Editing Community Data Science Course (Spring 2019)

:'''Community Data Science: Programming and Data Science for Communicators'''
:'''COMMLD 520 B''' - Department of Communication
:'''Location:''' CMU 302
:'''Instructor:''' [http://guyrt.github.com Richard Thomas (Tommy) Guy] 
:'''Course Website''': We will use Canvas for assignments and, and [https://canvas.uw.edu/courses/1272567/discussion_topics discussion]. Everything else will be linked on this page.
:'''Course Catalog Description:'''

This course will introduce basic programming and data science tools to give students the skills to use data to 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 publicly available data sets from the United States Government and the City of Seattle. Our goal is to enable you to gather and analyze data from any available source, but there are often subtle differences between data providers, and I would prefer that we see the full process once than get bogged down in data collection. Time will also be reserved to cover data access for popular social media platforms including Twitter.

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 like MatPlotLib as well as tools like LibreOffice, Google Docs, or Microsoft Excel to effectively graph and analyze data.
* Use web-based data to effective answer a substantively interesting question and to present this data effectively in the context of both a formal presentation and a written report.
* The ideal outcome is that students will have the working knowledge to more effectively collaborate with data professionals in their careers. They will be both more informed about the process and more likely to spot 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 "''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. 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.
# I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved.

== Readings ==
This class is going to be a studio and project based class. Although we will not rely very heavily on readings or discussing them in depth in class, I'm strongly recommending a book that will cover the material we go over in class and which will provide a reference work for you to refer to:

# '''[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 or more hours a week on programming tasks. 

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.
* 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 faster than that.

== Assignments ==

The assignments in this class are designed to give you an opportunity to try your hand at using the technical skills that we're covering in the class. There will be no exams or quizzes. There will be weekly assignments that I will ask you to hand-in but will only be graded as ''complete/incomplete''. Like many technical subjects, Data Science tends to build on earlier ideas, so I strongly suggest that you devote time to assignments every single week.

=== Final Project Idea ===
:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Due Date:''' Week 3


In this assignment, you should identify an area of interest, at least 2 sources with 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 7

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. Identify the documentation and the API endpoints where required. If there are libraries that you think may help with access, note them.

=== Final Project ===
:'''Presentation Date:''' Last week (date tbd)
:'''Paper Due Date:''' Last meeting plus 7 days.

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

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

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

A successful project will tell a compelling, defensible story in prose and plots and will contain source code sufficient to reproduce the results.

==== Paper and Code ====

Your final project should include detailed information on:

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

If you want inspiration for how people use data science to communicate this kinds of findings broadly and effectively, take a look at great sources of data journalism including [http://fivethirtyeight.com/ Five Thirty Eight] or [http://www.nytimes.com/upshot/ The Upshot at the New York Times]. Both of these publish a large amount of excellent examples of data analysis aimed at broader non-technical audiences like the ones you'll be communicating with and quite a bit of their work is actually done using Python and web APIs!  A simple Five Thirty Eight story will include a clear question, a brief overview of the data sources and method, a figure or two plus several paragraphs walking through the results, followed by a nice conclusion. I'm asking you to try to produce something roughly 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 along will not form a large portion of your final grade. Rather, I will focus on the degree to which you have been successful at answering the ''substantive'' questions you have identified.

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?"

==== 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. For instance, you can give a completely successful presentation by describing the motivation and walking through one plot in your 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 its 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. Please keep in mind that your slides are meant to be additive, not a teleprompter.

=== 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.

In general, my teaching style is more conversational than a formal lecture. I prefer that students feel they can "politely interrupt" at any time to seek clarification or make a well-informed point, and we keep the class small to encourage this.

=== Weekly Coding Challenges ===

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. These coding challenges will occasionally be turned in but will not be graded on effort not full correctness.

I will share my solutions to each of the coding challenges in the subsequent class or 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 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 for a short period of time at the beginning of each class.

== Grades ==

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

* 10% will be class participation, including attendance, participation in discussions and group work, and significant effort towards weekly assignments.
* 5% will be the Final Project Idea.
* 10% will be the Final Project Proposal.
* 50% will be the Final Project write up including visualizations.
* 25% will be your Final Presentation including your slides and presentation.


== Schedule ==

'''This section will be updated weekly'''  This section will be modified throughout the course to introduce the week's material and any hand-ins. Check back in weekly.

=== Week 1: April 3 ===

'''Readings:'''

'''Class Schedule:'''

* Class overview and expectations — We'll walk through this syllabus.
* [[Community_Data_Science_Course/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_2017)/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 me and I'll check you off.

* A few interesting links we discussed in class are [[Community_Data_Science_Course_%28Spring_2019%29/DataSources|here]]
* Hints
** For exercise 5, look at chapter 3 of the textbook. This introduces "if" statements.

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

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

=== Week 2: April 10 ===

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

Read chapters 2 and 3 of Python for Everyone:
* Chapter 2, Variables
* Chapter 3, Conditionals

Finish setup, tutorial and code academy in the week 01 exercises.

Do the Tip Calculator exercise in Code Academy. You can access this exercise after you finish the fist 14 exercises.

== Administrative Notes ==

=== Attendance ===

While we understand that as a professional program students will now and again have work or personal conflicts, it is expected that students communicate well in advance to faculty so that arrangements can be made for making up the work that was missed. It is the students' responsibility to seek out support from classmates for notes, handouts, and other information. 

=== 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 am very happy to have a skype or hangouts session where we can share our screens and discuss your questions. I'm also happy to meet in the evenings in the University District. Please contact me on email to arrange a meeting.

=== Disability Accommodations Statement ===

Your experience in this class is important to me. If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me 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 https://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. 


=== Incomplete ===

An Incomplete may be given only when the student has been in attendance and has done satisfactory work to within two weeks of the end of the quarter and has furnished proof satisfactory to the instructor that the work cannot be completed because of illness or other circumstances beyond the student’s control.
To obtain credit for the course, a student must successfully complete the work and the instructor must submit a grade. In no case may an Incomplete be converted into a passing grade after a lapse of two years or more. An incomplete received by the graduate student does not automatically convert to a grade of 0.0 but the “I” will remain as a permanent part of the student’s record.


=== 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. It is the responsibility of each UW student to know and uphold all tenets of the code, including those regarding integrity in academic conduct (http://www.washington.edu/admin/rules/policies/SGP/SPCH209.html#7). In this course, avoiding plagiarism, falsification of fieldwork data, and inappropriate collaboration are particularly important. All assignments will be reviewed for integrity. All rules regarding academic integrity extend to electronic communication and the use of online sources. All instances of suspected dishonesty or misconduct will be reported in accordance with UW policy, and may result in failure and removal from this course.If a faculty member suspects a violation of the Student Conduct Code from one of their students, the instructor will notify the student directly and file a report with the College of Arts and Sciences Student Conduct Office, as required by the College. Comm Lead faculty (indeed, all UW faculty) may neither attempt to reach a mutually agreeable resolution with a student suspected of academic misconduct NOR unilaterally lower a student’s grade based academic misconduct without taking the necessary steps outlined above.

 
In closing, Comm Lead  students are expected to:

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

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<!--  LocalWords:  num py Statmodels th Scrapy TTY ing analytical
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@@ Line 67: / Line 67: @@
 === Final Project Proposal ===
 :'''Maximum Length:''' 1500 words (~5 pages)
-:'''Due Date:''' Week 8
+:'''Due Date:''' Week 7
 This proposal should focus on two questions:
@@ Line 157: / Line 157: @@
 * Class overview and expectations — We'll walk through this syllabus.
 * [[Community_Data_Science_Course/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_2017)/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 me and I'll check you off.
+* [[Community_Data_Science_Course_(Spring_2017)/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 me and I'll check you off.
 * A few interesting links we discussed in class are [[Community_Data_Science_Course_%28Spring_2019%29/DataSources|here]]
@@ Line 178: / Line 178: @@
 Finish setup, tutorial and code academy in the week 01 exercises.
-Do the Tip Calculator exercise in Code Academy. You can access this exercise after you finish the first 14 exercises.
+Do the Tip Calculator exercise in Code Academy. You can access this exercise after you finish the fist 14 exercises.
-'''Class Schedule:'''
-* Discuss a successful final project from last year.
-* [[Community_Data_Science_Course_%28Spring_2019%29/Day_2_Lecture|Lecture notes]]
-* Review material from last week: variables, assignments, if statements
-* Introduce new material: loops and lists
-* Project time — We'll begin working on the [[wordplay]] independent projects independently or in small groups.
-Here are your [[Community_Data_Science_Course_(Spring_2019)/Day_2_Coding_Challenges|Exercises]]
-'''By the end of class you will:'''
-* Have written a program with loops and lists.
-* Have a better understanding of the expectations for your final project, and be ready to hand in your initial assignment.
-=== Week 3: April 17 ===
-'''Assignment Due:'''
-Final project idea.  Turn in on [https://canvas.uw.edu/courses/1272567/assignments/4788468 Canvas].
-Finish Wordplay examples
-Reading
-* Read chapter 4, 5 of Python for Informatics:
-** Functions (this is mostly new)
-** Iteration (this is mostly review)
-'''Course plan:'''
-* Go over last week's assignment.
-* Dictionaries and aggregations [[Community Data Science Course (Spring 2019)/Day 3 Notes|Day 3 Notes]]
-* A break! Let's really aim for 7:30 this time.
-* Discuss average, median using the wordplay data.
-* Project time — We'll begin working on a series of project based on the [http://mako.cc/teaching/2015/cdsw-autumn/babynames.zip Baby names] project.
-* [[Community Data Science Course (Spring 2019)/Day 3 Coding Challenges|Day 3 Coding Challenges]]
-'''Resources:'''
-* [[Python_data_types_cheat_sheet]] A cheat sheet with everything we've covered in class so far including today.
-=== Week 4: April 24 ===
-'''Assignment Due:'''
-Finish Baby Names examples.
-Reading
-* Read chapters 10 and 8 of Python for Informatics: Dictionaries and Files.
-'''Course Plan'''
-* Let's discuss two visualizations I found.
-* Discuss week of May 8. I'm in North Carolina.
-* Go over last week's assignment.
-* Discuss histograms in python, and build a few.
-* Project time - We'll reuse the babynames code.
-* [[Community Data Science Course (Spring 2019)/Day 4 Coding Challenges|Day 4 Coding Challenges]]
-=== Week 5: May 1 ===
-'''Assignment Due:'''
-Turn in (on canvas!) solution to this problem:
-List '''how many babies''' were born that share a name with 4, 6, 7, 8, ..., 19 other babies. Also, list how many babies share names with more than 20 other babies under the key "common".
-'''Course Plan'''
-* Let's discuss week of May 8. (Doodle poll results)
-* Go over last week's assignment and review histograms.
-* Discuss APIs and downloading data from the internet. Refer to [[Community Data Science Course (Spring 2019)/Day 5 Notes|Day 5 Notes]]
-* Spend time on [[Community Data Science Course (Spring 2019)/Day 5 Coding Challenges|Day 5 Coding Challenges]]
-=== Week 7: May 15 ===
-'''Course Plan'''
-* Let's discuss remaining schedule
-* Discuss data downloading and cleaning. Refer to [[Community Data Science Course (Sprint 2019)/Day 7 Notes|Day 7 Notes]]
-* We will be discussing this data set: https://data.seattle.gov/Transportation/Collisions/vac5-r8kk
-* Spend time on [[Community Data Science Course (Spring 2019)/Day 7 Coding Challenges|Day 7 Coding Challenges]] which are group challenges.
-=== Week 8: May 22 ===
-'''Assignment Due:'''
-Final Project Proposal. Canvas link [https://canvas.uw.edu/courses/1272567/assignments/4821879 here].
-'''Course Plan'''
-* Discuss pivot tables in Excel
-* [[Community Data Science Course (Spring 2019)/Day 8 notes|Day 8 notes]]
-=== Week 9: May 29 ===
-'''Assignment Due:'''
-Nothing! But I hope you are making good progress.
-'''Course Plan'''
-* Follow up from last week: let's discuss inference and A/B testing.
-** [https://www.exp-platform.com/Documents/2016-11BestRefutedCausalClaimsFromObservationalStudies.pdf Examples of bad observational studies]
-* Visualization dos and don'ts. We'll discuss the European Environmental Agency's [https://www.eea.europa.eu/data-and-maps/daviz/learn-more/chart-dos-and-donts list of advice for making charts]. **I will refer to this guide as a grade your final projects.**
-* Two options for remainder of class. You can work through this introductory guide to visualization in python or you can work on your final project. I'll be here to answer any questions.
-'''Optional visualization in python tutorial'''
-Self-guided visualization tutorial in python. [https://raw.githubusercontent.com/guyrt/teaching/master/2019/Com520B/VisualizationNotebook.ipynb Download here]. Save the file in a new directory in your desktop and open it with jupyter notebook
-If you are on Windows, you may run into an issue with missing path variables. [https://stackoverflow.com/questions/52821162/jupyter-notebook-failed-to-load-dll This SO post helped me solve it.]
-=== Week 10: June 5 ===
-'''Assignment Due:'''
-Final Project Presentation!
 == Administrative Notes ==