CMSE 495
Michigan State University Data Science Capstone.
CMSE495 Project Showcase (in-person)
Wednesday April 17, 2023 -
The CMSE Project showcase will be held in our classroom and will be open to the public.
The style of the presentations will be “Digital Posters”. Each team will use the Presentation TVs on the walls of the classroom to present what they have done for the semester during a 60 minute window. Visitors will walk around to each teams TV and ask questions. All student’s are expected to attend in-person.
Teams will be split into two groups. Group 1 will present while Group 2 asks questions and then they will switch. Each team will be assigned one of the TVs and are expected to plug one of their laptops (HDMI) to the TV and start displaying their content. Content may include slides, demo code and/or videos.
Presentations in the “Digital Poster” format are expected to be flexible and dynamic so that visitors can ask questions and interact with the team. It is recommended that each team have a short (1-2 minute) overview of the project that discusses the basic goals and outcomes of the project. Then the teams should be prepared to give a demo and/or answer questions from the audience and have other visuals available to help. Teams may want to assign roles to their members. For example, greeters engage new visitors as they approach their TV, maybe others on the team will be available to answer specific questions and yet others can be in charge of giving demos. What you do will depend on your team but make sure you are flexible and can keep the conversation going.
Agenda (80 Minutes)
- (20 minutes) Setup
- (30 minutes) Group 1 Presentations
- (30 minutes) Group 2 Presentations
| TV | Group 1 - 3:00-3:30pm | Group 2 - 3:30-4:00pm | |
|---|---|---|---|
| 1 | City of Grand Rapids - Social Impact | ICER - User Data Analytics | |
| 2 | QSIDE - SToPA: MultiTown Data Analysis | TwoSix - LLM to Graphs | |
| 3 | Ford - Defect Prediction | Techsmith - Healthy and Engaged User Data Exploration | |
| 4 | HAP - Synthetic Data Generation | Kellanova - Point of Sale Analysis | |
| 5 | Intramotev - Automated Video Data Labeling for Autonomous Trains | HFH - Revenue Cycle Prediction | |
| 6 | CEPI - Anomaly Detection | Tribal Start Program - Tribal Early Childhood Research Data |
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Written by Dr. Dirk Colbry, Michigan State University
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.