DS4UX (Spring 2016): Difference between revisions

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:'''Community Data Science: Programming and Data Science for Social Media'''
:'''Community Data Science: Programming and Data Science for User Experience Research'''
:'''COM597G''' - Department of Communication
:'''HCDE598A''' - Department of Human Centered Design & Engineering
:'''Instructor:''' [http://guyrt.github.com Richard Thomas (Tommy) Guy]  
:'''Instructor:''' [http://jtmorgan.net Jonathan T. Morgan]  
:'''Course Website''': We will use Canvas for TODOAnnouncements, TODOAssignments, and TODOdiscussion. Everything else will be linked on this page.
:'''Course Website''': We will use Canvas for [https://canvas.uw.edu/courses/1040891/announcements announcements], [https://canvas.uw.edu/courses/1040891/assignments turning in assignments], and [https://canvas.uw.edu/courses/1040891/discussion_topics discussion]. Everything else will be linked on this page.
:'''Course Catalog Description:'''
:'''Course Description:'''  


This course will introduce basic programming and data science tools to give students the skills to use data to answer questions about social media and online communities. The class will cover the basics of the Python programming language, an introduction to web APIs including APIs from Wikipedia and Twitter, and will teach basic tools and techniques for data analysis and visualization. We will focus on gaining access to data and basic data manipulation rather than complex statistical methods. As part of the class, participants will learn to write software in Python to collect and process data to produce numbers, hypothesis tests, tables, and graphical visualizations that answer questions like: Are new contributors in Wikipedia this year sticking around longer or contributing more than people who joined last year? Who are the most active or influential users of a particular Twitter hashtag? Are people who join through a Wikipedia outreach event staying involved? How do they compare to people who decide to join the project outside of the event? The class will be built around student-designed independent projects and is targeted at students with no previous programming experience.
Success in many UX related roles, particularly user research, require workers to possess an understanding of data science concepts and to have facility with the tools of data analysis. This course introduces students to widely-adopted programming and data science tools to give them the skills to use data to answer questions about the characteristics, behaviors, and needs of people who use a wide variety of products.
 
This course has students working with real data from real users. It is built around scenarios that are directly relevant to performing user research in industry, such as:
 
*identifying user segments (e.g. power users) and popular content
*manipulating very large datasets (too big for Excel!)
*performing data visualization and statistical analysis using code (not GUIs)
*implementing experimental designs such as A/B tests and funnel analysis
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<br/>


== Overview and Learning Objectives ==
== Overview and Learning Objectives ==
<div style="float:right;">__TOC__</div>
<div style="float:right;">__TOC__</div>
The goal of the course is to provide students with a basic grasp of programming and data science concepts using tools that they can reuse elsewhere. No previous programming experience is required, or even expected.  While the course is framed around user research, the use-cases we will work with are relevant to a wide variety of non-engineering roles in software development and the broader technology industry.


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 use data to answer questions about social media and online communities.
Upon completion of this course, students should be able to:
 
*Write or modify a program to collect a dataset from Wikipedia or the City of Seattle’s open data portal (Data.Seattle.gov)
In particular, the class will cover the basics of the Python programming language, an introduction to web APIs including APIs from Wikipedia and Twitter, and will teach basic tools and techniques for data analysis and visualization. As part of the class, participants will learn to write software in Python to collect data from web APIs and process that data to produce numbers, hypothesis tests, tables, and graphical visualizations that answer 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.
*Effectively read web API documentation and write Python software to parse and understand a new and unfamiliar JSON-based web API.
*Understand database schemas and use MySQL to extract user data from relational databases.
*Use web-based data to effectively answer a substantively interesting question and to present this data effectively in the context of both a formal presentation and a written report.
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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. If you want to become a professional programmers, this is probably not the right class. If you want to learn about programming so that you can more effectively answer questions about social media by writing your own software and by managing and communicating more effectively with programmers, you are in the right place.
=== Where does this course come from? ===


I will consider this class a complete success if, at the end, every student can:
This course is based on the curriculum of the [[Community_Data_Science_Workshops|Community Data Science Workshop series]] and [http://www.com.washington.edu/hill/ Professor Benjamin Mako Hill's] course ''[[Community_Data_Science_Course_(Spring_2015)|COM597G: Community Data Science: Programming and Data Science for Social Media]]'', offered first in Spring 2015, as well as in [[Community_Data_Science_Course_(Spring_2016)|Spring 2016]].


* Write or modify a program to collect a dataset from the Wikipedia and Twitter APIs.
For the first three weeks, we'll be paralleling these previous courses quite closely. Once we have hammered down some of the basics, we'll start to explore some tools, skills, and use cases that are more directly aligned with HCDE backgrounds and UX professional contexts.
* Read web API documentation and write Python software to parse and understand a new and unfamiliar JSON-based web API.
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* Use both Python-based tools like MatPlotLib as well as tools like LibreOffice, Google Docs, or Microsoft Excel to effectively graph and analyze data.
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* 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 un-declared assumptions in their colleague's work.


== Note About This Syllabus ==  
=== 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:
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.
# 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 [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.
# Closely monitor your email or the announcements section on the [https://canvas.uw.edu/courses/1040891/announcements course website on Canvas]. Because this a wiki, you will be able to track every change by clicking the ''history'' button on this page. I will also summarize these changes in an announcement [https://canvas.uw.edu/courses/1040891/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. In the past, I have made many adjustments based on this feedback.
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== Books ==
=== 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, '''[http://www.pythonlearn.com/book.php Python for Informatics: Exploring Information]''' by Charles Severance covers much of the material we cover in this course, and can serve as a reference work for review and self-study.


# '''[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."
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."


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.
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.
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=== 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.
* 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!
* Much of the class will be project-based and Ray 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!
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== Assignments ==
== 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. In addition to the four major assignments described below, there will be weekly assignments (tutorials and coding challenges) that I will ask you to complete and (sometimes) hand in, but these 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.
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.
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=== Final Project Idea ===
=== Final Project Idea ===
:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Due Date:''' April 13
:'''Due Date:''' May 2
:'''Drop box:''' [[TODO canvas assignments Turn in on Canvas]]
:'''Drop box:''' [https://canvas.uw.edu/courses/1040891/discussion_topics/3325827 Turn in on Canvas]


In this assignment, you should identify an area of interest, at least 2 datasets or APIs with relevant data, and at least 3 questions that you plan to explore. I am hoping that each of you will pick an area or domain 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.


In this assignment, you should identify an area of interest, at least 2 source domains with relevant data, and at least 3-4 questions that you plan to explore. I am hoping that each of you will pick an area or domain 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.
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.
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=== Final Project Proposal ===  
=== Final Project Proposal ===  
:'''Maximum Length:''' 1500 words (~5 pages)
:'''Maximum Length:''' 1500 words (~4 pages)
:'''Due Date:''' May 4th (at 6pm)
:'''Due Date:''' May 16th (at 6pm)


This proposal should focus on two questions:
This proposal should focus on two questions:
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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.
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=== Final Project ===
=== Final Project Presentation ===
:'''Presentation Date:''' June1
:'''Presentation Date:''' June 3
:'''Paper Due Date:''' TBD roughly 1 week after June 1.


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:
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 report. 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 report 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.


# A short presentation to the class (10 minutes)
;Timing: All presentations will need to be '''a maximum of 5 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.
# 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.
;Slides: You are encouraged to use slides for your talk but I will need your slides ahead of class.
<br/>


A successful project will tell a compelling story and will engage with, and improve upon, the course material to teach an audience that includes me, your classmates, and Comm Lead students taking this class in future years how to take advantage of community data science more effectively. The very best papers will give us all a new understanding of some aspect of course material and change the way I teach some portion of this course in the future.
=== Final Project Report ===
:'''Due Date:''' June 8


==== Paper and Code ====
For your final project report and presentation, I expect you to build on the first two assignments to describe what they have done and what you have found. Your final report should be no more than 3000 words and should include detailed information on:
 
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.
* The problem or area you have identified and enough background to understand the rest of your work and its importance or relevance.
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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 like this.
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 like this.


Keep in mind that most stories on Five Thirty Eight are under 1000 words and I'm giving up to 4,5000 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!
Keep in mind that most stories on Five Thirty Eight are under 1000 words and I'm giving up to 3000 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!
 
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 story and will engage with, and improve upon, the course material to teach an audience that includes me, your classmates, and HCDE students taking classes like this in future years how to take advantage of community data science more effectively.
 
The very best reports will give us all a new understanding of some aspect of course material and change the way I teach some portion of this course in the future.
<br/>


Finally, you should also share with me the full Python source code you used to collect the data as well as the dataset 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.
==== Code ====
Finally, you should also share with me the full Python source code you used to collect the data as well as the dataset 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.


At least 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?"
At least 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?"
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<br/>
=== Weekly Tutorials and Coding Challenges ===
For the first few weeks of the course, I will assign you some short tutuorials as homework. These are intended to help you practice and remember basic programming concepts.


==== Presentation ====
Each week I will also give you all a set of weekly coding challenges before the end of class. Most of these challenges 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.


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.
These tutorials will not be turned in or graded. I may ask your to turn in ''some'' of the coding challenges--I'll be very clear in specifying which ones. Coding challenges I ask you to turn in need to be submitted, even if you only have a partial solution, however they '''will not be graded for correctness'''. You'll receive credit for attempting the coding challenge, not for whether your answer is correct.  


;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.
I will share my solutions answers to each of the coding challenges by Monday 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!


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.


;Slides: You are encouraged to use slides for your talk but I will need your slides ahead of class.
Graded coding challenges only account for a small part of your grade, but if you repeatedly ignore them the numbers will add up. Worse: you're likely to fall behind in the course. Best practice is to always turn something in! Remember: you're not being graded on whether you answered the question correctly, only that you attempted to answer the question.
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=== 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.
<br/>
<br/>


=== Weekly Coding Challenges ===
== Schedule ==


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'''.
{{:DS4UX_(Spring_2016)/Schedule}}


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!
== Administrative Notes ==


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.
=== Attendance ===


== Schedule ==
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.
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=== Week 1: March 30 ===
=== Office Hours ===


'''Readings:'''
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 in person for brief discussions during the hour before class.


* Python for Informatics: [http://www.pythonlearn.com/html-009/book001.html Preface] and [http://www.pythonlearn.com/html-009/book002.html Chapter 1  Why should you learn to write programs?]
Remote meetings, using Skype or Google Hangout, are always an option. Please contact me or Ray Hong on email to arrange a meeting.
<br/>


'''Class Schedule:'''
=== Disability Accommodations Statement ===


* Quick introductions — Be ready to introduce yourself and describe your interest and goals in the class.
To request academic accommodations due to a disability please contact Disability Resources for Students, 448 Schmitz, 206-543-8924/V, 206-5430-8925/TTY. If you have a letter from Disability Resources for Students indicating that you have a disability that requires academic accommodations, please present the letter to me so we can discuss the accommodations that you might need for the class. I am happy to work with you to maximize your learning experience.
* Class overview and expectations — We'll walk through this syllabus.
<br/>
* Link coming — You'll install software including the Python programming language and run through a series of exercises.
* Link coming — 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:'''
=== Grades ===


* Have a working python environment on your personal laptop.
Grades for individual components for this course (participation, coding challenges, final project deliverables) will be assigned based on the following criteria. For more information on grading policy, see the [http://www.hcde.washington.edu/myhcde/grading-policies HCDE grading policy] and the [http://depts.washington.edu/grading/conduct/grading.html UW Academic Conduct policy for grading].  
* Have written your first program in the python language.


== Administrative Notes ==
To learn more about how I will evaluate your overall performance for the course, please see [https://mako.cc/teaching/assessment.html Professor Benjamin Mako Hill's assessment rubric].


=== Attendance ===
{| class="wikitable"
!Assignment
!Percentage
|-
|Participation
|30%
|-
|Required Coding Challenges
|10%
|-
|Final Project Idea
|10%
|-
|Final Project Plan
|10%
|-
|Final Project Presentation
|10%
|-
|Final Project Report
|30%
|}
<br/>


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.
==== Participation ====


=== Office Hours ===
You are not graded on whether you show up to class, or not. However, you ''are'' graded on your participation in class. Everyone misses class every once in a while. However, if you need to miss a class and you want to assure that your absence does not negatively impact your grade, follow the steps steps to demonstrate that you engaged with the material covered during that class session.


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.
'''IMPORTANT: For full participation credit on a day you are absent, you ''must'' contact the instructor or TA before 10pm the day after class via email with this information:'''
# you have reviewed any materials we covered in the class, including instructor slides, links, videos, etc. (check the wiki)
# you have attempted to perform any in-class exercises that we performed during class (if they're listed on the wiki).  
# you have turned in any assignments (graded coding challenges, project deliverables) due during the that week's class


=== Disability Accommodations Statement ===
'''In your email:''' please ask questions, what confused you, tell us how far you got on the coding challenges, and be honest about what you couldn't complete. I also highly recommend that you share your solutions or partial solutions in the email--sending me python files and/or pasting code into the body of an email is totally fine--this helps me and Ray understand your thought process and evaluate your effort and your progress.
<br/>


To request academic accommodations due to a disability please contact Disability Resources for Students, 448 Schmitz, 206-543-8924/V, 206-5430-8925/TTY. If you have a letter from Disability Resources for Students indicating that you have a disability that requires academic accommodations, please present the letter to me so we can discuss the accommodations that you might need for the class. I am happy to work with you to maximize your learning experience.
==== Coding Challenges ====


=== Comm Lead Electronic Mail Standards of Conduct ===
Only about half of the coding challenges will be graded (and I will be very clear which ones those are, so there is no confusion). Graded coding challenges are evaluated as ''complete/incomplete''. You gain a 'complete' for coding challenges by turning in the assignment (whether or not your answers are correct, or your code runs) via the submission channel I specified for that assignment.
<br/>
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 ===
==== Final project deliverables ====


Grades in this class are based on a rating scale.
Grades for all final project deliverables (idea, proposal, presentation, and report) for 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.
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 data science-related issues and demonstrates clear recognition of appropriate analytical approaches to data science 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 data science-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 data science-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.''
Line 189: Line 236:
   
   
;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.''
<br/>
<br/>


=== Academic Misconduct ===
=== Plagiarism ===
 
Comm Lead is committed to upholding the academic standards of the University of Washington’s Student Conduct Code. If I suspect a student violation of that code, I will first  engage in a conversation with that student about my concerns.
;Writing:
Please don't use other people's writing as your own. It's easy to spot plagiarism of this type, and the consequences can be serious (see link below).
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.
 
;Code:
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.
Programmers routinely copy chunks of code from other people's programs into their own programs. This in itself does not constitute plagiarism--though when in doubt, it is best to inform your instructor that you copied/adapted some part of your code from another source. I encourage you to adapt code from our in-class demonstrations, exercises, and coding challenges to your final project if you think that code will work for you. However, please do NOT copy whole scripts or substantial parts of scripts from other sources and try to pass it off as your own. Again, this is easy to spot--experienced programmers have their own 'styles' and conventions and it generally contrasts with the style of less experienced programmers. Also, if you're copying code because you don't know how to reproduce it yourself, you're probably not learning very much, and that will come back to bite you later in the course, and maybe even in your professional life!
 
In closing, Comm Lead  students are expected to:
For more information, see [http://www.hcde.washington.edu/policies/plagiarism-and-academic-conduct the UW policy on plagiarism and academic conduct].


* 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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Latest revision as of 23:33, 6 January 2020

Community Data Science: Programming and Data Science for User Experience Research
HCDE598A - Department of Human Centered Design & Engineering
Instructor: Jonathan T. Morgan
Course Website: We will use Canvas for announcements, turning in assignments, and discussion. Everything else will be linked on this page.
Course Description:

Success in many UX related roles, particularly user research, require workers to possess an understanding of data science concepts and to have facility with the tools of data analysis. This course introduces students to widely-adopted programming and data science tools to give them the skills to use data to answer questions about the characteristics, behaviors, and needs of people who use a wide variety of products.

This course has students working with real data from real users. It is built around scenarios that are directly relevant to performing user research in industry, such as:

  • identifying user segments (e.g. power users) and popular content
  • manipulating very large datasets (too big for Excel!)
  • performing data visualization and statistical analysis using code (not GUIs)
  • implementing experimental designs such as A/B tests and funnel analysis



Overview and Learning Objectives[edit]

The goal of the course is to provide students with a basic grasp of programming and data science concepts using tools that they can reuse elsewhere. No previous programming experience is required, or even expected. While the course is framed around user research, the use-cases we will work with are relevant to a wide variety of non-engineering roles in software development and the broader technology industry.

Upon completion of this course, students should be able to:

  • Write or modify a program to collect a dataset from Wikipedia or the City of Seattle’s open data portal (Data.Seattle.gov)
  • Effectively read web API documentation and write Python software to parse and understand a new and unfamiliar JSON-based web API.
  • Understand database schemas and use MySQL to extract user data from relational databases.
  • Use web-based data to effectively answer a substantively interesting question and to present this data effectively in the context of both a formal presentation and a written report.



Where does this course come from?[edit]

This course is based on the curriculum of the Community Data Science Workshop series and Professor Benjamin Mako Hill's course COM597G: Community Data Science: Programming and Data Science for Social Media, offered first in Spring 2015, as well as in Spring 2016.

For the first three weeks, we'll be paralleling these previous courses quite closely. Once we have hammered down some of the basics, we'll start to explore some tools, skills, and use cases that are more directly aligned with HCDE backgrounds and UX professional contexts.

Note About This Syllabus[edit]

You should expect this syllabus to be a dynamic document and you will notice that there are a few places marked "To Be Determined." Although the core expectations for this class are fixed, the details of readings and assignments may shift based on how the class goes. As a result, there are three important things to keep in mind:

  1. Although details on this syllabus will change, I will not change readings or assignments less than one week before they are due. If I don't fill in a "To Be Determined" one week before it's due, it is dropped. If you plan to read more than one week ahead, contact me first.
  2. Closely monitor your email or the announcements section on the course website on Canvas. Because this a wiki, you will be able to track every change by clicking the history button on this page. I will also summarize these changes in an announcement on Canvas that will be emailed to everybody in the class.
  3. I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback.


Books[edit]

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, Python for Informatics: Exploring Information by Charles Severance covers much of the material we cover in this course, and can serve as a reference work for review and self-study.

The book is available online for free but you can also buy a physical copy of the book from Amazon or get an electronic copy from the 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."

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.

General Notes[edit]

  • 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.
  • Much of the class will be project-based and Ray 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!



Assignments[edit]

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. In addition to the four major assignments described below, there will be weekly assignments (tutorials and coding challenges) that I will ask you to complete and (sometimes) hand in, but these 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[edit]

Maximum Length: 600 words (~2 pages double spaced)
Due Date: May 2
Drop box: Turn in on Canvas

In this assignment, you should identify an area of interest, at least 2 datasets or APIs with relevant data, and at least 3 questions that you plan to explore. I am hoping that each of you will pick an area or domain 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.

Final Project Proposal[edit]

Maximum Length: 1500 words (~4 pages)
Due Date: May 16th (at 6pm)

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.

Final Project Presentation[edit]

Presentation Date: June 3

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 report. 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 report 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 5 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.


Final Project Report[edit]

Due Date: June 8

For your final project report and presentation, I expect you to build on the first two assignments to describe what they have done and what you have found. Your final report should be no more than 3000 words and 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 Five Thirty Eight or 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 like this.

Keep in mind that most stories on Five Thirty Eight are under 1000 words and I'm giving up to 3000 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!

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 story and will engage with, and improve upon, the course material to teach an audience that includes me, your classmates, and HCDE students taking classes like this in future years how to take advantage of community data science more effectively.

The very best reports will give us all a new understanding of some aspect of course material and change the way I teach some portion of this course in the future.

Code[edit]

Finally, you should also share with me the full Python source code you used to collect the data as well as the dataset 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.

At least 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?"

Weekly Tutorials and Coding Challenges[edit]

For the first few weeks of the course, I will assign you some short tutuorials as homework. These are intended to help you practice and remember basic programming concepts.

Each week I will also give you all a set of weekly coding challenges before the end of class. Most of these challenges 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 tutorials will not be turned in or graded. I may ask your to turn in some of the coding challenges--I'll be very clear in specifying which ones. Coding challenges I ask you to turn in need to be submitted, even if you only have a partial solution, however they will not be graded for correctness. You'll receive credit for attempting the coding challenge, not for whether your answer is correct.

I will share my solutions answers to each of the coding challenges by Monday 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!

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.

Graded coding challenges only account for a small part of your grade, but if you repeatedly ignore them the numbers will add up. Worse: you're likely to fall behind in the course. Best practice is to always turn something in! Remember: you're not being graded on whether you answered the question correctly, only that you attempted to answer the question.

Participation[edit]

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.

In general, I prefer that students feel they can "politely interrupt" at any time to seek clarification or make a well-informed point.

Schedule[edit]

Week 1: March 28[edit]

Day 1 plan

Assignments due
  • fill out the pre-course survey
Agenda
  • Quick introductions — Be ready to introduce yourself and describe your interest and goals in the class.
  • Why Programming and Data Science for UX Research? — What this course is about
  • Class overview and expectations — We'll walk through this syllabus.
  • Group formation — We'll assemble in our peer programming groups for the first time.
  • Installation and setup — You'll install software including the Python programming language and run through a series of exercises.
  • Interactive lecture: programming concepts 1
  • Self-guided tutorial and exercises — You'll work through a self-guided tutorial to practice the basic concepts we introduced in the lecture.
Homework
Resources



Week 2: April 4[edit]

Day 2 plan

Agenda
Homework
Resources



Week 3: April 11[edit]

Day 3 plan

Agenda
Homework
Resources
  • go here



Week 4: April 18[edit]

Day 4 plan

Agenda
Homework
Resources
  • Two video lectures by Mako Hill, which covers most of the concepts from NEXT week's lecture (as well as some useful review):



Week 5: April 25[edit]

Day 5 plan

Assignments due
Agenda
Homework
Resources



Week 6: May 2[edit]

Day 6 plan

Assignments due
Agenda
  • An interactive lecture introducing the concept of user-defined functions
Homework
Resources



Week 7: May 9[edit]

Day 7 plan

Assignments due
Agenda
  • Week 6 coding challenge solutions - Jonathan will review the solutions to the week 6 coding challenges and answer questions
  • Writing your own functions - Ray will give a lecture and lead us through a series of interactive exercises on creating custom functions to make our code simpler, clearer, and more flexible.
  • Working with location data - Jonathan will introduce some simple techniques for aggregating and visualizing datasets that have a location component, using a corpus of Seattle building permit data.
Coding challenges



Week 8: May 16[edit]

Day 8 plan

Assignments due
Agenda
  • Jupyter notebooks: intro and setup
  • Some new concepts: try/except, sleep(), dateutil.parser, datetime.datetime
  • Functions II: walk through examples in Jupyter and terminal
Coding challenges
  • No coding challenges this week!
Resources
  • Data Science from Scratch, Joel Grus (O'Reilly)



Week 9: May 23[edit]

Day 9 plan

Agenda
  • We will review the requirements for the Final Presentation and Final Project assignments
  • We will review the course as a whole, and what we accomplished
  • We will go through 1-2 more examples of how to organize a program into functions
  • We will have an opportunity to review key Python concepts as a class
  • We will have plenty of time to ask questions about and work on final projects
Resources
Free (mostly) Python 3 tutorials and reference works



Week 10: June 3 (DATE CHANGE)[edit]

Please note that this class we will meet from 6pm to 9pm on Friday evening, rather than Monday evening, because of the Memorial Day holiday.

Assignments due
Agenda
  • Final project presentations
Resources
  • one



Week 11: June 6[edit]

FINALS WEEK - NO CLASS

Assignments due



Administrative Notes[edit]

Attendance[edit]

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[edit]

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 in person for brief discussions during the hour before class.

Remote meetings, using Skype or Google Hangout, are always an option. Please contact me or Ray Hong on email to arrange a meeting.

Disability Accommodations Statement[edit]

To request academic accommodations due to a disability please contact Disability Resources for Students, 448 Schmitz, 206-543-8924/V, 206-5430-8925/TTY. If you have a letter from Disability Resources for Students indicating that you have a disability that requires academic accommodations, please present the letter to me so we can discuss the accommodations that you might need for the class. I am happy to work with you to maximize your learning experience.

Grades[edit]

Grades for individual components for this course (participation, coding challenges, final project deliverables) will be assigned based on the following criteria. For more information on grading policy, see the HCDE grading policy and the UW Academic Conduct policy for grading.

To learn more about how I will evaluate your overall performance for the course, please see Professor Benjamin Mako Hill's assessment rubric.

Assignment Percentage
Participation 30%
Required Coding Challenges 10%
Final Project Idea 10%
Final Project Plan 10%
Final Project Presentation 10%
Final Project Report 30%


Participation[edit]

You are not graded on whether you show up to class, or not. However, you are graded on your participation in class. Everyone misses class every once in a while. However, if you need to miss a class and you want to assure that your absence does not negatively impact your grade, follow the steps steps to demonstrate that you engaged with the material covered during that class session.

IMPORTANT: For full participation credit on a day you are absent, you must contact the instructor or TA before 10pm the day after class via email with this information:

  1. you have reviewed any materials we covered in the class, including instructor slides, links, videos, etc. (check the wiki)
  2. you have attempted to perform any in-class exercises that we performed during class (if they're listed on the wiki).
  3. you have turned in any assignments (graded coding challenges, project deliverables) due during the that week's class

In your email: please ask questions, what confused you, tell us how far you got on the coding challenges, and be honest about what you couldn't complete. I also highly recommend that you share your solutions or partial solutions in the email--sending me python files and/or pasting code into the body of an email is totally fine--this helps me and Ray understand your thought process and evaluate your effort and your progress.

Coding Challenges[edit]

Only about half of the coding challenges will be graded (and I will be very clear which ones those are, so there is no confusion). Graded coding challenges are evaluated as complete/incomplete. You gain a 'complete' for coding challenges by turning in the assignment (whether or not your answers are correct, or your code runs) via the submission channel I specified for that assignment.

Final project deliverables[edit]

Grades for all final project deliverables (idea, proposal, presentation, and report) for 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 data science-related issues and demonstrates clear recognition of appropriate analytical approaches to data science 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 data science-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 data science-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.



Plagiarism[edit]

Writing

Please don't use other people's writing as your own. It's easy to spot plagiarism of this type, and the consequences can be serious (see link below).

Code

Programmers routinely copy chunks of code from other people's programs into their own programs. This in itself does not constitute plagiarism--though when in doubt, it is best to inform your instructor that you copied/adapted some part of your code from another source. I encourage you to adapt code from our in-class demonstrations, exercises, and coding challenges to your final project if you think that code will work for you. However, please do NOT copy whole scripts or substantial parts of scripts from other sources and try to pass it off as your own. Again, this is easy to spot--experienced programmers have their own 'styles' and conventions and it generally contrasts with the style of less experienced programmers. Also, if you're copying code because you don't know how to reproduce it yourself, you're probably not learning very much, and that will come back to bite you later in the course, and maybe even in your professional life!

For more information, see the UW policy on plagiarism and academic conduct.