations that balance content (subject matter) and aesthetics (use of color, typography, etc.), and cohesion (unity) and coupling (sequence) of presentation material. Teams will: 1) choose the right visual for the purpose/goal of the presentation, 2) link the visuals to exploratory analysis and descriptive statistics, and 3) demonstrate how the visuals suggest further data collection, confirmatory analysis, or predictive modeling techniques.
Data Science: Teams have appropriately stated their objectives and/or hypothesis and used appropriate datasets to explore their hypothesis. Teams have used the appropriate type of analyses to answer their research question and support their interpretation of the results. Teams demonstrate an understanding of sampling, observed variables, distribution-based confirmatory tests, and mathematical calculations.
Reproducible Research: Teams present their investigation consistent with core research integrity that allows for other individuals to: 1) understand the methodological framework and analytic environment of the researcher; 2) access the original data in an open format; 3) replicate each step in the analytical process; 4) verify results using software applications that are freely licensed and readily available; and, 5) access scripts and results in a format that are malleable and publicly accessible.
Insights: Teams generate insights into the use of data and specify: 1) acknowledgement of the limitations of provided (endogenous) data; 2) identification of alternate sources (exogenous) of data that adds value to their hypothesis; 3) appropriate acquisition of other data; and, 4) integration of new data to create information that is more descriptive, diagnostic, predictive, or prescriptive than use of the original data alone.
Resilience: Teams demonstrate an understanding of the breadth of resilience challenges faced by the LA region. Data is used to appropriately identify a unique resilience-related challenge for the LA region, justify the hypothesis, and support a compelling argument for the team's proposed solution and how it can apply to institutions and/or government agencies.
Judges Choice: Judges will award in this category at their complete discretion. Presentation stands out and exhibits exemplary work, potentially in a category outside of the defined criteria. Judges have complete freedom to award this category to any team for any reason. Potential criteria may include approaches to research, resilience, and/or data science that are considered "novel," "trans-disciplinary," "ethical," "innovative," "strategic," or other.
Many thanks to our DataJam 2018 Committee!
- Andrew Ainsworth (Psychology, CARE)
- Elizabeth Altman (Oviatt Library)
- Meeta Banerjee (Psychology)
- Kyle Dewey (Computer Science)
- Helen Heinrich (Information Technology)
- Sherrie Hixon (Research & Graduate Studies)
- Charissa Jefferson (Oviatt Library)
- Crist Khachikian (Research & Graduate Studies)
- Li Liu (Computer Science)
- Erika Reyes (Research & Graduate Studies)
- Chris Salvano (Oviatt Library)
- Tim Tiemann (CSUN Innovation Incubator)
- Dongling Huang (Marketing)
- Adriano Zambom (Mathematics)
We are very pleased to announce the winning team presentations for DataJam 2018!
Thank you to everyone who participated in this year's event!