Editing Community Data Science Course (Spring 2023)

:'''Community Data Science: Programming, Web Data Collection, and Data Science'''
:'''COM597 A / COMMLD 570 B''' — Offered jointly between the University of Washington [https://com.uw.edu/graduate/ma-phd/ma-phd-overview/ Department of Communication MA/Program] / and the [https://commlead.uw.edu/ Communication Leadership ]
:'''Location:''' [https://uw.edu/maps/?cmu Communications Building (CMU)] Room 104
:'''Instructors:''' [https://mako.cc/academic/ Benjamin Mako Hill] / [mailto:makohill@uw.edu makohill@uw.edu] and [https://kayleachampion.com/ Kaylea Champion] / she / [mailto:kaylea@uw.edu kaylea@uw.edu] (TA: you can call me Kaylea)
:'''Course Website''': We will use Canvas for [https://canvas.uw.edu/courses/1633288/assignments assignments] and [https://canvas.uw.edu/courses/1633288/announcements announcements]. Everything else will be linked on this page.
:'''Course Playlist''': [https://open.spotify.com/playlist/4iM6wz4GYRX7O2S7Z2oyXj?si=638c49b13277429e&pt=3f46c0ca5049a1ced44ae09173e28d7b Feel free to add what's moving you lately] (Link will only work if you're logged in to Spotify :/)
:Course Catalog Description (from '''Communication Leadership):'''

::''This course will introduce basic programming and data science tools to give students the skills to use data to read, critique, and produce stories and insights. The class will cover the basics of the Python programming language, acquiring and processing public data, and 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. 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 operate in a data-driven environment.

In particular, the class will cover the basics of the Python programming language, an introduction to web APIs, and will teach basic tools and techniques for data analysis and visualization. In order to efficiently cover an end to end data analysis project, we will focus on a series of publicly available data sets. Time will also be reserved to cover data access for several popular social media platforms.

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. We will focus on effectively answering questions from public data sets by writing your own software and by managing and communicating more effectively with programmers.


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 a publicly available data source.
* Read web API documentation and write Python software to parse and understand a new and unfamiliar web API.
* Use both Python-based tools as well as other 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 undeclared 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 "{{tbd}}." Although the core expectations for this class are fixed, the details of readings and assignments ''will'' 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 {{tbd}} 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. 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 that will be emailed to everybody in the class.
# The teaching team 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:

:'''[https://www.py4e.com/book Python for Everyone]'''. The book is available online for free and will provide a handy reference for the Python language.

I will list required chapters in the weekly notes below. In general, you should expect to spend an hour or less reading per week and 6-10 hours a week on programming tasks. Many weeks it will be more.

Much like Math or other technical courses, this course will build on itself every week. You should make every effort to cover the reading and exercise material every week in preparation for the next week.

== 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.
* If you need access to a computer, please reach out to me as soon as possible. The Department has laptops you can borrow for the course, but it's important to have that laptop in the first week.

== Staying in Touch ==

The best way to get in touch with the teaching team will either be to ask questions in the Teams server via of the public text channels. This is preferable because any questions you have can be answered in a way that is visible to others in the class. Also, it's quite possible that others in the class can you help you out!

=== Microsoft Teams ===

We'll be using Microsoft Teams for class chat. There are details in the canvas announcements!

=== Open Labs ===

We will be running three sets of open lab spaces. This is a great time to ask a question, or just show up and work on your programming challenges with others in the group. Those times will be:

* '''Tuesday 5-6pm''' virtually in Microsoft Teams with Kaylea
* '''Wednesdays 5-7pm''' in Communications (CMU) 242 with Mako 
* '''Thursday 5-6pm''' virtually in Microsoft Teams with Kaylea
* '''Friday 12:30-2pm''' in Gates Center 276 (up the atrium stairs and on the right) with Josh [Gates as in Bill Gates, NOT Mary Gates! This is the shiny new CS building.]

=== Office Hours ===

Mako will not be holding additional office hours but is generally available. You can set up a 30 minute minute meeting by adding something to [https://mako.cc/calendar/ his online calendar] which has a meeting signup link attached to it. 

You can set up time to meet with Kaylea on [https://harmonizely.com/kaylea her online calendar]!

== 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. Like many technical subjects, Data Science tends to build on earlier ideas, so I strongly suggest that you devote substantial time to assignments every single week.

=== Weekly Coding Challenges ===

:'''Due Date:''' Sundays at 11:59 p.m. on most weeks.
:'''Turning in:''' Upload your <code>.ipynb</code> files to [https://github.com/kayleachampion/spr23_CDSW the class Github repository] for the specific week

Most weeks 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.  We expect you to make substantial progress on your weekly assignment each week and we expect that many or most of you will have (mostly) working solutions or at least some code code for every single problem. These coding challenges will be turned in but will not be graded on effort, not on full correctness. They will be graded as ''complete/incomplete''.

I will share my solutions to each of the coding challenges via email. 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 on the night before class so that everybody has a chance to work through answers on their own. After midnight on Sunday, you are all welcome and encouraged to share your solutions and/or to discuss different approaches.  We will discuss the coding challenges at the beginning of each class.

Our plan is to ''randomly'' select folks each day of class and ask you to share your answer to one or more specific problems with the rest of the class. Everybody in the class will be "in the mix" for being called upon every time we select a person and we may call you more than once in a class. When you are called, we will pull up the code you wrote for your homework on the projector and ask you to walk us through and explain your choices in your work on the program challenges.

=== Final Project Idea ===

:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Due Date:''' Week 3 (April 10)
:'''Turning in:''' Turn in PDF in to [https://canvas.uw.edu/courses/1633288/assignments/8231677 the Canvas dropbox]

In this assignment, you should identify an area of interest, at least one sources of relevant data, and at least 3-4 questions that you plan to explore. We will discuss appropriate data sources for your project in the first and second week of the course. I am hoping that each of you will pick an area 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. In week 2, we will walk through successful projects from previous course offerings to give you an idea of the correct scope.

=== Final Project Proposal === 

:'''Maximum Length:''' 1500 words (~5 pages)
:'''Due Date:''' Week 8 (May 15)
:'''Turning in:''' Turn in PDF [https://canvas.uw.edu/courses/1633288/assignments/8231678 the Canvas dropbox]

This proposal should focus on two questions:

* Why are you planning to do this analysis? Make sure to introduce any background information about the topic, the community, your business, or anything else that will be required to properly contextualize your study.
* What is your plan? Describe the data sources will you collect and how they will be collected. Are there any blind spots given the data you have available? Are there any visualizations or tables that you plan to build?

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. I expect that you will have written at least some of the final code that you will use in this course. At the very least, you will have identify the documentation and the API endpoints that are required. If there are libraries that you think may help with access, note them.

If you are planning on create figures or tables, please include "dummy" versions of these that shows us what the x and y axes are. It's OK (and maybe best!) if these are simple and hand drawn!

=== Final Project ===

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 (8 minutes max)
# 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, defensible story in prose and plots and will contain source code sufficient to reproduce the results.

==== Final Presentation ====

;'''Presentation Date:''' 
* Presentations need to be shared in the Canvas discussion by '''Monday May 29 at 11:59pm'''
* Feedback on others presentations need to be posted by '''Thursday June 1 at 11:59pm'''
 
;'''Turning in:''' 
* Upload your video as a new thread in this [https://canvas.uw.edu/courses/1633288/discussion_topics/8134432 Canvas discussion]

;'''Watching presentations and feedback:'''
* We're expecting ''every'' person in the class to watch ''every'' presentation. It should take about 2 hours 30 minutes.
* Leave substantive feedback (see 5-question list below as a guide to feedback) by replying to the threads of the people in [https://teams.microsoft.com/l/message/19:a289c7b44b704149a725047dcafc814d@thread.tacv2/1684191632538?tenantId=f6b6dd5b-f02f-441a-99a0-162ac5060bd2&groupId=189ea783-b3e2-4b4e-83ce-74ebc15db6a5&parentMessageId=1684191632538&teamName=Community%20Data%20Science%20(Spring%202023)&channelName=Final%20Projects&createdTime=1684191632538&allowXTenantAccess=false your final presentation group] [requires Teams access]. We're expecting that you should spend about 15 minutes writing up meaningful feedback for these people. This should take about 90 minutes.
* Leave short feedback (spending 1 minute or so is fine) for everybody who is ''not'' in your presentation group. Saying you love their work is great, but the truest expression of love in data science is intellectual engagement with someone's hard work. Ask a question, make a connection to other ideas and work, or point out a place to dig deeper. 

;Length: All presentations will need to be '''a maximum of 8 minutes long'''. Do not exceed 8 minutes and 0 seconds and don't edit your video to speed it up if you go too long. Concisely communicating an idea in the time allotted is an important skill in its own right!

;Slides: You are encouraged to use slides for your talk. Please keep in mind that your slides are meant to be additive, not a teleprompter.

;Recording your talk: There are a range of options for recording your talk. If all else fails, you can join a Zoom room by yourself, share your screen, and record the sessions. It's been working for the class all quarter long so it'll probably work for you too!

Your presentation should provide the teaching team and your classmates 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. For instance, you can give a completely successful presentation by describing the motivation and walking through one plot in your paper. The teaching team will provide you with substantial feedback on your presentation. 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.

Here is a list of questions to think about both when providing in your feedback and when structuring your talk:

# In your own words, what research question did you hear the presenter answer? What are they trying to communicate? How well did they answer their question or achieve their goals? Did you find the answer compelling? Why or why not?
# Be critical. What problems, challenges, or limitations do you see with the way that the presenter is trying to measure things? What are the risks or threats to the validity of the work?
# How would you improve the work? What additional sort of data do you think could be helpful? What sort of additional analysis or visualization would be helpful? What additional interpretation would be helpful?
# If you saw this as a YouTube video or retweeted on Twitter, what sort of questions would you leave in the comments?
# If you were doing this project and presenting it to a supervisor, how would they respond? What changes would you want to make to address this audience a result? What if you were presenting it a colleague, collaborator or friend?

==== Final Paper (and Code!) ====

:'''Due Date:''' Thursday June 8 at 11:59 p.m.
:'''Turning in:''' Turn in a PDF of your paper to [https://canvas.uw.edu/courses/1633288/assignments/8231894 the Canvas dropbox]; Upload all your code into the [https://github.com/kayleachampion/spr23_CDSW/tree/main/final_project_code final_project_code directory in the class Github repository].

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 similar.

Keep in mind that most stories on Five Thirty Eight are under 1000 words and I'm giving up to 4,500 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 data set itself. Your code alone 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.

Visualization is critical to storytelling, so 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?"

=== 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. If there is anything I can do to help you participate in class, please let me know in the anonymous feedback.

== Grades ==

Assignments will typically be graded as either complete (full credit) or incomplete (no credit) or on the UW 4.0 grade scale. As detailed and linked elsewhere on the syllabus, I have provided [[User:Benjamin Mako Hill/Assessment|detailed rubrics on how I approach assessment]] for writing, for participation, and in general.

Assignments will accrue to your final grade in the following way:

* 10% will be class participation including attendance, participation in discussions, and group work
* 30% significant effort towards weekly programming challenges
* 5% will be the Final Project Idea
* 10% will be the Final Project Proposal
* 10% will be your Final Presentation including your slides and presentation.
* 35% will be the Final Project write up including visualizations

== Schedule ==

{{notice|This section will be modified throughout the course to introduce the week's material, assignments, and so on.}}

=== Week 1: March 27 ===

Today we'll be getting software installed and getting setup with Python.

'''Class Schedule:'''

* Introduce ourselves to each other
* Class overview and expectations — We'll walk through this syllabus
** We'll decide how we're going to stay in touch outside of class sessions.
** We'll introduce the teaching team.
** We'll discuss an example of a successful final project from a previous version of the class.
* [[/Week 1 Exercise]] — You'll install software including the Python programming language and run through a series of exercises.
* Introduce [[/Week 1 coding challenges]]

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

* Under the class plan and goals (at least as much as possible at this point!) 
* Have a working Python environment on your personal laptop.
* Have a GitHub account for turning in your coding challenges.
* Have an account on the class synchronous discussion system. 
* Have written your first program in the Python language. (!!!)
* Have learned a few basics about programming in Python including variables and lists.
* Be ready to start on Week 1 coding challenges. If all goes well, you'll start on them!

'''Programming challenges answers:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f85d134d-fc3a-4cc3-b3b5-afda01621fe7 Video from class walking through the results]
* [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week1/week1-programming_challenge_examples.ipynb Jupyter notebook with Kaylea's assembled list of examples from you along with notes]
* [https://github.com/kayleachampion/spr23_CDSW/blob/main/week1-homework/hill-week_01_coding_challenges-20230403.ipynb Jupyter notebook with Mako's answers to the programming challenges]

=== Week 2: April 3 ===

Today we'll be doing a crash course in basic programming in Python.

'''Class session:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f85d134d-fc3a-4cc3-b3b5-afda01621fe7 Video of announcements and week 1 programming challenges] [Requires Canvas Access]
* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=47a9e5d4-2998-4943-a047-afda01621fe4 Video of week 2 lecture] [Requires Canvas Access]
* [[/Week 2 lecture notes]]
* [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week2/week2-class_lecture.ipynb Week 2 lecture Jupyter notebook]

'''Resources:'''

* Chapters 2 and 3 of ''Python for Everyone'':
** [https://www.py4e.com/html3/02-variables Chapter 2, Variables]
** [https://www.py4e.com/html3/03-conditional Chapter 3, Conditionals]
* [[Python data types cheat sheet]]

'''Assignment Due:'''

* Finish and turn in your [[/Week 1 coding challenges]] (Due Sunday before class)

'''Class schedule:'''

* Announcements
* Answer any questions that folks have about the final projects
* Review answers to last week's coding challenges (~60m) [Kaylea will lead]
* ''Break''
* [[/Week 2 lecture notes]] which will briefly review stuff from last week and introduce new material including loops and lists
* Introduce the [[/Week 2 coding challenges]] and the three worked examples 

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

* Have a better understanding of the expectations for your final project,
* Start working on your idea for your final project choice (due on Monday! Ask for early feedback via teams!)
* Feel (more?) comfortable with the week 1 material
* Have written your first program with loops and lists

=== Week 3: April 10 ===

Today we'll be doing introducing some additional programming concepts in Python including aggregating and counting with dictionaries and some first steps toward writing data to files.

'''Class session:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f855f94e-a0e7-4483-ad73-afe1011f745d Video of announcements and week 2 programming challenges] [Requires Canvas Access]
* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=da51ee4c-59b7-4925-9f64-afe1011f7452 Video of week 2 lecture] [Requires Canvas Access]
* [[/Week 3 lecture notes]]
* [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week3/week3-class_lecture.ipynb Week 3 lecture Jupyter notebook]

'''Resources:'''

* Chapter 5, 6, and 8 of ''Python for Everyone'':
** [https://www.py4e.com/html3/05-iterations Chapter 5, Iterations]: Feel free to skip down to "Definite loops using for" or just skim the first part since we'll introduce it next week
** [https://www.py4e.com/html3/06-strings Chapter 6, Strings] 
** [https://www.py4e.com/html3/08-lists Chapter 8, Lists]: You can skip the part on "Parsing lines"
* [[Python loops cheat sheet]]

'''Assignment Due:'''

* [[#Final Project Idea|Final project idea]] (Due at 5pm on Monday, April 10)
* Finish and turn in your [[/Week 2 coding challenges]] (Due Sunday before class)

'''Class schedule:'''

* Some quick announcements
* Go over last week's assignments
* [[/Week 3 lecture notes]] introducing new material on dictionaries and aggregations plus average, more loop control, and writing data to files
* ''A Break''
* Introduce the [[/Week 3 coding challenges]] based on the [[Baby names]] project.
* Project time—We'll work through projects

<!--'''Resources:'''

* [[Python data types cheat sheet]] A cheat sheet with everything we've covered in class so far including today.

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

-->

=== Week 4: April 17 ===

Today we'll be learning about the basic of web APIs and JSON.

'''Class session:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=49d798d0-61b0-4998-b8db-afe8013c5ca8 Video of announcements and week 3 programming challenges] [Requires Canvas Access]
* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=8aa88ce5-5de7-4b49-8920-afe8013c5cb9 Video of week 4 lecture part 1] [Requires Canvas Access]
* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=001c2a6d-71ea-478d-9ce6-afe801795f2c Video of week 4 lecture part 2] [Requires Canvas Access] (rerecorded material from the last 30m of class)
* [[/Week 4 lecture notes]]
* [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week4/week4_lecture_part_1_bored_api.ipynb Week 4 lecture Jupyter notebook part 1] covering the [http://www.boredapi.com/ Bored API]
* [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week4/week4_lecture_part_2_nominatim.ipynb Week 4 lecture Jupyter notebook part 2] covering the [https://nominatim.openstreetmap.org/ OpenStreetMap Nominatim API] 

'''Assignment Due:'''

* Finish and turn in your [[/Week 3 coding challenges]] (Due Sunday before class)

'''Resources:'''

* Read chapters 7 and 9 of ''Python for Everyone'':
** [https://www.py4e.com/html3/07-files Chapter 7, Files] — we'll only be doing the section on "Writing files" (NB: you'll notice that the book uses <code>.write()</code> instead of <code>print()</code>)
** [https://www.py4e.com/html3/09-dictionaries Chapter 9, Dictionaries] — feel free to stop when you get to the section on "Advanced text parsing"

'''Class schedule:'''

* Go over the [[/Week 3 coding challenges]]
* [[/Week 4 lecture notes]] introducing new material on APIs and JSON
* Introduce the [[/Week 4 coding challenges]]  <!-- [[Community Data Science Course (Spring 2019)/Day 5 Coding Challenges|Day 5 Coding Challenges]] -->
* Project time—We'll work through projects

=== Week 5: April 24 ===

Today we'll be using Python to read and write files from disk and walk through an API project from start to finish.

'''Class session:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=8dff4630-e718-489c-a0c3-afef01525285 Video of announcements and week 4 programming challenges] [Requires Canvas Access]
* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=8133566f-217d-4e45-b3c9-afef0152528b Video of week 5 lecture]
* [[/Week 5 lecture notes]]
* Notebooks from the lecture including:
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week5/week_5_lecture_part_1-data_collection.ipynb Week 5 lecture notebook part 1] - Data Collection
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week5/week_5_lecture_part_2-data_processing.ipynb Week 5 lecture notebook part 2] - Data Processing
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week5/week-5-lecture_pre-baked_workthrough-20230424.ipynb Week 5 lecture notebook (prebaked)] — A combination of the ideas from both notebooks above with the version of the code that I wrote as notes for myself before the class. 

'''Assignment Due:'''

* Finish and turn in your [[/Week 4 coding challenges]] (Due Sunday before class)

'''Resources:'''

* Read selections of chapter 13 from ''Python for Everyone'':
** [https://www.py4e.com/html3/13-web Chapter 13, Python and Web Services] — sections on "JavaScript Object Notation - JSON", "Parsing JSON", and "Application Programming Interfaces"

'''Class schedule:'''

* Go over the [[/Week 4 coding challenges]]
* [[/Week 5 lecture notes]] introducing some new concepts and walking through a project from start to finish
* Introduce the [[/Week 5 coding challenges]]
* Project time—We'll work through projects

=== Week 6: May 1  ===

Today we'll be putting everything together and walking through a project that builds a dataset from the Wikipedia API from start-to-finish, with a focus on cleaning and getting data into tools like Excel or Google Sheets, and introducing a set of tricks along the way.

'''Class session:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=cc521a93-d6ea-4093-aefe-aff6016dde91 Video of announcements and week 5 programming challenges] [Requires Canvas Access]
* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=cc2034fa-eeb3-4b07-abbc-aff8007f170f Video of week 6 lecture]
* [[/Week 6 lecture notes]]
* Notebooks from the lecture including:
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week6/01-collect_rock_bands_json.ipynb Week 6 lecture notebook Wikipedia part 1] which handles data collection
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week6/02-analyze_rock_band_data.ipynb Week 6 lecture notebook Wikipedia part 2] which handles data analysis
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week6/yelp_business_search-module.ipynb Week 6 lecture notebook Yelp (module)] which uses the [https://github.com/lanl/yelpapi yelpapi] Python module
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week6/yelp_business_search-module.ipynb Week 6 lecture notebook Yelp (direct)] which uses API directly and is only here so you can see how this would work without a module so you get a sense for what the module is doing

'''Assignment Due:'''

* Finish and turn in your [[/Week 5 coding challenges]] (Due Sunday before class)

'''Class schedule:'''

* Let's discuss remaining schedule
* Go over the [[/Week 5 coding challenges]]
* [[/Week 6 lecture notes]]
* Introduce the [[/Week 6 coding challenges]] 
* Project time—We'll work through projects
<!--

* We will be discussing this data set: https://data.seattle.gov/Transportation/Collisions/vac5-r8kk
* Introduce and start working on [[/Day 7 coding challenges]]
[[Community Data Science Course (Spring 2019)/Day 8 notes|Day 8 notes]]
* Discuss data downloading and cleaning. Refer to [[/Day 7 notes]]
* [[/Day 8 notes]]
-->

=== Week 7: May 8  ===

Today we'll (finally!) focus on using a module called <code>pandas</code> that provides spreadsheet like functionality within Python.

'''Class session:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=8078bce8-61bf-4b3d-8bf5-affd0130c1a7 Video of announcements and week 6 programming challenges] [Requires Canvas Access]
* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a815f1ce-ded8-4972-9bb0-affd0130c1a6 Video of week 7 lecture on pandas] [Requires Canvas Access]
* [[/Week 7 lecture notes]]
* Notebooks from the lecture including:
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week7/week_7_lecture_as_delivered-2023-05-08.ipynb Week 7 lecture notebook] which is the notebook I created in class
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week7/week_7_lecture_prework-2023-05-08.ipynb Week 7 lecture notebook (prework)] which is most of the same content but it is the version I created before class
** [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week7/02-analyze_rock_band_data-pandasify.ipynb Wikipedia revisions pandas-ified] which is a modification of the <code>02-analyze_rock_band_data-pandasify.ipynb</code> notebook I created last week but which is modified to output pandas data.

'''Assignment Due:'''

* Finish and turn in your [[/Week 6 coding challenges]] (Due Sunday before class)

'''Class schedule:'''

* Go over the [[/Week 6 coding challenges]]
* [[/Week 7 lecture notes]] introducing new material on new APIs
* Introduce the [[/Week 7 coding challenges]]
* Project time—We'll work through projects

=== Week 8: May 15 ===

We'll be transitioning into a focus on the final projects and trying to build time into class to make real progress on the programming and data collection part of your final projects.


'''Class session:'''

* [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=bb9b5451-6d22-4e70-ac30-b00500463da4 Video of announcements and week 7 programming challenges] [Requires Canvas Access]
* <strike>[https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=322d5481-254f-4df4-929d-b00500463da3 Video of the second part of the class with final presentations overview]</strike> [Appears to be Basically broken; Requires Canvas Access]

'''Resources:'''

* Readings:
** [https://pandas.pydata.org/docs/user_guide/10min.html 10 minutes to pandas] (from the pandas user guide)

'''Assignment Due:'''

* [[#Final Project Proposal]]
* Finish and turn in your [[/Week 7 coding challenges]] (Due Sunday before class)

'''Class schedule:'''

* Let's discuss remaining schedule
* Go over the [[/Week 7 coding challenges]]
* We'll talk in some depth about the [[#Final Presentation]]
* Project time—We'll work through projects

=== Week 9: May 22 ===

Today we'll be talking about doing data visualization in a range of tools in Python.

'''Assignment Due:'''

* Upload your complete data collection code to the [https://github.com/kayleachampion/spr23_CDSW/tree/main/week8-homework week8-homework directory] in the Github repository and be ready to talk about it in class!

'''Class schedule:'''

* Project time—We'll check in on projects and walk through folks data collection code in two groups
* We'll do three topics in parallel: (1) ChatGPT API with Josh; (2) AI/ML for classification with Kaylea, and (3) statistics with Mako.

=== Week 10: May 29 (NO MEETING) ===

Because of Memorial Day, '''there will be no class this week.'''

In lieu of class, we will arrange to have a virtual final presentations this week. This will involve (a) posting a short video of you presentation and (b) giving feedback to some number of your classmates. Details are posted in the [[#Final Presentation]] section of this page.

== Administrative Notes ==

=== Teaching and learning after COVID ===

The COVID-19 pandemic has had a tremendous impact on all of us. We may have lost people we care about. We may be struggling to recover our own mental and physical health. For many of us life is still not back to "normal" and instead we are developing a sense of a "new normal".

Many of us have experienced elevated levels of exhaustion, stress, uncertainty and distraction. We may have needed to provide additional support to others. I have personally experienced all of these things at various times over the past two years and I expect that some of you have too. We may find that our personal energy and emotional resilience is lower than it used to be. It has been and still is a difficult time.

I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time, please contact me.  please contact the teaching team.

:<div style="font-size: 80%; font-style: italic">This text is borrowed and adapted from [[Statistics and Statistical Programming (Fall 2020)|Aaron Shaw's statistics course]].</div>

=== Your Presence in Class ===

As detailed in [[#Case discussion|section on case studies]] and in [[User:Benjamin Mako Hill/Assessment|my detailed page on assessment]], your homework in the class is to prepare for cases and case discussion is an important way that I will assess learning. Obviously, you must be in class in order to participate. <!--<strike>If you need to miss class for any reason, please fill out the [https://forms.gle/KSDJiptVwusBWw4BA course absence form] so that we know you are not coming and do not include you in our cold call list.</strike>--> In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc. 

There are many students who have eagerly requested to join the class, but there are not enough seats. I want to include as many students in the class as possible, we will automatically drop anyone who misses the first two class sessions and try to replace them with unenrolled students who do attend. This is consistent with college policy and with the course description in the catalog.
<!-- === Devices in Class ===

Electronic devices (e.g., phones, tablets, laptops) are '''not''' going to permitted in class. If you have a documented need to use a device, please contact me ahead of time to let me know. If you do get permission to use a device, I will ask you to sit in the very back of the classroom.

The goal of this policy is to help you stay focused and avoid distractions for yourself and your peers in the classroom. This is really important and turns out to be much more difficult in the presence of powerful computing devices with brightly glowing screens and fast connections to the Internet. For more on the rationale behind this policy, please read [https://medium.com/@cshirky/why-i-just-asked-my-students-to-put-their-laptops-away-7f5f7c50f368 Clay Shirky’s thoughtful discussion of his approach to this issue].

Of course, we will discuss assignments and topics that involve referring to things online. Toward that end, you might find it convenient to bring a laptop or tablet to class. If you want to look something up on your device outside of a time I clearly point out are device-allowed, please ask me. I will always point out explicitly in class if it's OK to use devices. 

'''Except during these parts of class — which  — I ask that you refrain from using your laptops, tablets, phones, and pretty much any (digital) device with a screen.'''
--> 
=== Religious Accommodations ===

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at [https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/ Religious Accommodations Policy]. Accommodations must be requested within the first two weeks of this course using the [https://registrar.washington.edu/students/religious-accommodations-request/ Religious Accommodations Request form].

=== Student Conduct ===

The University of Washington Student Conduct Code (WAC 478-121) defines prohibited academic and behavioral conduct and describes how the University holds students accountable as they pursue their academic goals. Allegations of misconduct by students may be referred to the appropriate campus office for investigation and resolution. More information can be found online at https://www.washington.edu/studentconduct/
Safety

Call SafeCampus at 206-685-7233 anytime–no matter where you work or study–to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support, while discussing short- and long-term solutions and connecting you with additional resources when requested.

=== Academic Dishonesty ===

This includes: cheating on assignments, plagiarizing (misrepresenting work by another author as your own, paraphrasing or quoting sources without acknowledging the original author, or using information from the internet without proper citation), and submitting the same or similar paper to meet the requirements of more than one course without instructor approval. Academic dishonesty in any part of this course is grounds for failure and further disciplinary action. The first incident of plagiarism will result in the student’s receiving a zero on the plagiarized assignment. The second incident of plagiarism will result in the student’s receiving a zero in the class.

=== Disability Resources ===

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to uw at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

=== Mental Health ===

Your mental health is important. If you are feeling distressed, anxious, depressed, or in any way struggling with your emotional and psychological wellness, please know that you are not alone. Graduate school can be a profoundly difficult time for many of us. 

Resources are available for you:
* UW 24/7 Help Line 1.866.775.0608
* https://wellbeing.uw.edu/topic/mental-health/
* https://www.crisistextline.org/

=== Other Student Support ===

Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live, and believes this may affect their performance in the course, is urged to contact the graduate program advisor for support. Furthermore, please notify the professors if you are comfortable in doing so. This will enable us to provide any resources that we may possess (adapted from Sara Goldrick-Rab). Please also note the student food pantry, Any Hungry Husky at the ECC.

== Credit and Notes ==

This class has been taught at UW in several forms and this syllabuses draws heavily from these previous versions. Syllabuses from earlier classes can be found online at:

* [[Community Data Science Course (Spring 2017)]] taught by [[User:Tommy Guy|Tommy Guy]]
* [[Community Data Science Course (Spring 2016)]] taught by [[Tommy Guy]]
* [[Community Data Science Course (Spring 2015)]] taught by [[Benjamin Mako Hill]]
* [[DS4UX (Spring 2016)|Community Data Science: Programming and Data Science for User Experience Research (Spring 2016)]] by Jonathan Morgan
* [[Human Centered Data Science]] which was developed by Jonathan T. Morgan, Brock Craft, and Cecilia Aragon with contributions by Os Keyes and Brandon Martin-Anderson. That class was taught three times:
** [[Human Centered Data Science (Fall 2019)]] by [http://jtmorgan.net/ Jonathan Morgan]
** [[Human Centered Data Science (Fall 2018)]] by [http://jtmorgan.net/ Jonathan Morgan]
** [[Human Centered Data Science (Fall 2017)]] by [http://jtmorgan.net/ Jonathan Morgan]

All of these classes were strongly based on the curriculum developed as part of the [[Community Data Science Workshops]] which were organized and developed by by [http://mako.cc/ Benjamin Mako Hill], Ben Lewis, [http://franceshocutt.com/ Frances Hocutt], [http://jtmorgan.net/ Jonathan Morgan], [http://mika.im Mika Matsuzaki], [https://guyrt.github.io/ Tommy Guy], and Dharma Dailey. The workshops have been designed with lots of help and inspiration from Shauna Gordon-McKeon and Asheesh Laroia of OpenHatch and lots of inspiration from the [[:openhatch:Boston Python Workshop|Boston Python Workshop]].

<!--  LocalWords:  TOC JSON MatPlotLib Elance Odesk MCCN MCDM Tweepy
 -->
<!--  LocalWords:  num py Statmodels th Scrapy TTY ing analytical
 -->