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Data Science Expo

Judging Criteria

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Data Science Expo / Judging Criteria

Data Visualization/Storytelling Event Judging Criteria

Your Data Visualization and Storytelling project will be evaluated according to the following judging criteria below.

Teams are encouraged to design their posters or animated graph/presentation to allow evaluation of each category to promote an in-depth discussion with the judge during the pre-evaluation phase of judging. Projects are scored using 5 category indicators ranging from 1-5, 20 points is the maximum score given to this phase of judging.

Each team will present their project orally providing a comprehensive overview of their project. Projects are scored using 5 category indicators ranging from 1-3, 18 points is the maximum score given to each oral presentation.

Scores are combined to determine your overall score.

CategoryJudging CriteriaPoints Given
5-Excellent
4-3: Good
2-1: Satisfactory
Data and Visualization
All data in the dashboard are accurate and not manipulated
Data source is presented for each image or chart, datasets are cited (provide links)
Charts are clear with visuals descriptive statistics in clear and understandable tables and displays
Graphs used are understandable on their own
Data Visualization support the design, results of the study, and recommendations
Analysis and Insight
Problem Statement is Clear and Interesting; title of Project accurately reflects data
Give viewers meaningful insights by providing clear visual analysis of charts and graphs that represent the datasets
Objective approaches, data management, displays and results are transparent
Graphs and storyboards are structured and organized
Design and Aesthetics
Overall quality of the poster or animated graph
Design Aesthetic: limited clutter, good use of color contrast and patterns
Visual communicates a clear message for your audience
Data representation is bold and original
Total Points

Judging Criteria For ORAL Presentation:

Oral PresentationJudging CriteriaPoints Given
3-Excellent
2-Good
1-Satisfactory
Team CollaborationEach Team member presents or provide insights that reflects aspects of their project
CommunicationPresentation is well paced and communicated. Logical sequence is obvious.
PresentationProcess, insights and a solution to your project are presented orally. Explanation of your model is used to discuss your AI/ML project solution. Visual, physical artifact, website or app is presented to explain how users access the solution.
Reflection of ProcessProvide a balanced picture of the project by reflecting on positive aspects of the project, areas that needed improvement and future ideas to implement.
PassionPassion is demonstrated from team members
LengthPresentation is within 5-min range




Artificial Intelligence/Machine Learning Event Judging Criteria

Your Artificial Intelligence/Machine Learning Project will be evaluated according to the following judging criteria below.

Teams are encouraged to design their posters or digital presentation to allow evaluation of each category to promote an in-depth discussion with the judge during the pre-evaluation phase of judging. Projects are scored using 5 category indicators ranging from 1-5, 20 points is the maximum score given to this phase of judging.

Each team will present their project orally providing a comprehensive overview of their project. Projects are scored using 5 category indicators ranging from 1-3, 15 points is the maximum score given to each oral presentation.

Scores are combined to determine your overall score.


CategoryJudging CriteriaPoints Given
5: Excellent
4-3: Good
1: Satisfactory
Identify Problem and Understanding Users
Problem Statement is Clear and Interesting.
Target users are identified
Data
Identify data and code used to train the AI model.
Provide evidence on how the data is collected, sourced, and balanced including safety and privacy considerations.
Design
Show how AI is a good fit for the proposed solution.
Relevant documentation is provided to show how users will benefit from the solution/experience.
Prototype and Testing
Define user requirements needed to test your prototype.
Evidence of a prototype testing is provided to show, representation and trained tasks are met using a defined user requirement.
A prototype or model is created to successfully meet user requirements and models how users access the solution.


Oral PresentationJudging CriteriaPoints Given
3-Excellent
2-Good
1-Satisfactory
Team CollaborationEach Team member presents or provide insights that reflects aspects of their project
CommunicationPresentation is well paced and communicated. Logical sequence is obvious.
PresentationProcess, insights and a solution to your project are presented orally. Explanation of your model is used to discuss your AI/ML project solution. Visual, physical artifact, website or app is presented to explain how users access the solution.
Reflection of ProcessProvide a balanced picture of the project by reflecting on positive aspects of the project, areas that needed improvement and future ideas to implement.
PassionPassion is demonstrated from team members
LengthPresentation is within 5-min range