Community Data Science Course (Spring 2015)
| **Community Data Science: Programming and Data Science for Social Media** | **COM597** - Department of Communication | **Instructor:** `Benjamin Mako Hill`__ (`University of Washington`__) | **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. 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 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 absolutely no previous programming experience.
__ http://mako.cc/academic/ __ http://www.com.washington.edu/hill/
Overview and Learning Objectives
week 1: introduction, setup. installation, variables, looping, conditionals
week 2: lecture overview, dictionaries, using functions, for loops;
programming practice, baby names projects
due: one paragraph community suggestion
week 3: lecture: web apis; getting pages with requests, json, writing to files; wikipedia api
week 4: more advanced looping; defining functions, full run through of big function
week 5: objects and classes; using tweepy: example projects using twitter
due: project proposal
week 6: freqency counting; defaultdicts; simple visualization and analysis using google docs
week 7: visualization using matplotlib: example projects using matplotlib; independent project time
week 8: num.py and pandas: example projects using pandas; basic statistical tests; independent project time
week 9: web scraping; working outside of APIs; indepdent project time
week 10: final presentations
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:
1. 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.
2. Closely monitor your email or the announcements section on the
`course website on Canvas`__. When I make changes, I will note these changes in the `syllabus changelog`__ so that you can track what has changed and I will summarize these changes in an announcement `on Canvas`__ that will be emailed to everybody in the class.
3. 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.
__ https://canvas.uw.edu/courses/951120/announcements __ changelog.html __ https://canvas.uw.edu/courses/951120/announcements
Administrative Notes
Attendance
=
As detailed in `my page on assessment`__, attendance in class is expected of all participants. If you need to miss class for any reason, please contact a member of the teaching team 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.
__ /teaching/assessment.html
Office Hours
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Because this is an evening degree program and I understand you have busy schedules that keep you away from campus during the day, I will not hold regular office hours. In general, I will be available to meet after 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 in this class are based on a (Faculty member: Choose percentage or rating) scale. (Faculty member: If you choose percentage, use the following scale & fine tune language to suit your course):
Percentage-scale grades are calculated by dividing the points earned by the points possible. This calculation may be performed for either individual assignments or the course as a whole. The grades for both are based on this progressive scale:
97% - 100% = 4 94% - 96.9% = 3.9 91% - 93.9% = 3.8 89% - 90.9% = 3.7 87% - 88.9% = 3.6 86% - 86.9% = 3.5 85% - 85.9% = 3.4 84% - 84.9% = 3.3 82.3% - 83.9% = 3.2 80.7% - 82.2% = 3.1 79% - 80.6% = 3 77.7% - 78.9% = 2.9 76.3% - 77.6% = 2.8 75% - 76.2% = 2.7
(Faculty member: If you choose ratings, use the following):
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.
