Final Project proposal details#

Process#

  1. Find a dataset that seems interesting.

    • Use at least one dataset that you aren’t familiar with.

      • Using data from a primary source is preferred.

    • To meet the requirement that your project “not be trivial,” you probably want a dataset that’s large enough that you can’t understand it at a glance. In other words, you probably want it to have 500+ rows.

    • Finding a dataset available in CSV or JSON is recommended, though pandas can read other formats.

  2. If necessary, upload the data.

  3. Load the data into a DataFrame.

  4. Inspect the data a bit.

  5. Come up with a question that the data is capable of answering and isn’t trivial to answer.

    • If you aren’t sure, ask.

  6. Come up with a hypothesis (a.k.a. a guess of the answer to the question).

  7. Submit the proposal to the Final Project proposal thread on Ed, using the format below.

If the proposal shows effort and follows the format below, full credit will be given.

Format#

  • What dataset(s) are you going to use?

    • Please include link(s).

  • What’s the question you are trying to answer?

    • It should be specific, and objectively answerable through the data available.

  • What columns of the DataFrame(s) are you going to use to answer that?

  • If you’re using multiple datasets: What columns are you going to merge/join them on?

  • What’s your hypothesis?

Feel free to include any questions/concerns/uncertainties that the Reader can help with.

Tips#

  • Don’t overthink it; the process above shouldn’t take more than a few hours.

  • Your question/hypothesis doesn’t need to be something novel; confirming something you read / heard about is fine.

  • You won’t be graded on the scientific soundness of your work.

    • That said, please think through and note assumptions/caveats/unknowns of your approach.

  • The sooner you post your proposal, the sooner you’ll get feedback.

  • The point of the proposal is to ensure you’ve dug into the data and that your project scope is reasonable. Think of it as a head start rather than something you’re locked into.

Simplified example#

  • Dataset: Recycling Diversion and Capture Rates

  • Question: From 2016 to 2019, what community district increased their diversion (recycling) rate the most?

  • Columns: District, Fiscal Year, Diversion Rate-Total (Total Recycling / Total Waste)

  • Hypothesis: Bushwick, because it’s gentrified over that time, and hipsters love to recycle.

Another example#

  • Dataset: data about people’s trash

  • Question: Is recycling better now than before?

  • Hypothesis: probably

What’s wrong with this proposal?

Question your question#

Even the question can bake in assumptions. For example:

What ZIP code has the highest number of food poisoning cases?

assumes a relationship between food-borne illness and geography. What assumptions does your question make?

Changing your proposal#

You may change your dataset/question after your proposal was submitted, but unless directed to do so, it’s not recommended. The purpose of the Final Project is to get practice doing data analysis through code … the dataset and research question are a means to that end. The hope is that you’re getting into the analysis sooner than later, rather than being stuck picking the perfect dataset/question.

If you do decide to change them, be very sure they meet the requirements/tips above.