The main deliverable for the final project is the presentation. I chose a presentation over a paper since I think presentation is an important skill that needs more emphasis across the curriculum. This is an opportunity to practice presenting and receiving feedback. I also wanted everyone in the class to be able to see the other projects, which doesn’t always happen with final papers.
In addition to the presentation, evening you should also submit (on git):
I’ll go through each of these pieces below. When thinking about what to include in your git repo, keep a reproducibility perspective in mind. From your lab notebook, references, code, and slides, I should be able to reproduce your project and results exactly.
Each person will have 4-5 minutes total to present. The scale with group size is not exactly linear since there is some startup cost to doing a presentation. Roughly we will do:
We will have a bit of time after each presentation for questions and transition to the next group. I will have a timer that will go off when your group has 1 min left. The best way to make sure you are hitting the right time is to practice.
To make transitions easier, please EMAIL me your slides by Wednesday Dec 18 at 9am. They must be in PDF format! I will put them on the lab machine and I’ll also have a laser pointer / slide advancer for you to use.
In terms of presentation content, you should (very briefly) include all the main components you mentioned in your proposal, as well as future work:
Introduce your topic and goal in a creative or visual way. Whenever you give a presentation, there will be those in the audience less interested in the topic than you are, who might question the “point” of your topic or thesis. Give them a reason to pay attention. Often this involves placing your topic in a larger context, using an image the audience can relate to, telling a personal story, or posing a question you’ll answer later in the talk.
Briefly explain your dataset and/or chosen methods. Try to pick one detail or aspect that you found interesting or challenging. If you are using methods we’ve talked about in class, you could expand on how you prepared the data. If you are implementing or using a new method, tie it to our class material and then explain how it is different or novel. Overall, try to briefly give the project a narrative; explain your thought-process throughout the project.
Display your results in a visual way. Negative results are results too, and can definitely be included. How did you evaluate and interpret your results? If they did not match your expectations, what might be going on?
In a few words, what were your main takeaways from the project? What would you do if you had 6 months to work on this project instead of a few weeks? What aspects would you change or extend further?
Speak loudly, to the back of the class.
Avoid text-heavy slides, try to use images and diagrams to convey information.
Include citations for any figures/info you use that you did not create (on the slide where you use it).
You do not need to include a full list of references in the slides.
For groups with more than one person, feel free to divide the presentation however you like, as long as each person gets equal time.
As an audience member, be respectful to the other presenters. Be on time and give them your full attention (this counts toward your participation grade).
Time permitting, each person should ask at least one question to another group. I know we have a large class and a long time block - sometimes keeping a question in mind is a good way to stay engaged.
See the Proposal page for details about the lab notebook (at the end). In addition to keeping track of what you have done so far, also include a list of references at the end. This should include anything that you made use of - papers, datasets, external software. The format of the references is also described in the Proposal. Think about the standard of reproducibility when creating your lab notebook.
Except for external software, include all code that was necessary to obtain your final results. Keep your code organized and commented. You can include some small example datasets, but avoid putting large data files on git since this can cause problems. Err on the side of including more results though (output files, figures, etc).
Make sure to put your presentation slides on git (I’ll have them over email as well, but just to make sure everything is in the same place).