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.
All CSUN students interested in participating must enroll for the DataJam 2019 course on Canvas. The course provides an overview of data science with tutorials on data visualization, mapping and geospatial data, statistical analysis, and using open data. Resources are also provided on resilience topics. Datasets that must be used in competition will be available through the Canvas site immediately following the launch on September 27th.
Students interested in competing in DataJam 2019 will form teams of at least two and no more than five CSUN students. Teams must register by Monday, October 7th using this form.
The launch of DataJam 2020 will take place on Friday, September 25th. Location TBD.
We are excited to welcome John Peach, Sr. Data Scientist at Amazon Alexa, as our Keynote Speaker! Don't miss this unique opportunity to hear from a leader in this exciting and transforming field!
Workshops will focus on data science topics student teams need to be successful in data analysis and related software programs. Please RSVP for each session you plan to attend.
|Start Time||USU Lakeview Terrace||USU Flintridge|
The schedule for teams presenting in DataJam 2020 is TBA.
The event will be held TBA. If you plan to attend as a guest, please RSVP.
|10:45||team check-ins begin|| |
|11:00|| || |
|11:15|| || |
|11:30|| || |
|11:45|| || |
|12:00|| || |
|12:15|| || |
|12:30|| || |
|1:00|| || |
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 in 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 2019 Committee!
- Andrew Ainsworth (Psychology, CARE)
- Meeta Banerjee (Psychology)
- Kyle Dewey (Computer Science)
- Helen Heinrich (Information Technology)
- Sherrie Hixon (Research and Sponsored Programs)
- Dongling Huang (Marketing)
- Charissa Jefferson (Oviatt Library)
- Li Liu (Computer Science)
- Erika Reyes (Research and Sponsored Programs)
- Chris Salvano (Oviatt Library)
- Tim Tiemann (CSUN Innovation Incubator)
- Adriano Zambom (Mathematics)
We are pleased to announce the winning team presentations for DataJam 2019!
Congratulations! Thank you to everyone who participated in this year's event!