Project Timeline

The following is a rough guideline to help you understand what you should have done by when. As you'll learn in this class, the data science process is highly iterative, and so you should expect that you'll have to retrace your steps at times. That being said, if you want to be sucessful in this class, it will help to try to hit these milestones.

Deliverables

A list of deliverables and dates is below. Some details are subject to change as the quarter progresses.

Assignments vary between individual and group assignments, as noted. For group assignments, it is sufficient for one group member to submit via Canvas; please note the names and UW Net IDs of all group members in your submission.

Please submit your files on Canvas unless otherwise specified.

All deliverables are due at midnight PST before class as indicated in our schedule.

• Project Pitch (Individual): 2%
  See this presentation for details.
• Project Plan (Group): 4%
  See this presentation for details.
• Project Selection Reflection (Individual): 1%
  On Canvas: individually, submit a short reflection (one brief paragraph) about what influenced your project choice (e.g., interest in a topic or technology, desire to work with a specific person) and how the project changed from its initial presentation at the start of class, if at all (e.g., by merging projects or new brainstorming in post-class discussion).
• Example Paper Reflection (Individual): 2%
  On Canvas: individually, submit a short reflection (two brief paragraphs) about the data science example paper you read: How did (or didn’t) the paper address goals of construct, internal and external validity? How could other researchers improve or build on this work?
• Validity Reflection Presentation (Group): 4%
  See this presentation for details.
• Spark Word Count Colab Assignment (Individual): 1%
  Download this file, add it to your Google Drive, and open it with Google Colab, then follow the instructions within. Submit your answer in a single file containing both your code and output in a human readable format on Canvas.
• Midpoint Presentation Video (Group): 15%
  See this presentation for details.
• Midpoint Feedback Reflection and Action Plan (Group): 4%
  On Canvas: as a group, summarize and reflect on the feedback you received on your midpoint presentation. What will you add or do differently by the end of the quarter?
• Final Presentation Video (Group): 25%
  See this presentation for details.
• Final Project Report (Group): 25%
  See below for details.
• Summary of Individual Contribution to Project (Individual): 1%
  On Canvas: Individually, not in groups, submit a ~1 page summary of your individual contribution to your group’s project.
• Final Reflection (Individual): 2%
  On Canvas: individually, submit a final reflection. This reflection should include:
  (1) Notes on other presentations: suggestions for improvement if the project were to continue,
  (2) Notes on own project: what would you still do if the project were to continue, and
  (3) Reflections on the overall process: what worked well, didn’t work well, would you do differently next time.
• Feedback to other students all throughout quarter (Individual): 14%
  We will use this form to give each other feedback during the course.

Final Report

The final project report should be a 5-10 page paper, describing the introduction, analysis approach, results, related work and conclusion. We will not accept reports longer than 10 pages (page count includes figures, but excludes references). Please use the NeurIPS 2019 template as given here or here. If you are not using LaTeX, you may follow the formatting instructions given in Section 2 of the links. We will discuss in detail how to write a good paper towards the end of the class.

You should use the following structure for your final report:

• Abstract
• Introduction/Motivation
  • What is your precise research question(s)?
  • Why is answering this question important?
  • What hypotheses do you have and what are they based on?
• Dataset description
  • Summary of your data and any preprocessing you might have done with it.
• Analytical Approach
  • Describe your analysis approach. It is especially important that this part be clear and well written so that we can fully understand what you did.
  • How have you turned your research questions and hypotheses into concrete analyses?
  • What are key considerations and challenges and how did you address them?
  • Describe what you did to ensure and evaluate the validity of your approach. Consider construct, internal and external validity.
• Results / Findings
  • We are interested in seeing a clear and conclusive set of analyses or experiments which successfully answer the research question you set out to answer.
• Discussion of results/insights
  • Discuss and interpret your results.
  • What story do your results tell?
  • Carefully describe what your work shows and doesn’t show based on any potential limitations.
• Related Work
  • How does your project relate to other research papers? Please give a short summary on each paper you cite and include how it is relevant. The papers may share the same research questions, data, or analytical approach. Please include at least two papers.
• Conclusion