CMSE 201 Final Project Template

CMSE 201 Final Project Template#

INSTRUCTIONS: This is a template to help organize your project. All projects should include the 5 major sections below (you do not need to use this template file). If you use this file, complete your work below and remove content in parentheses. Also, remove this current cell.

CMSE 201 Final Project#

✅ MY_NAME#

✅ Section_00X#

✅ Date#

PROJECT TITLE HERE#

Background and Motivation#

(Provide context for the problem. Clearly state the question(s) you set out to answer.)

Data and Algorithm Contexting#

As we have explored so far this semester, the context of your data and/or model is critical for conducting ethical work and informing what claims you can make based on your results. To that end, you will be required to articulate the context of your data or model as part of this checkpoint and in your final notebook and presentation. This checkpoint is your chance to ground your work in the context and practice articulating the context. Below, you will see example questions for a Data-intensive project or a Modeling-intensive project. Depending on your choice, you should make sure that you answer the listed questions in your context statement. Your statement can have more content than this but not less. If you aren’t sure of the answers to the questions, come to office hours and/or speak with your section instructors!

What to cover in a Data Context Statement#

  1. Who collected/generated the data?

  2. How was the data collected/generated?

  3. Who/what is included in the data?

  4. Who/what is not included in the data?

  5. What are the limitations or biases of the data?

  6. Based on the questions you’ve had to reflect on above, what are you going to do in your project because of this?

What to cover in an Algorithm or Modeling Context Statement#

  1. What model are you going to use (e.g. mathematical model, curve fitting, differential equations, compartmental model, Agent-based model, etc.)

  2. What assumptions will go into your model?

  3. What biases might be present in your model?

  4. What are the limitations of your model?

  5. How might your assumptions and biases affect your claims?

  6. Based on the questions you’ve had to reflect on above, what are you going to do in your project because of this?

Methodology#

(How did you go about answering your question(s)? Most of your code will be contained in this section.)

Results#

(What did you find when you carried out your methods? Some of your code related to presenting results/figures/data may be replicated from the methods section or may only be present in this section. All of the plots that you plan on using for your presentation should be present in this section)

Discussion and Conclusion#

(What did you learn from your results? What obstacles did you run into? What would you do differently next time? Clearly provide quantitative answers to your question(s)? At least one of your questions should be answered with numbers. That is, it is not sufficient to answer “yes” or “no”, but rather to say something quantitative such as variable 1 increased roughly 10% for every 1 year increase in variable 2. Make sure to connect your results with your context!)

References#

(List the source(s) for any data and/or literature cited in your project. Ideally, this should be formatted using a formal citation format (MLA or APA or other, your choice!). Multiple free online citation generators are available such as http://www.easybib.com/style. Important: if you use any code that you find on the internet for your project you must cite it or you risk losing most/all of the points for you project.)

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