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User:Groceryheist/drafts/Data Science Syllabus
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== Assignments and coursework == Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete. Unless otherwise noted, all assignments are due before 5pm on the following week's class. Unless otherwise noted, all assignments are individual assignments. === Weekly reading reflections === This course will introduce you to cutting edge research and opinion from major thinkers in the domain of human centered data science. By reading and writing about this material, you will have an opportunity to explore the complex intersections of technology, methodology, ethics, and social thought that characterize this budding field of research and practice. As a participant in the course, you are responsible for intellectually engaging with ''all assigned readings'' and developing an understanding of the ideas discussed in them. The weekly reading reflections assignment is designed to encourage you to reflect on these works and make connections during our class discussions. To this end, you will be responsible for posting reflections on the previous week's assigned reading before the next class session. There will generally be multiple readings assigned each week. You are responsible for reading ''all of them.'' However, you only need to write a reflection on '''one reading per week.''' Unless your instructor specifies otherwise, you can choose which reading you would like to reflect on. These reflections are meant to be succinct but meaningful. Follow the instructions below, demonstrate that you engaged with the material, and turn the reflection in on time, and you will receive full credit. Late reading reflections will never be accepted. ;Instructions # Read all assigned readings. # Select a reading to reflect on. # In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?" # Using full sentences, list ''at least 1 question'' that this reading raised in your mind, and say ''why'' the reading caused you to ask this question. # Post your reflection to Canvas before the next class session. You are encouraged, but not required, to make connections between different readings (from the current week, from previous weeks, or other relevant material you've read/listened to/watched) in your reflections. === Project Assignments === This section provides basic descriptions of all scheduled course assignments. In assignments 1 and 2 you will build on the skills you'll learn in the community data science workshop to analyze data of your own substantive interests. The goals are to reinforce learning from the workshop and to give you hands on experience that will help you think about how data science might apply to your own community or organization. Assignments 3, 4, and 5 scaffold your final project for the course in which you will conduct a qualitative study of data science in an organizational context. I strongly recommend for you to make arrangements to conduct observations and interviews with a data science team as soon as possible. === A1: Project proposal and data aquisition === For this assignment you will propose a midterm project and use the skills you have learned in the CDSW to collect or present a dataset. You will turn in a one-page project description that :* Identifies a dataset for analysis, and what makes it interesting to you. :* Explains how the source of the data, how did you get it? :* Describes 2-3 questions that the data can help answer, and explain how you will answer them. :* What results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself). :* Includes a table of summary statistics (minimum, maximum, median, and mean values) for variables in your dataset related to these questions I hope that you find a dataset related to your own interests, such as data from your workplace, community, or any other organization you may be involved in. For some ideas about where to look for datasets related to your interests, [[HCDS (Fall 2017)/Datasets | see this page]] with examples of freely available datasets that you can use for this project. ==== Evaluation and Rubric ==== :''Dataset identification:'' 20% :''Explaination of data source:'' 20% :''Example questions:'' 20% :''Anticipated results and their significance:'' 20% :''Summary statistics:'' 20% === A2: Data analysis === For this exercise, you will design and execute the analysis that you proposed in A1. You must attempt to answer to the questions you posed using your new data science skills, but you must also practice a kind of "meta-analysis" of your analysis to understand limitations and potential consequences of your analysis. Turn in a report of about 1500 words with about equal space dedicated to: * Presenting your analysis: what did you do and what did you find out? Communicate your findings though using at least one chart or table. * Explaining the significance of your analysis to the (real or hypothetical) organization or community that will make use of it. Why should we care about this analysis? * Critique of the analysis in terms of both what you did and how it might be used. How might your analysis improve through better data or analysis? What assumptions underlie your interpretation of it? How might (or might not) this analysis influence or mislead its audiences? ==== Evaluation and Rubric ==== :''Presentation of data analysis:'' 40% :''Appropriate chart or table:'' 5% :''Explanation of applicability to community or organization:'' 20% :''Critique in terms of improving the analysis :'' 10% :''Critique in terms of application :'' 10% :''Writing quality (see [[User:Benjamin Mako Hill/Assessment#Writing Rubric | the writing rubric]]): 15%'' === A3: Final project plan === For this assignment, you will write up a study plan for your final class project. The goal of this project is for you to apply what you have learned about data science studies to understanding and improving data science practice in an organizational context. The plan will cover a variety of details about your final project. Specifically your plan should: * Identify the organization that you will work with, and your contact there. * Summarize what you already know about this organization and how they use data science. * Identify a research question that you don't already know the answer to, but where project can realistically help you answer it. This should be specific and tied to a particular aspect of how data science is practiced or is used in this organization. Effective research questions will often raise issues or problems with the organization. * Outline your plan to collect qualitative data. Will you conduct interviews? Who with? What questions will you ask? Will you conduct workplace observations? * Explain what results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself). A reasonable amount of data collection for this project is about 4 30 minutes interviews with different team members, about 4 hours of workplace observation, or an equivalent combination of interviews and observation. Maximum length: 1500 words. ==== Evaluation and Rubric ==== :''Organization and identification:'' 20% :''Summary of what you already know:'' 20% :''Research question:'' 20% :''Data collection plan:'' 20% :''Anticipated results and their significance:'' 20% === A4: Final project presentation === For this assignment, you will give an in-class presentation of your final project. The goal of this assignment is to demonstrate that you are able to effectively communicate your research questions, methods, conclusions, and implications to your target audience. This is your chance to get quality feedback on your project from me and from your classmates. Key elements that you should cover in your presentation include: :* What organization or team you studied :* Your research question about how data science functions in this organization :* Your findings: what you learned about your research question from your data in relation to course material. :* Who you observed or interviewed :* At least one quotation or anecdote from your qualitative data that support your findings :* Tentative recommendations to the organization based on your findings ==== Evaluation and Rubric ==== :''Presentation organization and design:'' 15% :''Explaination and justification of a research question:'' 15% :''Presentation of evidence:'' 25% :''Findings:'' 25% :''Recommendations to the organization:'' 20% === A5: Final project report === In the final report, I expect you to take feedback from your presentation and and report on your project in up to 3000 words. You can organize your paper however you want, but it should do the following: :* Introduce the organization or team you studied :* Document how you collected data with the team (who you interviewed or observed, for how long, describe their jobs and roles). :* Motivate your project in terms of your substantive interest, curiosity, and course concepts. :* Introduce and articulate a specific research question about how data science functions in this organization. This will often be driven by a particular challenge or issue the team you are studying faces. :* The bulk of your report (about 2000 words) should argue for an understanding of the research question based on ::* Your empirical findings: what you learned about the organization and the practice of data science from your own observations or interviews. Use anecdotes and quotes as appropriate to support your argument. ::* Any course material relevant to the challenge or issue at hand. :* Make recommendations to the organization based on your findings. Optionally, you may make recommendations to me about the course material in relation to this project. Was there anything that you expected to see based on the course material that you didn't observe? Was there anything interesting that you observed that the course didn't address? How might you improve the course given what you learned? ==== Evaluation and Rubric ==== :''Writing quality (see [[User:Benjamin Mako Hill/Assessment#Writing Rubric | the writing rubric]]): 15%'' :''Explaination and justification of a research question:'' 15% :''Presentation of evidence:'' 25% :''Findings:'' 25% :''Recommendations to the organization:'' 20% :''Course feedback (Extra credit):'' 3%
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