- Course Sessions
- Saturday Night Electives
- Meet the Scientist
Additional Material
Introduction to NetLogo, Track 1, Schedule
- 3 hours of Netlogo coding class
- Overview of Artificial Intelligence
- Dear Data class slides
- Meet the Scientist
Meet with a Scientist to help you make your proposal better.
Geospatial Agent-based Modeling / Netlogo Class, Track 2, Schedule
- Overview of Artificial Intelligence
- 3 hour Intermediate Netlogo coding class
- Dear Data class slides
- Meet the Scientist
Meet with a Scientist to help you make your proposal better.
Introduction to Python, Track 3, Schedule
We will do some data analysis using pandas, matplotlib, seaborn.
Predictive Data Analysis is the art of building and using models that make predictions based on patterns extracted from historical data. Topics include data preparation, data exploration, and data visualization using Python libraries such as pandas, sklearn, matplotlib, numpy, and seaborn. This course will be an interactive exploration of datasets involving Python programming in Google's Colab environment. Students will need a Google account and the ability to access the Colab programming environment. Some knowledge of Python recommended, but the Python libraries will do most of the heavy lifting.
- 3 hour Introduction to Python coding class
- Dear Data class slides
- Overview of Artificial Intelligence
- Meet the Scientist
Meet with a Scientist to help you make your proposal better.
Experienced, Track 4, Schedule
- Jupyter Notebooks
Class video part 1
Class video part 2 - Data Science and Relational Modeling
Linear and Nonlinear Correlation - Meet the Scientist
Meet with a Scientist to help you make your proposal better. - Video of Machine Learning class
- Python Mesa slides
Video of Python Mesa class
MIT App Inventor, Track 5, Schedule
This is a unique 5-hour MIT App Inventor learning strand. Students, if you decide this is right for you, you'll need a team member with an Android or Apple smartphone, a laptop computer, and a good attitude for learning.
Students, register here for this track.
Teacher Afternoon Tea, Speare room 116, 1:45 - 2:45
- Come for refreshment and discussions.
- Freebies: Book swap and posters of young local STEM women from NMOST
- Handouts: Summary of STEM Competitions, Professional Development Opportunities
- Website Dates, Need for Login, Spam Filter
- Mentors
- Q and A
Facilitated by Consult (consult@supercomputingchallenge.org) Member Karen Glennon and Creighton Edington
Developing a Strong Project
- Developing a Strong Project webpage
Meet the Scientist/Proposal Review
The students should have submitted their proposals and reviewed the Computational Science Process map. The Meet the Scientist Proposal Review form will be filled out.
The purpose of the session is to make sure teams have chosen a problem that is suitable for computational science, has measurable components so that a mathematical model can be developed, and from that a computing solution can be written. The session is secondarily about mentoring teams who have good proposals and are ready to get started on their projects. Meet the Scientist is a key session for helping students get off to a good start on their projects. For info about Proposals, see: Proposals.
It may be helpful to look at the proposal guidelines and the proposals that are already up on the Challenge web page – Proposals. There is also a link on the web page for questions to ask to direct the students: Team Project Development. Additionally, Areas of Science links to areas of science and may be helpful for teams still looking for an idea. This guidelines link can be useful, too: Challenge Proposal Guidelines. You can see which teams have submitted proposal on the proposals page of the Challenge web site, Proposals.
Here is the Agent Based Planning Document for Middle School teams choosing to do an Agent Based model.
Introduction to Computational Science and Modeling
- Introduction to Computational Science and Modeling (.pptx)
- Computational Science and Modeling including Mathematical Modeling (.pptx)
- Computational Science Process Map
- Patterns of Change and Mathematical Models (.pdf)