Editing Intro to Programming and Data Science (Fall 2024) (section)

= Assignments =

There will be three main types of assignments. Each is discussed in detail below but here is a brief summary:

# '''Research Project:''' The main outcome of this course will be a research project exploring a social science question using Python, and the bulk of your grade will be based on that project. Submit these via Brightspace
# '''Coding Challenges:''' There will be weekly programming assignments that I will ask you to turn in on Brightspace but which will only be graded as complete/incomplete. I will also randomly assign someone to present their solution to each of the problems during our synchronous sessions.
# '''Paper Discussion:''' Each week we will read and discuss a paper which uses computational approaches to address social science questions.

== 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:

* '''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: September 3
;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, [[/Datasets|this page]] lists some open datasets).
* A short (several sentences) description of how the project will fit into your career trajectory.

=== Project planning document ===

;Due date: October 15
;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: December 11
;Maximum length: 4000 words (~15 pages)

;Presentation due date: December 3 and December 5
;Maximum length: 8 minutes

==== The project report ====

You will write a document or 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 you choose to write the report as a Word document, then you will need to include the code in a separate file.

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 do not need to 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 your references section does not need to be 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 many of you will give many 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 a simple 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.

== Coding Challenges ==

Nearly every week you will have set of coding challenges to complete. These coding challenges will be turned in on Brightspace but will not be graded. I encourage you to work together on these challenges but to make sure that you understand the concepts yourself.

Each day I will randomly select a set of students to share their solutions to a selected exercise. If you can't figure out the problem that's been assigned to you, then explain where you got stuck and what you tried. I encourage you to also use Element to ask and answer each other's questions as you work on the assignments. We will use some of our lecture time to review the problems and I will make sure that a correct solution is posted by the end of that day. As you will see over the course of the module, there are many possible solutions to many programming problems and my own approaches will often be different than yours. That's completely fine! Coding is a creative act!

While I won't be grading these assignments, I will review a sample of them to look for common difficulties or problems. If you want me to provide specific feedback for an assignment, please let me know and I will be happy to do so.

Here's the link to the spreadsheet with what we want to go over: https://docs.google.com/spreadsheets/d/1lrgbxWMgE3ZL0B_S6Np4ZYj4BAC7Kxw-XkRkyTkJxrE/edit?usp=sharing

== Paper Discussions ==

Every Thursday we will review a paper that uses computational methods. On the first day, I will ask you to sign up to lead the discussion for one or more of these papers. When leading the discussion, you will prepare a presentation as though you were presenting the paper at a conference and then lead a discussion about it.

I am eager to update the papers that we read in the class. If you would like to propose a different paper that you would prefer to present on, I will almost certainly say yes.

When you are not presenting, I do not expect you to read the week's paper thoroughly. However, you should read it closely enough that you are prepared to discuss it.

== Reflection papers ==

As discussed in more detail [[#Grades|below]], two times during the course I will ask you to respond to a set of reflection questions. These questions are intended to help you to think about what you have learned and accomplished and to craft goals for the remainder of the course. They are also an important way for me to gather feedback about how the course is going so that I can adjust.