Editing Community Data Science Course (Spring 2016)

:'''Community Data Science: Programming and Data Science for Social Media'''
:'''COM597G''' - Department of Communication
:'''Instructor:''' [http://guyrt.github.com Richard Thomas (Tommy) Guy] 
:'''Course Website''': We will use Canvas for [https://canvas.uw.edu/courses/1039305/announcements Announcements], [https://canvas.uw.edu/courses/1039305/assignments Assignments], and [https://canvas.uw.edu/courses/1039305/discussion_topics discussion]. Everything else will be linked on this page.
:'''Course Catalog Description:'''

This course will introduce basic programming and data science tools to give students the skills to use data to answer questions about social media and online communities. The class will cover the basics of the Python programming language, an introduction to web APIs including APIs from Wikipedia and Twitter, and will teach basic tools and techniques for data analysis and visualization. We will focus on gaining access to data and basic data manipulation rather than complex statistical methods. As part of the class, participants will learn to write software in Python to collect and process data to produce numbers, hypothesis tests, tables, and graphical visualizations that answer questions like: Are new contributors in Wikipedia this year sticking around longer or contributing more than people who joined last year? Who are the most active or influential users of a particular Twitter hashtag? Are people who join through a Wikipedia outreach event staying involved? How do they compare to people who decide to join the project outside of the event? The class will be built around student-designed independent projects and is targeted at students with no previous programming experience.

== Overview and Learning Objectives ==
<div style="float:right;">__TOC__</div>

In a world that is increasingly driven by software and data, developing a basic level of fluency with programming and the basic tools of data analysis is a crucial skill. This course will introduce basic programming and data science tools to give students the skills to use data to answer questions about social media and online communities.

In particular, the class will cover the basics of the Python programming language, an introduction to web APIs including APIs from Wikipedia and Twitter, and will teach basic tools and techniques for data analysis and visualization. As part of the class, participants will learn to write software in Python to collect data from web APIs and process that data to produce numbers, hypothesis tests, tables, and graphical visualizations that answer real questions. The class will be built around student-designed independent projects. Every student will pick a question or issue they are interested in pursuing in the first week and will work with the instructor to build from that question toward a completed analysis of data that the student has collected using software they have written.

This is not a computer science class and I am not going to be training you to become professional programmers. This introduction to programming is intentionally quick and dirty and is focused on what you need to get things done. If you want to become a professional programmers, this is probably not the right class. If you want to learn about programming so that you can more effectively answer questions about social media by writing your own software and by managing and communicating more effectively with programmers, you are in the right place.

I will consider this class a complete success if, at the end, every student can:

* Write or modify a program to collect a dataset from the Wikipedia and Twitter APIs.
* Read web API documentation and write Python software to parse and understand a new and unfamiliar JSON-based web API.
* Use both Python-based tools like MatPlotLib as well as tools like LibreOffice, Google Docs, or Microsoft Excel to effectively graph and analyze data.
* Use web-based data to effective answer a substantively interesting question and to present this data effectively in the context of both a formal presentation and a written report.
* The ideal outcome is that students will have the working knowledge to more effectively collaborate with data professionals in their careers. They will be both more informed about the process and more likely to spot un-declared assumptions in their colleague's work.

== Note About This Syllabus == 

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:

# Although details on this syllabus will change, I will not change readings or assignments less than one week before they are due. If I don't fill in a "''To Be Determined''" one week before it's due, it is dropped. If you plan to read more than one week ahead, contact me first.
# Closely monitor your email or the announcements section on the [https://canvas.uw.edu/courses/1039305/announcements canva]. Because this a wiki, you will be able to track every change by clicking the ''history'' button on this page (you'll even learn how to do this in a program in this class!). I will also summarize these changes in an announcement [https://canvas.uw.edu/courses/1039305/announcements 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. 

== Readings ==
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, I'm strongly recommending a book that will cover the material we go over in class and which will provide a reference work for you to refer to:

# '''[http://www.pythonlearn.com/book.php Python for Informatics: Exploring Information]''' by Charles Severance. The book is available online for free but you can also buy a physical copy of the book [http://www.amazon.com/gp/product/1492339245/ref=as_li_ss_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=1492339245&linkCode=as2&tag=drchu02-20 from Amazon] or get an electronic copy from the [http://www.amazon.com/dp/B00K0O8HFQ Kindle Store]. According to the book's website: "The goal of this book is to provide an Informatics-oriented introduction to programming. The primary difference between a computer science approach and the Informatics approach taken in this book is a greater focus on using Python to solve data analysis problems common in the world of Informatics."

There will still be a few readings throughout the semester including at least the following:

# Chapter 2 ("How to Keep Score") of [http://www.amazon.com/Lean-Analytics-Better-Startup-Faster/dp/1449335675 Lean Analytics] by Alistair Croll and Benjamin Yoskovitz.
# Chapters 1 and 2 of [http://masteringmetrics.com/ Mastering Metrics] by Joshua D. Angrist and Jorn-Steffen Pischke. 
# Several excellent uses of data in articles from [http://www.fivethirtyeight.com FiveThirtyEight] and similar sources. 

If you run across a particularly great example of a story told with data, please pass it along!

For book chapters, I'll make pdfs available at least 1 week ahead of time. In general, you should expect to spend an hour or less reading per week and 6 or more hours a week on programming tasks.

== General Notes ==

* 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.
* Jason Portenoy is our assistant during class. Much of the class will be project-based and Jason 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!
* I can be reached at the following: richardtguy84@gmail.com or guyrt@uw.edu (it all flows to the same place). Email is generally the easiest way to reach out, but Google Hangouts at richardtguy84 will also work. Like many of you, I work 9-5 but I commit to responding to any email within 24 hours of receipt and generally less than that.

== 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. There will be no exams or quizzes. There will be weekly assignments that I will ask you to hand-in but 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:''' April 13
:'''Drop box:''' [https://canvas.uw.edu/courses/1039305/assignments/3252050 Canvas]


In this assignment, you should identify an area of interest, at least 2 source domains with relevant data, and at least 3-4 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 === 
:'''Maximum Length:''' 1500 words (~5 pages)
:'''Due Date:''' May 4th (at 6pm)

TODOREVIEW
Building on your project idea assignment, you should describe the specific types of data you will collect, the steps you will take to collect the dataset, the limits and strength of these data for answering the question you have selected, and a description of the kinds of report and visualization you will make.

And important step here is going to be ''framing'' your analysis. Why is this is an important question? Why do you care? What do we need to know (e.g., about the question, about underlying theories, about your business, about the topic, about the community) to understand this analysis? This will all need to be part of your final project and it's good opportunity to do this, or at least being doing it, here.

I will give you feedback on these proposals and suggest changes or modifications that are more likely to make them successful or compelling and to work with you to make sure that you have the resources and support necessary to carry out your project successfully.

Be as specific as possible about the data available on the sources you've chosen. Identify the documentation and the API endpoints where required. If there are libraries that you think may help with access, note them.

=== Final Project ===
:'''Presentation Date:''' June 1, 2016
:'''Paper Due Date:''' June 10, 2016 at midnight.

''' [https://canvas.uw.edu/courses/1039305/assignments/3293283 Hand in your presentation here] '''

''' [https://canvas.uw.edu/courses/1039305/assignments/3293284 Hand in your final paper here] '''

For your final project, I expect you to build on the first two assignments to describe what they have done and what you have found. I'll expect every student to give both:

# A short presentation to the class (10 minutes)
# A final report that is not more than 4500 words (~18 pages)

I expect that your reports will include text from the first two assignments and reflect comprehensive documentation of your project. Each project should include: (a) the description of the question you have identified and information necessary to frame your question, (b) a description of the how you collected your data, (c) the results, (d) a description of the scope or limitations of your conclusion.

A successful project will tell a compelling story and will engage with, and improve upon, the course material to teach an audience that includes me, your classmates, and Comm Lead students taking this class in future years how to take advantage of community data science more effectively. The very best papers 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.

==== Paper and Code ====

Your final project should include detailed information on:

* The problem or area you have identified and enough background to understand the rest of your work and its importance or relevance.
* Your research question(s) and/or hypotheses.
* The methods, data, and approach that you used to collect the data plus information on why you think this was appropriate way to approach your question(s).
* The results and findings including numbers, tables, graphics, and figures.
* A discussion of limitations for your work and how you might improve them.

If you want inspiration for how people use data science to communicate this kinds of findings broadly and effectively, take a look at great sources of data journalism including [http://fivethirtyeight.com/ Five Thirty Eight] or [http://www.nytimes.com/upshot/ The Upshot at the New York Times]. Both of these publish a large amount of excellent examples of data analysis aimed at broader non-technical audiences like the ones you'll be communicating with and quite a bit of their work is actually done using Python and web APIs!  A simple Five Thirty Eight story will include a clear question, a brief overview of the data sources and method, a figure or two plus several paragraphs walking through the results, followed by a nice conclusion. I'm asking you to try to produce something roughly like this.

Keep in mind that most stories on Five Thirty Eight are under 1000 words and I'm giving up to 4,5000 words to show me what you've learned. As a result, you should do ''more'' than FiveThirtyEight does in a single story. You can ask and answer more questions, you can provide more background, context, and justification, you can provide more details on your methods and data sources, you can show us more graphs, you can discuss the implications of your findings more. Use the space I've given you to show off what you've done and what you've learned!

Finally, you should also share with me the full Python source code you used to collect the data as well as the dataset itself. Your code along will not form a large portion of your final grade. Rather, I will focus on the degree to which you have been successful at answering the ''substantive'' questions you have identified.

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?"

==== Presentation ====

Your presentation should provide me with a very clear idea of what to expect in your final paper. 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. I'm going to give you all at least a paragraph of feedback after your talk. This will be an opportunity for me to see a preview of your paper and give you a sense for what I think you can improve. It's to your advantage to both give a compelling talk and to give me a sense for your project.

;Timing: All presentations will need to be '''a maximum of 7 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. See link at top of this section.

=== 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 extremely 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 that we'll define as a group at the end of the previous class. 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.

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

=== Weekly Coding Challenges ===

Each week I will give you all a set of weekly coding challenges before the end of class that will involve changing or adding to code that I've given you as part of the projects in the final parts of class to solve new problems. These coding challenges '''will not be turned in''' and '''will not be graded'''.

I will share my solutions answers to each of the coding challenges by Wednesday morning of class. As you will see over the course of the quarter, 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!

Please do not share answers to challenges before midnight on Sunday so that everybody has a chance to work through answers on their own. After midnight on Sunday, you are all welcome to share your solutions and/or to discuss different approaches.  We will discuss the coding challenges for a short period of time at the beginning of each class.

== Schedule ==

=== Week 1: March 30 ===

'''Readings:'''

* Python for Informatics: [http://www.pythonlearn.com/html-009/book001.html Preface] and [http://www.pythonlearn.com/html-009/book002.html Chapter 1  Why should you learn to write programs?]

'''Class Schedule:'''

* Quick introductions — Be ready to introduce yourself and describe your interest and goals in the class.
* Class overview and expectations — We'll walk through this syllabus.
* [[Community_Data_Science_Course_%28Spring_2016%29/Day_1_Exercise|Day 1 Exercise]] — You'll install software including the Python programming language and run through a series of exercises.
* [[Community_Data_Science_Course_%28Spring_2016%29/Day_1_Tutorial|Day 1 Tutorial]] — You'll work through a self-guided tutorial introducing you to some basic concepts. When you're done, you'll meet with a member of the teaching team and we'll check you off.

'''By the end of class you will:'''

* Have a working python environment on your personal laptop.
* Have written your first program in the python language.

[http://goo.gl/forms/KO9Kyc9nqN Poll]

=== Week 2: April 6 ===

'''Assignment Due (nothing to turn in):''' 

* Finish setup, tutorial and code academy in the [[Community Data Science Course (Spring 2016)/Day 1 Exercise|week 01 exercises]].

'''Readings:'''

* Python for Informatics: [http://www.pythonlearn.com/html-009/book003.html Chapter 2  Variables, expressions and statements] and [http://www.pythonlearn.com/html-009/book004.html Chapter 3  Conditional execution]

'''Class Schedule:'''

* [[Community Data Science Course (Spring 2016)/Day 2 Lecture|Day 2 Lecture]] — Interactive class lecture including a review of material from last week and new material including loops, lists, and modules.
* Project time — We'll begin working on the [[wordplay]] independent projects independently or in small groups with assistance from the teaching team.

'''Resources:'''

* [[Community Data Science Course (Spring 2016)/Day 2 Plan|Day 2 Plan]]
* [[Community Data Science Course (Spring 2016)/Day 2 Coding Challenges|Day 2 Coding Challenges]]

[http://goo.gl/forms/xcwx6mDDZV Feedback Poll]

=== Week 3: April 13 ===

'''Assignment Due:'''

* [[#Final_Project_Ideas|Final Project Ideas]] [[https://canvas.uw.edu/courses/1039305/assignments/3252050 Turn in on Canvas]]
* Code solving challenges in [[wordplay]] project. This will ''not'' be graded, but I want to get a sense for where everyone is at. [[https://canvas.uw.edu/courses/1039305/assignments/3252042 Turn in on Canvas]]

'''Class Schedule:'''

* Review and Lecture — We'll walk through answers to the assignments for last week as a group.
* Project time — We'll begin working on a series of project based on the [[Baby_names]] project.

'''Resources:'''

* [[Python_data_types_cheat_sheet]] A cheat sheet with everything we've covered in class so far including today.
* [[Community Data Science Course (Spring 2016)/Day 3 Plan|Day 3 Plan]]
* [[Community Data Science Course (Spring 2016)/Day 3 Coding Challenges|Day 3 Coding Challenges]]

=== Week 4: April 20 ===


'''Assignment Due (nothing to turn in):''' 

'''Class Schedule:'''

* Data Viz: let's walk through an example.
* Review and Lecture — We'll walk through answers to the assignments for last week as a group. Then we'll introduce APIs.
* Project time — We'll begin working on a series of projects based on Wikipedia's API.

'''Resources:'''

* [[Community Data Science Course (Spring 2016)/Day 4 Plan|Day 4 Plan]]
* [[Community_Data_Science_Course_(Spring_2016)/Day_4_Lecture|Day 4 Lecture]]
* [[Community Data Science Course (Spring 2016)/Day 4 Coding Challenges|Day 4 Coding Challenges]]
* [[Python_data_types_cheat_sheet]] A cheat sheet with everything we've covered in class so far.



=== Week 5: April 27 ===


'''Assignment Due (nothing to turn in):''' 

'''Class Schedule:'''

* Review and Lecture — We'll walk through answers to the assignments for last week as a group. Then we'll introduce APIs.
* Project time — We'll begin working on a series of projects based on Wikipedia's API.

'''Resources:'''

* [[Community Data Science Course (Spring 2016)/Day 5 Plan|Day 5 Plan]]
* [[Community_Data_Science_Course_(Spring_2016)/Day_5_Lecture|Day 5 Lecture]]
* [[Community Data Science Course (Spring 2016)/Day 5 Coding Challenges|Day 5 Coding Challenges]]
* [[Python_data_types_cheat_sheet]] A cheat sheet with everything we've covered in class so far.

=== Week 6: May 4 ===

'''IMPORTANT: Class will be starting at 6:30 not 6:00 for May 4.'''

'''Assignment Due:''' Final project proposal. 
[[https://canvas.uw.edu/courses/1039305/assignments/3270775 Turn in on Canvas]]

''' Class Schedule:'''

* Review and Lecture - We'll review all of the material from last week. No new material this week: I want to make sure you get caught up to date on working APIs in general.
* Project time - Two options. 
** Option 1: Continue working on the Wikipedia questions. I've added a few new questions.
** Option 2: Start working on your final project with the help of a mentor. Use this time to cover questions like data access. If you can already download your data, great! Start thinking about analysis questions and visualizations. 

* [[Community Data Science Course (Spring 2016)/Day 6 Plan|Day 6 Plan]]
* [[Community_Data_Science_Course_(Spring_2016)/Day_6_Coding_Challenges|Day 6 Challenge]]
* [[Python_data_types_cheat_sheet]] A cheat sheet with everything we've covered in class so far.

=== Week 7: May 11 ===

'''Assignment Due:'''

* Finish the [[Twitter authentication setup]] to request keys necessary to begin using the Twitter API.

''' Class Schedule:'''

* Review and Lecture - we'll review the exercise from last week.
** Samples
* One new thing to learn: opening and reading files.
* Lecture - introduce the [https://dev.twitter.com/overview/documentation|Twitter API]
** Sample code is [[Community_Data_Science_Course_(Spring_2016)/Day_6_Lecture|here]]
* Project time - Three options.
** Option 1: If you are working on Twitter, I would encourage you to work on the [[Community_Data_Science_Workshops_(Spring_2016)/Day_2_Projects/Twitter|getting Twitter data]] exercises.
** Option 2: If you are not working on Twitter, I would encourage you to work on the [[Community_Data_Science_Workshops_%28Spring_2016%29/Day_3_Projects/Twitter|Using Twitter Data]] exercises.
** Option 3: Take this time to get help with your projects (and do one of the other options this week).

''' Resources '''
* Twitter: [https://mako.cc/teaching/2015/cdsw-autumn/twitter-api-cdsw.zip]

=== Week 8: May 18 ===

''' Class Schedule:'''

* Talk about final presentations.
* Review last week's Twitter assignments (30 mins)
* Lecture (without code!) about what makes a good metric.
* Project time. Take this time to get help with and/or work on your projects.

== Administrative Notes ==

=== Attendance ===

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 ===

Because this is an evening degree program and I understand you have busy schedules that keep us away from campus during the day, I will not hold regular office hours. In general, I will be available to meet before class. Please contact me on email to arrange a meeting then or at another time.

=== 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.

=== Comm Lead Electronic Mail Standards of Conduct ===
 
Email communications (and all communications generally) among Comm Lead community members should seek to respect the rights and privileges of all members of the academic community. This includes not interfering with university functions or endangering the health, welfare, or safety of other persons. With this in mind, in addition to the University of Washington's Student Conduct Code, Comm Lead establishes the following standards of conduct in respect to electronic communications among students and faculty:
 
:If, as a student, you have a question about course content or procedures, please use the online discussion board designed for this purpose. If you have specific questions about your performance, contact me directly.
 
:* I strive to respond to Email communications within 48 hours. If you do not hear from me, please come to my office hours, call me, or send me a reminder Email.
:* Email communications should be limited to occasional messages necessary to the specific educational experience at hand.
:* Email communications should not include any CC-ing of anyone not directly involved in the specific educational experience at hand.
:* Email communications should not include any blind-CC-ing to third parties, regardless of the third party’s relevance to the matter at hand.

=== Grades ===

Grades in this class are based on a rating scale.

Rating-scale grades are based on the faculty member's assessment of each assignment as opposed to a calculation from earned and possible points. The broad criteria for the ratings are given below. The ratings for some assignments may be multiplied by a constant (e.g. 2 or 3) so as to count more toward the final grade. The final grade is calculated as the average of all ratings.
 
;4.0 - 3.9: Excellent and exceptional work for a graduate student. Work at this level is extraordinarily thorough, well reasoned, methodologically sophisticated, and well written. Work is of good professional quality, shows an incisive understanding of digital media-related issues and demonstrates clear recognition of appropriate analytical approaches to digital media challenges and opportunities. ''Clients who received a deliverable of this quality would likely develop loyalty toward the vendor to the exclusion of other vendors.''
 
;3.8 - 3.7: Strong work for a graduate student. Work at this level shows some signs of creativity, is thorough and well-reasoned, indicates strong understanding of appropriate methodological or analytical approaches, and demonstrates clear recognition and good understanding of salient digital media-related challenges and opportunities. ''Clients who received a deliverable of this quality would likely recommend this vendor to others and consider a longer-term engagement.''
 
;3.6 - 3.5: Competent and sound work for a graduate student; well reasoned and thorough, methodologically sound, but not especially creative or insightful or technically sophisticated; shows adequate understanding of digital media-related challenges and opportunities, although that understanding may be somewhat incomplete. This is the graduate student grade that indicates neither unusual strength nor exceptional weakness. ''Clients who received a deliverable of this quality would likely agree to repeat business with this vendor.''
 
;3.3 - 3.4: Adequate work for a graduate student even though some weaknesses are evident. Moderately thorough and well reasoned, but some indication that understanding of the important issues is less than complete and perhaps inadequate in other respects as well. Methodological or analytical approaches used are generally adequate but have one or more weaknesses or limitations. ''Clients who received a deliverable of this quality would likely entertain competitor vendors.''
 
;3.0 - 3.2: Fair work for a graduate student; meets the minimal expectations for a graduate student in the course; understanding of salient issues is incomplete, methodological or analytical work performed in the course is minimally adequate. Overall performance, if consistent in graduate courses, would be in jeopardy of sustaining graduate status in "good standing." ''Clients who received a deliverable of this quality would likely pay the vendor in full but not seek further engagement.''
 
;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.''

=== Academic Misconduct ===
 
Comm Lead is committed to upholding the academic standards of the University of Washington’s Student Conduct Code. If I suspect a student violation of that code, I will first  engage in a conversation with that student about my concerns.
 
If we cannot successfully resolve a suspected case of academic misconduct through our conversations, I will refer the situation to the Anita Crofts, Comm Lead Associate Director of Academic Affairs. The Comm Lead Associate Director of Academic Affairs, in consultation with the Comm Lead Director, can then work with the COM Chair to seek further input and if necessary, move the case up to the Dean.
 
While evidence of academic misconduct may result in a lower grade, Comm Lead faculty (indeed, all UW faculty) may '''not''' unilaterally lower a grade without taking the necessary steps outlined above.
 
In closing, Comm Lead  students are expected to:

* Write coherently and clearly.
* Complete assignments on time and as directed.
* Not miss more than two classes a quarter, unless due to extreme circumstances.
* Engage as much as possible with colleagues and the instructor.
* Stay current with the latest developments in the field of communications and digital media.

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