Deep Dive Coding participants all complete personal and team projects while in the bootcamp. With guidance from their instructors, participants select project ideas that they are interested in, that support the community, and that allow for hands-on learning of the bootcamp curriculum. Below are just a few of the hundreds of projects completed by past Deep Divers.
This Data Science cohort 2 capstone team analyzed data to identify underserved communities in New Mexico that would could benefit from additional Head Start locations and funding, using machine learning tools.
Using course survey data from over six years of Deep Dive Coding, this cohort 2 capstone team evaluated our evaluations, using unsupervised learning with aspects of natural language processing.
Created by Clint in IoT cohort 1, the Indoor Environment Monitor contains sensors to detect dust, smoke, carbon monoxide, and several other airborne toxins. When any of these sensors detects an unfavorable condition, a solar-powered fan is automatically activated to help vent the area. Sensor data is tracked on a cloud-base dashboard, and if dangerous air quality conditions exist, alerts and emails will be sent to the owner of the device. The owner can then investigate and take appropriate action.
Created by Nycole in IoT cohort 1, the arroyo project is a prototype system designed to monitor, record and warn when there are severe changes in the weather. This system will display auditory and visual signals in the cases of flash flood and earthquake. Using sensors for vibration, moisture, water speed, and universal weather data this system will help divert water if necessary. The system can be monitored remotely using online dashboards, and provide addition warnings through text message.
Created by John in cohort 1, the particle air sensor is used to detect very fine particle or particulate matter in the air. It measures 10 microns, 2.5 microns and 1.0 microns . Through a simple user interface the user can adjust the parameters of the sensor to their specific requirements. This will allow this device to be used anywhere from the dirtiest environments like industrial plants all the way to clean rooms. There are visual light indicators that show the user the quality of the air with green, yellow and red lights. If the air quality becomes of poor quality an alarm will sound, e-mail/ text alerts will be sent to the appropriate personnel to address the situation.
CNM Parking – An app used to request and print temporary parking passes for students and guests visiting CNM STEMulus Center in downtown ABQ.
ABQ Bike Trails – ABQ Bike trails utilized the city of Albuquerque’s bike paths open data set in an attempt to plot the safest bike routes for commuters and visitors looking to explore the The Duke City by bike. We used the LAMP stack on the back-end with React, Bootstrap, and Mapbox GL on the front-end.
Created by students in Java+Android cohort 9, Homestead is a tool that facilitates access to community services for the homeless.
Created by students in Java+Android cohort 9, Lightbulb is an application that encourages and creates an open forum discussion between users.
Created by students in Java+Android cohort 9, Scavengr is an Android application that is built with a client/server-side structure to organize and store scavenger hunts created by an organizer to share with hunters.
Created by students in Java+Android cohort 9, Office Hours allows teachers to make appointments with their students.