Tips for Final Report

  • Be terse and professional.
  • Provide an Abstract: a very short, full summary of the research.
  • Present answers. Don’t present the original questions.
  • Don’t explain the methodology.
  • You may want to explain how the data is insufficient or biased.
  • Present the sources of the data you used, but avoid details on its structure (unless that helps understand the results). Where and how the data was collected can inform the reader of potential biases.
  • Be data driven and avoid speculation.
    • You may do some speculation, but you should have some evidence or source that you cite.
    • If you speculate, be sure that you’re not conflating your opinion with the facts.
    • In the write-up, it should be clear to the reader that you’re speculating and that you know you’re speculating.

You may want to extend beyond your peers to make your project unique and cool. You will want to find creative and valuable additions to incorporate. Here are a few considerations.

Make Connections

Do your findings and takeaways support each other? Or contradict each other?

Once you have created your visualizations, machine learning model, or any other means of extracting information from your data, your task now is to have them communicate with each other.

Example:

In the study of alcohol abuse and related factors in the United States, the following information was extracted by various datasets:

  1. Visualizations
  2. Regression ML Model error
  3. Analysis on the ML Decision Tree
  4. Feature Importance

One commonality between all of the information was that they supported that income levels can affect the rates of alcohol abuse. Highlighting these connections in your presentation can help connect fragmented parts of your research and build a deeper connection overall.