Editing Intro to Programming and Data Science (Spring 2020) (section)

= Assignments =

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. 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 will also be weekly programming assignments that I will ask you to hand in but which will only be graded as complete/incomplete. I will randomly sample from the assignments to make sure that people are understanding the topics and I will randomly choose students to share their responses to exercises as an extra way to incentivize you to complete them.

== 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 semester.
* '''Find a dataset''' — You should quickly identify a dataset you will use to complete this project.
* '''Report and interpret your findings''' — 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 produce 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.

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 plan and dataset identification ===

;Due date: January 28, 2020
;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 and when you will.
* A short (several sentences?) description of how the project will fit into your career trajectory.

=== Project planning document ===

;Due date: Thursday, March 10, 2020
;Maximum length: ~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 paper ===

;Paper due date: May 5, 2020
;Maximum length: 4500 words (~18 pages)

;Presentation due date: April 28, 2020
;Maximum length: 8 minutes

==== The paper ====

Ideally, I expect you to produce a high quality short research paper that you might revise and submit for publication. I do not expect the paper 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]].

As noted above, you should also 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.

I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software to handle your bibliographic sources.

I am also open to projects that are in the form of a Jupyter notebook, but I expect the same sorts of content to be present.

==== 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.

All presentations will need to be ''a maximum of 8 minutes long'' with additional 2-3 minutes for questions and answers. Concisely communicating an idea in the time allotted is an important skill in its own right.

== Participation ==

The course relies heavily on participation. The material we're going to be covering is difficult and we're going to be covering it quickly. It is going to be very difficult to make up any missed classes. Attendance will be the most important part of participation and missing more than 1 class is going to make it extremely difficult to excel in our class.

Nearly every week, we will begin by discussing challenges and problem sets. Please speak up and engage in this part of the class as well as asking questions anytime there is anything confusing. If you are feel confused about a new Python concept, it's highly unlikely that you are the only one. If there is anything I can do to help you participate in class, please let me know in the anonymous feedback.

In general, my teaching style is more conversational than a formal lecture. I prefer that students feel they can "politely interrupt" at any time to seek clarification or make a well-informed point, and we keep the class small to encourage this.

== Weekly Coding Challenges ==

Most weeks I will give you all a set of weekly coding challenges before the end of class that will involve writing code or adding to code that I've given you. 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.

I will share my solutions to each of the coding challenges in the subsequent class or via email. As you will see over the course of the semester, 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!

You are welcome to discuss the exercises on our Brightspace discussion board but please do not share answers to challenges more than 24 hours before they are due. After that, you are welcome and encouraged to share your solutions and/or to discuss different approaches. We will discuss a few of the exercises during class and I will randomly choose a few students to explain their solutions.

== Reflection papers ==

As discussed in more detail [[#Grades|below]], four 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.