CMSE 201 - Semester Project#
As part of your semester in CMSE 201, you are expected to complete a fully-fledged data analysis or computational modeling project. This is your opportunity to choose a topic that sounds interesting to you and dig into it.
At the end of the semester you’ll submit your work as a detailed Jupyter Notebook and you’ll present your findings to the rest of the class.
Choosing your project#
You have the freedom to choose from any of the techniques that we have covered and will be covering in class. These techniques include, but are not limited to:
Data Science: analyzing, modeling, and visualizing data
Computational Modeling: building, running, and evaluating models with
ordinary differential equations
compartmental models
agent-based models
Furthermore, you can also choose nearly any topic that you find interesting or exciting. Possible areas of interest may be:
Physical Systems: modeling problems in physics, chemistry, ecology, biology, etc.
Social Science: modeling people and their interactions
Finance, Banking, and Economics
Diseases: modeling the tissue scale or the person scale
No matter what you choose, the main goal is to define a question that you think you can answer using the techniques we’re learning in class and not just a question that you can look up the answer for on the internet.
Project requirements#
A successful project will include all of the following components:
A question that you will attempt to answer.
A model, expressed in broad mathematical or descriptive terms, that can be applied to a dataset or chosen topic.
Computional methods that seek to answer your question. (This will vary from project to project, depending on the context).
Meaningful visualizations that productively convey your results.
An answer to the question or an explanation as to why you were unable to answer the question.
Locating data and models#
You’ll need to spend some time figuring out what model is the right model to use or what data are available to answer your question. The internet is your friend for this part. You should be able to find the details you need to compute your model or the data you want to analyze on the web. These are some possible, non-exhaustive resources for locating data:
You may also wish to explore some of the additional resources listed on this page: https://www.dataquest.io/blog/free-datasets-for-projects/
For the models, you’ll need to do a bit of background research and determine which of the models we’re working with in class are the most appropriate for your question.
VERY IMPORTANT NOTE: When you’re finding datasets online you should make sure to record exactly where you found the dataset and cite the source in your final project notebook. Additionally, if you use any code that you find online to complete part of your project you must give credit to the original source code and cite this in your project as well. Any code you use that is found online and not properly cited will be considered plagiarism and violates the academic integrity expected of you in this course.
Project Timeline#
The following is a tentative plan for the timeline we’ll be following to ensure that you successfully complete your assignment. There may end up being slight shifts in these days based on our progress through the course material.
Oct 16/17: Introduction to project. Project Checkpoint assigned.
Nov 6/7: Project Checkpoint Due
Nov 20/21: Draft of Semester Project Due; You will submit a preliminary draft of your project notebook, along with 1-2 slides explaining your project and highlighting your work/results so far.
Nov 25/26: Structured Project Work Day 1; All students will give lightning presentations, where everyone will give a short (<90 second, 2 slides max) presentation of their project in class. They will receive feedback from their classmates about their project.
Dec 2/3: Semester Project Due
Dec 2 through 6: Semester Project Presentations
We’ll spend the end of the semester having everyone present the outcomes of their projects. The requirements for the presentation will come out later in the semester.