Community Data Science Course (Spring 2015)

Community Data Science: Programming and Data Science for Social Media

COM597G - Department of Communication

Instructor: Benjamin Mako Hill (University of Washington)

Course Website: We will use Canvas for announcements, turning in assignments, and 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. 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.

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 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 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.

Overview and Learning Objectives

Schedule

Week 1: March 30

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.
Installation and setup — You'll install software including the Python programming language and run through a series of exercises.
Self-guided 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.

Week 2: April 6

Assignment Due (Sunday at 11:59): Final Project Ideas

Readings:

To be determined.

Class Schedule:

Lecture — Interactive class lecture including a review of material from last week and new material including dictionaries, loops, functions, and modules.
Project time — We'll begin working on the Baby names independent projects independently or in small groups with assistance from the teaching team.

Week 3: April 13

Assignment Due (Sunday at 11:59): Code solving challenges in Baby names project.

Readings:

To be determined.

Class Schedule:

Review — We'll walk through answers to the assignments for last week as a group.
Lecture — Interactive class lecture including background into web APIs; requesting web pages with requests, JSON, and writing to files.
Project time — We'll begin working on a series of projects using the Wikipedia API.

Week 4: April 20

Assignment Due (Sunday at 11:59): Code solving challenges in in the Wikipedi API project from last week.

Readings:

To be determined.

Class Schedule:

Review — We'll walk through answers to the assignments for last week as a group.
Lecture — Interactive class lecture covering while loops, user-defined functions, debugging, filesystem output, and putting things together into a "real" program.
Project time — We'll begin modifying the program we walk through in class to adapt it toward our needs and we'll pick out ideas for next steps and challenges for the coming week..

Week 5: April 27

Assignment Due (Sunday at 11:59):

Code solving challenges in created at the end of class in Week 20.
Finish the Twitter authentication instructions to request keys necessary to begin using the Twitter API.
Project proposal

Readings:

To be determined.
Browse the Tweepy API Documentation.

Class Schedule:

Review — We'll walk through answers to the assignments for last week as a group.
Lecture — Interactive class lecture covering code abstraction, Python objects and classes and using Tweepy to collect data from Twitter.
Project time — Twitter API challenages.

Week 6: May 4

Assignment Due (Sunday at 11:59):

Code solving challenges in created at the end of previous class.

Readings:

To be determined.

Class Schedule:

Review — We'll walk through answers to the assignments for last week as a group.
Lecture — Interactive class lecture counting and powerful "group by" functionality using dictionaries and exporting and simple graphing of procssed data using Google Docs , LibreOffice, Microsoft Excel, etc.
Project time — Graphing and work on challenges that use either the Twitter and/or Wikipedia data that we've collected in the two previous sessions.

Week 7: May 11

Code solving challenges in created at the end of previous class.

Readings:

Documentation on MatPlotLib

Class Schedule:

Review — We'll walk through answers to the assignments for last week as a group.
Lecture — Interactive class on using Python to creating visualization using MatPlotLib. Graphing and work on challenges on data on gender and Wikipedia.
Project time — Project time will be devoted to Q&A focused on individual final projects.

Week 8: May 18

Readings:

To be determined.

Class Schedule:

Review — We'll walk through answers to the assignments for last week as a group.
Lecture — Interaction lecture on num.py, pandas, doing basic statistical tests using Statmodels.
Project time — Project time will be devoted to Q&A focused on individual final projects.

Week 9: May 25

Note: May 25th is Memorial day and is a University Holiday. Because UW policy requires that we meet 10 times, we will meeting as scheduled.

Readings:

If you are not very comfortable with reading and writin HTML already, compile this online HTML Tutorial.
Scrapy Documentation

Class Schedule:

Review — We'll walk through answers to the assignments for last week as a group.
Lecture — Interaction lecture on web scraping focusing on what scraping is, what's involved, and how to do it using the Python module Scrapy.
Project time — Project time will be devoted to Q&A focused on individual final projects.

Week 10: June 1

The full length of class will be devoted to final presentations of your data collection, your initial visualizations, and your results.

Administrative Notes

Attendance

As detailed in my page on assessment, 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 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

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.