Editing Intro to Programming and Data Science (Summer 2021) (section)

== Research project ==

As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:

* '''Design and describe a plan for a study''' — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this module.
* '''Find a dataset''' — You should very quickly identify a dataset you will use to complete this project.
* '''Analyze, visualize, report, and interpret your data''' — You will do this in both a short paper and a short presentation.
* '''Ensure that your work is replicable''' — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.

''I strongly urge you'' to work on a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, that you can use as pilot analysis that you can report in a grant or thesis proposal, and/or that fulfills a degree requirement. I prefer that you do projects on your own but it may be possible to work as a small team (maximum 3 people). Team projects are expected to be more ambitious than individual projects. Preliminary assignments will help you to develop your idea and to get feedback from me and others.

There are several intermediate milestones and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Brightspace.

=== Project idea and dataset identification ===

;Due date: May 19
;Maximum length: 500 words (~1-2 pages)

Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:

* An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.
* An identification of the dataset you will use and a description of the columns or type of data it will include. If you do not currently have access to these data, explain why not and when you will have access (If you need ideas, [[Data_Into_Insights_(Spring_2021)/Final_project#Datasets|this page]] from one of my undergrad classes lists some open datasets).
* A short (several sentences) description of how the project will fit into your career trajectory.

=== Project planning document ===

;Due date: May 27
;Maximum length: ~4-5 pages

The project planning document is a basic shell/outline of an empirical quantitative research paper. The planning document should focus around three big questions:
* Why are you planning to do this analysis? Make sure to introduce any background information about the topic, the community, your business, or anything else that will be required to properly contextualize your study.
* How will you get the data to analyze? Describe the data sources will you collect and how they will be collected.
* How will you analyze the data? Describe the visualizations, tables, or statistical tests that you will produce.

One approach that I have found helpful is outlined [[CommunityData:Planning document|on this wiki page]].

=== Project presentation and report ===

;Report due date: June 11
;Maximum length: 4000 words (~15 pages)

;Presentation due date: June 10
;Maximum length: 8 minutes

==== The project report ====

You will craft a Jupyter Notebook that will ideally provide the foundation for a high quality short research paper that you might revise and submit for publication. I do not expect the report to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the [[structure of a quantitative empirical research paper]].

The great thing about a Jupyter Notebook is that it allows you to provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.

Because the emphasis in this class is on methods and because I'm not an expert in each of your fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.

Jupyter Notebooks do not have all of the tools for citations that Word or LaTeX or even Google Docs have, so while I expect you to cite related work it is OK if it isn't as polished as citation management software would make it.

==== The presentation ====

The presentation will provide an opportunity to share a brief summary of your project and findings with the other members of the class. However, don't treat it as a comprehensive overview of your paper: I would rather you tell a subset of the story well than the whole story in a rushed fashion. For instance, you can give a completely successful presentation by describing the motivation and walking through one plot in your paper. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills.

I anticipate that most people will either create a PowerPoint presentation or will walk us through their Jupyter Notebook. All presentations will need to be ''a maximum of 8 minutes long''. Concisely communicating an idea in the time allotted is an important skill in its own right. Presentations should be uploaded to the Discussion forum on Brightspace created for this purpose.