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:'''COM597G''' - Department of Communication
:'''COM597G''' - Department of Communication
:'''Instructor:''' [http://guyrt.github.com Richard Thomas (Tommy) Guy]  
:'''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 Website''': We will use Canvas for TODOAnnouncements, TODOAssignments, and TODOdiscussion. Everything else will be linked on this page.
:'''Course Catalog Description:'''
:'''Course Catalog Description:'''


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# 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.
# 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.
# Closely monitor your email or the announcements section on the [TODO canvas link]. 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 [TODO 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.  
# 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 ==
== Books ==
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:
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."
# '''[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:
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.


# 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 ==
== 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.
* 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!
* TODO who is my assistant We're working on it.? Much of the class will be project-based and William 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 ==
== Assignments ==
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:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Due Date:''' April 13
:'''Due Date:''' April 13
:'''Drop box:''' [https://canvas.uw.edu/courses/1039305/assignments/3252050 Canvas]
:'''Drop box:''' [[TODO canvas assignments Turn in on Canvas]]




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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.
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.
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 ===
=== Final Project ===
:'''Presentation Date:''' June 1, 2016
:'''Presentation Date:''' June1
:'''Paper Due Date:''' June 10, 2016 at midnight.
:'''Paper Due Date:''' TBD roughly 1 week after June 1.
 
''' [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:
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:
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==== Presentation ====
==== 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.
Your presentation should do everything that your paper does and should provide me with a very clear idea of what to expect in your final paper. 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.
;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.
;Slides: You are encouraged to use slides for your talk but I will need your slides ahead of class.


=== Participation ===
=== Participation ===
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In general, I prefer that students feel they can "politely interrupt" at any time to seek clarification or make a well-informed point.
In general, I prefer that students feel they can "politely interrupt" at any time to seek clarification or make a well-informed point.
=== Grading ===
TODOREVIEW
I have put together a very detailed page that describes [the grading rubric] we will be using in this course. Please read it carefully I will assign grades for each of following items on the UW 4.0 grade scale according to the weights below:
* Participation: 30%
* Final project idea: 5%
* Final project proposal 10%
* Final project presentation: 15%
* Final paper: 40%


=== Weekly Coding Challenges ===
=== Weekly Coding Challenges ===
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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'''.
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!
I will share my solutions answers to each of the coding challenges by Monday morning of class in a Canvas discussion threads. 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.
Please do not share answers to challenges before midnight on Tuesday so that everybody has a chance to work through answers on their own. After midnight on Tuesday, 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 ==
== Schedule ==
TODOREVIEW


=== Week 1: March 30 ===
=== Week 1: March 30 ===
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* Quick introductions — Be ready to introduce yourself and describe your interest and goals in the class.
* 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.
* 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 (Spring 2015)/Day 1 Exercise|Installation and setup]] — 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.
* [[Community Data Science Course (Spring 2015)/Day 1 Exercise|Self-guided tutorial and exercises]] — 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:'''
'''Resources:'''
 
* Have a working python environment on your personal laptop.
* Have written your first program in the python language.


[http://goo.gl/forms/KO9Kyc9nqN Poll]
* [[Community Data Science Course (Spring 2015)/Day 1 Plan|Day 1 Plan]]


=== Week 2: April 6 ===
=== Week 2: April 6 ===


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


* Finish setup, tutorial and code academy in the [[Community Data Science Course (Spring 2016)/Day 1 Exercise|week 01 exercises]].
* Finish setup, tutorial and code academy in the [[Community Data Science Course (Spring 2015)/Day 1 Exercise|week 01 exercises]].


'''Readings:'''
'''Readings:'''
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'''Class Schedule:'''
'''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.
* [[Community Data Science Course (Spring 2015)/Day 2 Lecture|Day 2 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 [[wordplay]] independent projects independently or in small groups with assistance from the teaching team.
* Project time — We'll begin working on the [[baby names]] independent projects independently or in small groups with assistance from the teaching team.


'''Resources:'''
'''Resources:'''


* [[Community Data Science Course (Spring 2016)/Day 2 Plan|Day 2 Plan]]
* [[Community Data Science Course (Spring 2015)/Day 2 Plan|Day 2 Plan]]
* [[Community Data Science Course (Spring 2016)/Day 2 Coding Challenges|Day 2 Coding Challenges]]
* [[Community Data Science Course (Spring 2015)/Day 2 Coding Challenges|Day 2 Coding Challenges]]
 
* [[Community Data Science Course (Spring 2015)/Day 2 Followup|Day 2 Followup]]
[http://goo.gl/forms/xcwx6mDDZV Feedback Poll]


=== Week 3: April 13 ===
=== Week 3: April 13 ===
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'''Assignment Due:'''
'''Assignment Due:'''


* [[#Final_Project_Ideas|Final Project Ideas]] [[https://canvas.uw.edu/courses/1039305/assignments/3252050 Turn in on Canvas]]
* [[#Final_Project_Ideas|Final Project Ideas]] [[https://canvas.uw.edu/courses/963931/assignments/2816619 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]]
* Code solving challenges in [[Baby names]] project.


'''Class Schedule:'''
'''Class Schedule:'''


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


'''Resources:'''
'''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 2015)/Day 3 Plan|Day 3 Plan]]
* [[Community Data Science Course (Spring 2016)/Day 3 Plan|Day 3 Plan]]
* [[Community Data Science Course (Spring 2015)/Day 3 Coding Challenges|Day 3 Coding Challenges]]
* [[Community Data Science Course (Spring 2016)/Day 3 Coding Challenges|Day 3 Coding Challenges]]


=== Week 4: April 20 ===
=== Week 4: April 20 ===


'''Readings:'''


'''Assignment Due (nothing to turn in):'''
* Python for Informatics: [http://www.pythonlearn.com/html-009/book013.html Chapter 12  Networked programs] and [http://www.pythonlearn.com/html-009/book014.html Chapter 13  Using Web Services] (Moved from the previous week)


'''Class Schedule:'''
'''Class Schedule''':


* Data Viz: let's walk through an example.
* Review: We'll walk through answers to the assignment and code challenges from last week as a group.
* Review and Lecture — We'll walk through answers to the assignments for last week as a group. Then we'll introduce APIs.
* Lecture — Interactive class lecture including background into web APIs; requesting web pages with <code>requests</code>, JSON, and writing to files.
* Project time — We'll begin working on a series of projects based on Wikipedia's API.
* Project time — We'll begin working on [[Community Data Science Course (Spring 2015)/Wikipedia API projects|a series of projects using the Wikipedia API]].


'''Resources:'''
'''Resources''':


* [[Community Data Science Course (Spring 2016)/Day 4 Plan|Day 4 Plan]]
* [[Community Data Science Course (Spring 2015)/Day 4 Lecture|Day 4 Lecture]]
* [[Community_Data_Science_Course_(Spring_2016)/Day_4_Lecture|Day 4 Lecture]]
* [[Community Data Science Course (Spring 2015)/Wikipedia API projects|Wikipedia API projects]]
* [[Community Data Science Course (Spring 2016)/Day 4 Coding Challenges|Day 4 Coding Challenges]]
* [[Community Data Science Course (Spring 2015)/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:''' Code solving challenges in in the Wikipedia API project from last week.


=== Week 5: April 27 ===
'''Readings:'''


 
* Python for Informatics: [http://www.pythonlearn.com/html-009/book006.html Chapter 5 Iteration] and [http://www.pythonlearn.com/html-009/book008.html Chapter 7 Files]
'''Assignment Due (nothing to turn in):'''


'''Class Schedule:'''
'''Class Schedule:'''


* Review and Lecture — We'll walk through answers to the assignments for last week as a group. Then we'll introduce APIs.
* Review — We'll walk through answers to the assignments for last week as a group.
* Project time — We'll begin working on a series of projects based on Wikipedia's API.
* Lecture — [[Community Data Science Course (Spring 2015)/Day 5 Lecture|Interactive class lecture]] covering user-defined functions, debugging, filesystem input, 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..


'''Resources:'''
'''Resources:'''


* [[Community Data Science Course (Spring 2016)/Day 5 Plan|Day 5 Plan]]
* [[Community Data Science Course (Spring 2015)/Day 5 Lecture|Day 5 Lecture]]
* [[Community_Data_Science_Course_(Spring_2016)/Day_5_Lecture|Day 5 Lecture]]
* '''Day 5 Project:''' [[Harry Potter on Wikipedia]]
* [[Community Data Science Course (Spring 2016)/Day 5 Coding Challenges|Day 5 Coding Challenges]]
* [[Community Data Science Course (Spring 2015)/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 ===
=== Week 6: May 4 ===


'''IMPORTANT: Class will be starting at 6:30 not 6:00 for May 4.'''
'''Assignment Due:'''
 
* Code solving challenges in created at the end of class the previous week.
* Finish the [[Twitter authentication setup]] to request keys necessary to begin using the Twitter API.
* [[#Final Project Proposal|Final project proposal]]
 
'''Readings:'''
 
* [[:w:Object-oriented_programming|Object-oriented programming article on Wikipedia]]
* Browse the [http://docs.tweepy.org/en/v3.2.0/ Tweepy API Documentation]


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


''' Class Schedule:'''
* Review — We'll walk through answers to the assignments for last week as a group.
* Lecture — Interactive class lecture covering Python objects and classes and using Tweepy to collect data from Twitter.
* Project time — [[Community Data Science Course (Spring 2015)/Day 6 Project|Twitter API project]]


* 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.
'''Resources:'''
* 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]]
* '''Day 6 Project:''' [[Community Data Science Course (Spring 2015)/Day 6 Project|Day 6 Project]]
* [[Community_Data_Science_Course_(Spring_2016)/Day_6_Coding_Challenges|Day 6 Challenge]]
* [[Community Data Science Course (Spring 2015)/Day 6 Coding Challenges|Day 6 Coding Challenges]]
* [[Python_data_types_cheat_sheet]] A cheat sheet with everything we've covered in class so far.
* [[Twitter words of warning]]


=== Week 7: May 11 ===
=== Week 7: May 11 ===
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'''Assignment Due:'''
'''Assignment Due:'''


* Finish the [[Twitter authentication setup]] to request keys necessary to begin using the Twitter API.
* Code solving challenges in created at the end of previous class.


''' Class Schedule:'''
'''Readings:'''


* Review and Lecture - we'll review the exercise from last week.
* Python for Informatics: [http://www.pythonlearn.com/html-009/book005.html Chapter 4 Functions] and [http://www.pythonlearn.com/html-009/book012.html Chapter 11 Regular expressions]
** 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 '''
'''Class Schedule:'''
* Twitter: [https://mako.cc/teaching/2015/cdsw-autumn/twitter-api-cdsw.zip]
 
* Review — We'll walk through answers to the assignments for last week as a group.
* Lecture — Interactive class lecture on regular expressions and pattern matching
* Project time — Working on regular expressions and independent projects


=== Week 8: May 18 ===
=== Week 8: May 18 ===


''' Class Schedule:'''
'''Class Schedule:'''
 
* Final Project — We'll through expectations for final projects.
* Lecture — We'll walk through a series of common challenges people are having on their projects.
* Project time — We'll spend the majority of class focused on creating space for students to work on their individual final projects.
 
'''Optional Readings:'''
 
* Python for Informatics: [http://www.pythonlearn.com/html-009/book016.html Chapter 15 Visualizing Data]
* Python for Data Analysis: ''Chapter 8 Plotting and Visualization''
 
=== Week 9: May 25 ===


* Talk about final presentations.
'''Class Schedule:'''
* Review last week's Twitter assignments (30 mins)
 
* Lecture (without code!) about what makes a good metric.
* Final Project — We'll through expectations for final projects.
* Project time. Take this time to get help with and/or work on your projects.
* Lecture — We'll walk through a series of common challenges people are having on their projects.
* Project time — We'll spend the majority of class focused on creating space for students to work on their individual final projects.
 
'''Optional Readings:'''
 
* Python for Data Analysis: ''Chapter 4 NumPy Basics: Arrays and Vectorized Computation'' and ''Chapter 5 Getting Started with pandas''
 
=== 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 ==
== Administrative Notes ==
TODOREVIEW


=== Attendance ===
=== 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.
As detailed in [http://mako.cc/teaching/assessment.html 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 ===
=== 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.
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 ===
=== Disability Accommodations Statement ===
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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.
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.''
;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.''
;3.8 - 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.''
;3.6 - 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.''
;3.3 - 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.''
;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.''
;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 ===
=== Academic Misconduct ===
Line 337: Line 357:
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.
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.
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:
In closing, Comm Lead  students are expected to:
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