Editing DS4UX (Spring 2016)

:'''Community Data Science: Programming and Data Science for User Experience Research'''

:'''HCDE598A''' - Department of Human Centered Design & Engineering

*performing data visualization and statistical analysis using code (not GUIs)

*implementing experimental designs such as A/B tests and funnel analysis

== Overview and Learning Objectives ==

*Understand database schemas and use MySQL to extract user data from relational databases.

*Use web-based data to effectively answer a substantively interesting question and to present this data effectively in the context of both a formal presentation and a written report.

=== Where does this course come from? ===

This course is based on the curriculum of the [[Community_Data_Science_Workshops|Community Data Science Workshop series]] and [http://www.com.washington.edu/hill/ Professor Benjamin Mako Hill's] course ''[[Community_Data_Science_Course_(Spring_2015)|COM597G: Community Data Science: Programming and Data Science for Social Media]]'', offered first in Spring 2015, as well as in [[Community_Data_Science_Course_(Spring_2016)|Spring 2016]].  

For the first three weeks, we'll be paralleling these previous courses quite closely. Once we have hammered down some of the basics, we'll start to explore some tools, skills, and use cases that are more directly aligned with HCDE backgrounds and UX professional contexts.

You should expect this syllabus to be a dynamic document and you will notice that there are a few places marked "''To Be Determined''." Although the core expectations for this class are fixed, the details of readings and assignments may shift based on how the class goes. As a result, there are three important things to keep in mind:

# Closely monitor your email or the announcements section on the [https://canvas.uw.edu/courses/1040891/announcements course website on Canvas]. Because this a wiki, you will be able to track every change by clicking the ''history'' button on this page. I will also summarize these changes in an announcement [https://canvas.uw.edu/courses/1040891/announcements on Canvas] that will be emailed to everybody in the class.

# I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback.

This class is going to be a studio and project based class. Although we will not rely very heavily on readings or discussing them in depth in class, '''[http://www.pythonlearn.com/book.php Python for Informatics: Exploring Information]''' by Charles Severance covers much of the material we cover in this course, and can serve as a reference work for review and self-study.

Some people find it's helpful to have a book to learn a new programming language: it tells you want you "don't know you don't know". Other people prefer to use adhoc resources. I'll point you to resources I find helpful throughout the semester.

* I expect you to come to class every day ''with your own laptop''. Windows, Mac OS and Linux are all fine but an iPad or Android tablet is not going to cut it. We're going to install software during the class and you'll be working on projects for homework so please bring the same laptop each time. If for some reason your laptop dies mid-course, please contact me so we can get your new one up to speed.

* Much of the class will be project-based and Ray and I will be available to help you through challenges you encounter in this work during class. If you have questions and need to reach to somebody outside of class, however, please reach out to me!

== Assignments ==

The assignments in this class are designed to give you an opportunity to try your hand at using the technical skills that we're covering in the class. In addition to the four major assignments described below, there will be weekly assignments (tutorials and coding challenges) that I will ask you to complete and (sometimes) hand in, but these will only be graded as ''complete/incomplete''. Like many technical subjects, Data Science tends to build on earlier ideas, so I strongly suggest that you devote time to assignments every single week.

=== Final Project Idea ===

:'''Maximum Length:''' 600 words (~2 pages double spaced)

:'''Due Date:''' May 2

In this assignment, you should identify an area of interest, at least 2 datasets or APIs with relevant data, and at least 3 questions that you plan to explore. I am hoping that each of you will pick an area or domain that you are intellectually committed to and invested in (e.g., in your business or personal life). You will be successful if you describe the scope of the problem and explain why you think the data sources you've identified are relevant.

I will give you feedback on these write-ups and will let you each know if I think you have identified a questions that might be too ambitious, too trivial, too broad, too narrow, etc.

=== Final Project Proposal ===  

This proposal should focus on two questions:

Your proposal should frame your final analysis, but it's also a chance to "sanity check" your plan. I will give you feedback on these proposals and suggest changes or modifications that are more likely to make them successful or compelling. I will also work with you to make sure that you have the resources and support necessary to carry out your project successfully.

=== Final Project Presentation ===

;Timing: All presentations will need to be '''a maximum of 5 minutes long'' with additional 2-3 minutes for questions and answers. Timing is going to be tight and I'm going to set an alarm and stop presentations that go too long. Concisely communicating an idea in the time allotted is an important skill in it's own right.

;Slides: You are encouraged to use slides for your talk but I will need your slides ahead of class.

=== Final Project Report ===

The very best reports will give us all a new understanding of some aspect of course material and change the way I teach some portion of this course in the future.

==== Code ====

At least 25% of your grade for this project will be determined by the visualizations and tables in your report. Good visualizations should "stand alone" and motivate the core results in your paper all by themselves. A good question to keep in mind is "could I tell this story with the visualizations and a tweet?"

=== Weekly Tutorials and Coding Challenges ===

Graded coding challenges only account for a small part of your grade, but if you repeatedly ignore them the numbers will add up. Worse: you're likely to fall behind in the course. Best practice is to always turn something in! Remember: you're not being graded on whether you answered the question correctly, only that you attempted to answer the question.

=== Participation ===

In general, I prefer that students feel they can "politely interrupt" at any time to seek clarification or make a well-informed point.

== Schedule ==

Attendance in class is expected of all participants. This class is going to move very quickly and the things we learn will build on the things we've covered the week before. ''It will be extremely difficult to miss classes.'' If you need to miss class for any reason, please contact the instructor ahead of time (email is best). Multiple unexplained absences will likely result in a lower grade or (in extreme circumstances) a failing grade. In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc.

=== Office Hours ===

Remote meetings, using Skype or Google Hangout, are always an option. Please contact me or Ray Hong on email to arrange a meeting.

=== Disability Accommodations Statement ===

To request academic accommodations due to a disability please contact Disability Resources for Students, 448 Schmitz, 206-543-8924/V, 206-5430-8925/TTY. If you have a letter from Disability Resources for Students indicating that you have a disability that requires academic accommodations, please present the letter to me so we can discuss the accommodations that you might need for the class. I am happy to work with you to maximize your learning experience.

=== Grades ===

|30%

|}

==== Participation ====

You are not graded on whether you show up to class, or not. However, you ''are'' graded on your participation in class. Everyone misses class every once in a while. However, if you need to miss a class and you want to assure that your absence does not negatively impact your grade, follow the steps steps to demonstrate that you engaged with the material covered during that class session.  

'''In your email:''' please ask questions, what confused you, tell us how far you got on the coding challenges, and be honest about what you couldn't complete. I also highly recommend that you share your solutions or partial solutions in the email--sending me python files and/or pasting code into the body of an email is totally fine--this helps me and Ray understand your thought process and evaluate your effort and your progress.

==== Coding Challenges ====

Only about half of the coding challenges will be graded (and I will be very clear which ones those are, so there is no confusion). Graded coding challenges are evaluated as ''complete/incomplete''. You gain a 'complete' for coding challenges by turning in the assignment (whether or not your answers are correct, or your code runs) via the submission channel I specified for that assignment.

==== Final project deliverables ====

   

;2.7 - 2.9: Borderline work for a graduate student; barely meets the minimal expectations for a graduate student in the course. Work is inadequately developed, important issues are misunderstood, and in many cases assignments are late or incomplete. This is the minimum grade needed to pass the course. ''Clients who received a deliverable of this quality would likely delay payment until one or more criteria were met.''

=== Plagiarism ===

;Writing:

Please don't use other people's writing as your own. It's easy to spot plagiarism of this type, and the consequences can be serious (see link below).