Intro to Programming and Data Science (Spring 2020): Difference between revisions

From CommunityData
(Updating some links manually, copying over content I already changed.)
(Purdue required sections; starting to adjust schedule)
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* Identify multiple ways that computational approaches are being used for social science research.
* Identify multiple ways that computational approaches are being used for social science research.
* Feel comfortable taking more advanced computational methods courses or learning new techniques on your own.
* Feel comfortable taking more advanced computational methods courses or learning new techniques on your own.
== Note About This Syllabus ==
This is a brand new course and this syllabus will be a dynamic document. 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. Please let me know what is working and what can be improved.


== Required resources and texts ==
== Required resources and texts ==
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I will list required chapters in the weekly notes below. In general, you should expect to spend far more time working on programming tasks than reading. 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.
I will list required chapters in the weekly notes below. In general, you should expect to spend far more time working on programming tasks than reading. 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.


* Other readings: Throughout the year we will read and discuss examples of computational social science that I find particularly well done or interesting. If you come across additional examples that you think the class would benefit from, please suggest them to me.
* Other readings: Throughout the year we will read and discuss examples of computational social science that I find particularly well done or interesting. I will put these document on Blackboard. If you come across additional examples that you think the class would benefit from, please suggest them to me.


* Optional readings: Matthew Salganik's book 'Bit by Bit: Social Research in the Digital Age' is a wonderful introduction to computational social science. We will not be discussing it in class but I highly recommend it.
* Optional readings: Matthew Salganik's book 'Bit by Bit: Social Research in the Digital Age' is a wonderful introduction to computational social science. We will not be discussing it in class but I highly recommend it.


== General Notes ==
== Course logistics ==


* Email is the best way to be in contact with me. You can reach me at [mailto:jdfoote@purdue.edu jdfoote@purdue.edu]. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours (~9-5).
=== Note About This Syllabus ===
* I strongly encourage you to work together on programming exercises and to ask each other questions before coming to me. Often [https://en.wikipedia.org/wiki/Rubber_duck_debugging just trying to explain your code] can help you to recognize where you've gone wrong.
 
This is a brand new course and this syllabus will be a dynamic document. 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. Please let me know what is working and what can be improved.
 
=== Office hours and email ===
 
* I will hold office hours Thursday afternoons. If you come with a programming question, I will expect that you have already tried to solve it yourself in multiple ways and that you have discussed it with at least two classmates. This policy lets me have time to help more students, but it's also a useful strategy. Often [https://en.wikipedia.org/wiki/Rubber_duck_debugging just trying to explain your code] can help you to recognize where you've gone wrong.
* I am also available by email. You can reach me at [mailto:jdfoote@purdue.edu jdfoote@purdue.edu]. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours (~9-5).


== 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. There will be no exams or quizzes. There will be weekly assignments that I will ask you to hand in but which will only be graded as complete/incomplete. I will randomly sample from the assignments to make sure that people are understanding the topics and I will randomly choose students to share their responses to exercises.
The main outcome of this course will be a research project exploring a social science question using Python, and the bulk of your grade will be based on that project. I prefer that you do projects on your own but it may be possible to work as a small team (maximum 3 people). Team projects are expected to be more ambitious than individual projects. Preliminary assignments will help you to develop your idea and to get feedback from me and others.
 
The main out


=== Final Project Idea ===
There will also be weekly programming assignments that I will ask you to hand in but which will only be graded as complete/incomplete. I will randomly sample from the assignments to make sure that people are understanding the topics and I will randomly choose students to share their responses to exercises as an extra way to incentivize you to complete them.
:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Due Date:''' Week 3


=== Research project ===


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.
As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:


* '''Design and describe a plan for a study''' — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.
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.
* '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project.
* '''Report and interpret your findings''' — You will do this in both a short paper and a short presentation.
* '''Ensure that your work is replicable''' — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.


=== Final Project Proposal ===
''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, that you can use as pilot analysis that you can report in a grant or thesis proposal, and/or that fulfills a degree requirement.
:'''Maximum Length:''' 1500 words (~5 pages)
:'''Due Date:''' Week 8


This proposal should focus on two questions:
There are several intermediate milestones and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Blackboard.


* 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.
==== Project plan and dataset identification ====
* 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.
;Due date: January 28, 2020
;Maximum length: 500 words (~1-2 pages)


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.
Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:


=== Final Project ===
* An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.
:'''Presentation Date:''' Last week (date tbd)
* An identification of the dataset you will use and a description of the columns or type of data it will include. If you do not currently have access to these data, explain why and when you will.
:'''Paper Due Date:''' Last meeting plus 7 days.
* A short (several sentences?) description of how the project will fit into your career trajectory.


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:
==== Project planning document ====


# A short presentation to the class (10 minutes)
;Due date: Thursday, March 10, 2020
# A final report that is not more than 4500 words (~18 pages)
;Maximum length: ~5 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.
The project planning document is a basic shell/outline of an empirical quantitative research paper. The planning document should focus around three big 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.
* How will you get the data to analyze? Describe the data sources will you collect and how they will be collected.
* How will you analyze the data? Describe the visualizations, tables, or statistical tests that you will produce.


A successful project will tell a compelling, defensible story in prose and plots and will contain source code sufficient to reproduce the results.
One approach that I have found helpful is outlined [[CommunityData:Planning document|on this wiki page]].


==== Paper and Code ====
==== Project presentation and paper ====


Your final project should include detailed information on:
;Paper due date: May 5, 2020
;Maximum length: 4500 words (~18 pages)


* The problem or area you have identified and enough background to understand the rest of your work and its importance or relevance.
;Presentation due date: April 28, 2020
* Your research question(s) and/or hypotheses.
;Maximum length: 8 minutes
* 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!
''The paper:'' Ideally, I expect you to produce a high quality short research paper that you might revise and submit for publication. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the [[structure of a quantitative empirical research paper]].


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.
As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.


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?"
Because the emphasis in this class is on methods and because I'm not an expert in each of your fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.


==== Presentation ====
I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software to handle your bibliographic sources.


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.
I am also open to projects that are in the form of a Jupyter notebook, but I expect the same sorts of content to be present.


;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.
''[[/Final_project_presentations|The presentation:]]'' The presentation will provide an opportunity to share a brief summary of your project and findings with the other members of the class. 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. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills.


;Timing: All presentations will need to be '''a maximum of 7 minutes long'' with additional 2-3 minutes for questions and answers. 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 ===
=== 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.
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 very 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.
Nearly every week, we will begin by discussing challenges and problem sets. 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.
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.
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=== Weekly Coding Challenges ===
=== 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.
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. These coding challenges will be turned in on Blackboard but will not be graded. I encourage you to work together on these challenges but to be sure that you understand the concepts yourself.


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!
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.
You are welcome to discuss the exercises on our Blackboard discussion board but please do not share answers to challenges before they are due. After they are due, you are welcome and encouraged to share your solutions and/or to discuss different approaches. We will discuss a few of the exercises during class and I will randomly choose a few students to explain their solutions.  


== Grades ==
== Grades ==


Assignments will accrue to your final grade in the following way:
This course will follow a "self-assessment" philosophy. I am more interested in helping you to learn things that will be useful to you than grading your. The university still requires grades, so you will be leading the evaluation of your work. This will be completed with me in four stages, at the end of weeks 4, 8, 12, and 16. In each stage, you will reflect on what you have accomplished thus far, how it has met, not met, or exceeded expectations, based both on rubrics and personal goals and objectives. At each of these stages you will receive feedback on your assessments. By the end of the semester, you should have a clear vision of your accomplishments and growth, which you will turn into a grade. As the instructor-of-record, I maintain the right to disagree with your assessment and alter grades as I see fit, but any time that I do this it will be accompanied by an explanation and discussion. These personal assessments, reflecting both honest and meaningful reflection of your work will be the most important factor in final grades.


* 15% will be class participation, including attendance, participation in discussions and group work, and significant effort towards weekly assignments.
I suggest that we use the following rubric in our assessment:
* 5% will be the Final Project Idea.
* 10% will be the Final Project Proposal.
* 50% will be the Final Project write up including visualizations.
* 20% will be your Final Presentation including your slides and presentation.


* 15%: class participation, including attendance, participation in discussions and group work, and significant effort towards weekly assignments.
* 5%: Final Project Idea.
* 10%: Final Project Proposal.
* 50%: Final Project paper/Jupyter notebook.
* 20%: Final Presentation including your slides and presentation.


== Schedule ==
== Schedule ==
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=== Week 1: January 14 ===
=== Week 1: January 14 ===


'''Readings:''' None
'''Assignment Due:''' None
 
'''Required Readings:'''  


'''Class Schedule:'''
'''Class Schedule:'''
* Class overview and expectations — We'll walk through this syllabus.
* Class overview and expectations — We'll walk through this syllabus.
* [[Intro to Programming and Data Science (Spring 2020)/Day_1_Exercise|Day 1 Exercise]] — You'll install software including the Python programming language and run through a series of exercises.
* [[Intro to Programming and Data Science (Spring 2020)/Day_1_Exercise|Day 1 Exercise]] — You'll install software including the Python programming language and run through a series of exercises.
* [[Intro to Programming and Data Science (Spring 2020)/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.
* [[Intro to Programming and Data Science (Spring 2020)/Day_1_Tutorial|Day 1 Tutorial]] — You'll work through a self-guided tutorial introducing you to some basic programming concepts.


* A few interesting links we discussed in class are [[Intro to Programming and Data Science (Spring 2020)/DataSources|here]]
* A few interesting links discussed in class are [[Intro to Programming and Data Science (Spring 2020)/DataSources|here]]
* Hints
** For exercise 5, look at chapter 3 of the textbook. This introduces "if" statements.


'''By the end of class you will:'''
'''By the end of class you will:'''
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=== Week 2: January 21 ===
=== Week 2: January 21 ===


'''Assignment Due (nothing to turn in):'''
'''Assignment Due:''' Finish Day 1 exercises and tutorials


Read chapters 2 and 3 of Python for Everyone:
'''Required readings:''' Python for Everyone, chapters 1-2
* 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 first 14 exercises.


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


* Discuss a successful final project from last year.
* [[Intro to Programming and Data Science (Spring 2020)/Day_2_Lecture|Lecture notes]]
* [[Intro to Programming and Data Science (Spring 2020)/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.
* Project time — We'll begin working on the [[wordplay]] independent projects independently or in small groups.


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== Administrative Notes ==
== Administrative Notes ==


=== Attendance ===
=== Attendance Policy ===


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.  
Attendance is very important and it will be difficult to make up for any classes that are missed. 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 your 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.
=== Incomplete ===


=== Disability Accommodations Statement ===
A grade of incomplete (I) will be given only in unusual circumstances. The request must describe the circumstances, along with a proposed timeline for completing the course work. Submitting a request does not ensure that an incomplete grade will be granted. If granted, you will be required to fill out and sign an “Incomplete Contract” form that will be turned in with the course grades. Any requests made after the course is completed will not be considered for an incomplete grade.


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.


=== Academic Integrity ===
While I encourage collaboration, I expect that any work that you submit is your own. Basic guidelines for Purdue students are outlined [https://www.purdue.edu/odos/osrr/academic-integrity/index.html here] but I expect you to be exemplary members of the academic community. Please get in touch if you have any questions or concerns.


=== Incomplete ===
=== Nondiscrimination ===


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.
I strongly support Purdue's policy of nondiscrimination (below). If you feel like any member of our classroom--including me--is not living up to these principles, then please come and talk to me about it.
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 ===
Purdue University is committed to maintaining a community which recognizes and values the inherent worth and dignity of every person; fosters tolerance, sensitivity, understanding, and mutual respect among its members; and encourages each individual to strive to reach his or her own potential. In pursuit of its goal of academic excellence, the University seeks to develop and nurture diversity. The University believes that diversity among its many members strengthens the institution, stimulates creativity, promotes the exchange of ideas, and enriches campus life.


Grades in this class are based on a rating scale.
=== Students with Disabilities ===


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.
Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let me know so that we can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247.
;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 ===
=== Emergency Preparation ===
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 the event of a major campus emergency, I will update the requirements and deadlines as needed.
In closing, Comm Lead  students are expected to:


* Write coherently and clearly.
=== Mental Health ===
* 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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If you or someone you know is feeling overwhelmed, depressed, and/or in need of mental health support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at 765-494-6995 during and after hours, on weekends and holidays, or by going to the CAPS office of the second floor of the Purdue University Student Health Center (PUSH) during business hours.
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Revision as of 20:55, 6 November 2019

Course Information

COM 574: Introduction to Programming and Data Science
Location: BRNG 2273
Class Hours: Tuesdays; 5:30-8:20pm

Instructor

Instructor: Jeremy Foote
Email: jdfoote@purdue.edu
Office Hours: Thursdays; 12:30-2:30pm; BRNG 2156


Course Overview and Learning Objectives

This is an exciting time to be a social scientist and especially a Communication scholar! An increasing amount of our lives---and our interactions---are stored digitally. Social scientists are increasingly using that data to ask and answer questions about how the social world works. I firmly believe that computational tools have created a new frontier in the social sciences which those who develop computational skills can explore.

This class is an introduction into that world. The course is intended to give students an introduction to programming principles, the Python programming language, and data science tools and approaches. However, this is not a computer science class or a statistics class, and no prior programming experience is required or expected. We will focus on gaining access to data and basic data manipulation rather than complex statistical methods.

The main goal of the class is to help you to complete a preliminary, independent, data-centric project. As part of this project, you (on your own or in a team) will write software to collect data from web APIs, process and clean that data, and produce statistics, hypothesis tests, and graphical visualizations that address questions you are interested in.


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.
  • Understand and follow basic version control practices.
  • Use digital 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.
  • Identify multiple ways that computational approaches are being used for social science research.
  • Feel comfortable taking more advanced computational methods courses or learning new techniques on your own.

Required resources and texts

Laptop

I expect you to come to class every day with your own laptop. We are currently scheduled to meet in the computer lab classroom but I strongly suggest that you use your own laptop. Windows, Mac OS, and Linux are all fine but an iPad or Android tablet won't work. 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.

Readings

  • Required text: Python for Everyone by Charles R. Severance. The book is freely licensed and available online for free. You can also buy the book if you prefer a hard copy.

I will list required chapters in the weekly notes below. In general, you should expect to spend far more time working on programming tasks than reading. 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.

  • Other readings: Throughout the year we will read and discuss examples of computational social science that I find particularly well done or interesting. I will put these document on Blackboard. If you come across additional examples that you think the class would benefit from, please suggest them to me.
  • Optional readings: Matthew Salganik's book 'Bit by Bit: Social Research in the Digital Age' is a wonderful introduction to computational social science. We will not be discussing it in class but I highly recommend it.

Course logistics

Note About This Syllabus

This is a brand new course and this syllabus will be a dynamic document. 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. 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.
  3. I will ask the class for voluntary anonymous feedback frequently. Please let me know what is working and what can be improved.

Office hours and email

  • I will hold office hours Thursday afternoons. If you come with a programming question, I will expect that you have already tried to solve it yourself in multiple ways and that you have discussed it with at least two classmates. This policy lets me have time to help more students, but it's also a useful strategy. Often just trying to explain your code can help you to recognize where you've gone wrong.
  • I am also available by email. You can reach me at jdfoote@purdue.edu. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours (~9-5).

Assignments

The main outcome of this course will be a research project exploring a social science question using Python, and the bulk of your grade will be based on that project. I prefer that you do projects on your own but it may be possible to work as a small team (maximum 3 people). Team projects are expected to be more ambitious than individual projects. Preliminary assignments will help you to develop your idea and to get feedback from me and others.

There will also be weekly programming assignments that I will ask you to hand in but which will only be graded as complete/incomplete. I will randomly sample from the assignments to make sure that people are understanding the topics and I will randomly choose students to share their responses to exercises as an extra way to incentivize you to complete them.

Research project

As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:

  • Design and describe a plan for a study — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.
  • Find a dataset — Very quickly, you should identify a dataset you will use to complete this project.
  • Report and interpret your findings — You will do this in both a short paper and a short presentation.
  • Ensure that your work is replicable — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.

I strongly urge you to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, that you can use as pilot analysis that you can report in a grant or thesis proposal, and/or that fulfills a degree requirement.

There are several intermediate milestones and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Blackboard.

Project plan and dataset identification

Due date
January 28, 2020
Maximum length
500 words (~1-2 pages)

Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:

  • An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.
  • An identification of the dataset you will use and a description of the columns or type of data it will include. If you do not currently have access to these data, explain why and when you will.
  • A short (several sentences?) description of how the project will fit into your career trajectory.

Project planning document

Due date
Thursday, March 10, 2020
Maximum length
~5 pages

The project planning document is a basic shell/outline of an empirical quantitative research paper. The planning document should focus around three big 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.
  • How will you get the data to analyze? Describe the data sources will you collect and how they will be collected.
  • How will you analyze the data? Describe the visualizations, tables, or statistical tests that you will produce.

One approach that I have found helpful is outlined on this wiki page.

Project presentation and paper

Paper due date
May 5, 2020
Maximum length
4500 words (~18 pages)
Presentation due date
April 28, 2020
Maximum length
8 minutes


The paper: Ideally, I expect you to produce a high quality short research paper that you might revise and submit for publication. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the structure of a quantitative empirical research paper.

As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.

Because the emphasis in this class is on methods and because I'm not an expert in each of your fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.

I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, your paper must follow a standard format (e.g., ACM SIGCHI CSCW format or APA 6th edition (Word and LaTeX templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software to handle your bibliographic sources.

I am also open to projects that are in the form of a Jupyter notebook, but I expect the same sorts of content to be present.

The presentation: The presentation will provide an opportunity to share a brief summary of your project and findings with the other members of the class. 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. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills.

Timing
All presentations will need to be 'a maximum of 7 minutes long with additional 2-3 minutes for questions and answers. Concisely communicating an idea in the time allotted is an important skill in its own right.


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 very 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. 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. These coding challenges will be turned in on Blackboard but will not be graded. I encourage you to work together on these challenges but to be sure that you understand the concepts yourself.

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!

You are welcome to discuss the exercises on our Blackboard discussion board but please do not share answers to challenges before they are due. After they are due, you are welcome and encouraged to share your solutions and/or to discuss different approaches. We will discuss a few of the exercises during class and I will randomly choose a few students to explain their solutions.

Grades

This course will follow a "self-assessment" philosophy. I am more interested in helping you to learn things that will be useful to you than grading your. The university still requires grades, so you will be leading the evaluation of your work. This will be completed with me in four stages, at the end of weeks 4, 8, 12, and 16. In each stage, you will reflect on what you have accomplished thus far, how it has met, not met, or exceeded expectations, based both on rubrics and personal goals and objectives. At each of these stages you will receive feedback on your assessments. By the end of the semester, you should have a clear vision of your accomplishments and growth, which you will turn into a grade. As the instructor-of-record, I maintain the right to disagree with your assessment and alter grades as I see fit, but any time that I do this it will be accompanied by an explanation and discussion. These personal assessments, reflecting both honest and meaningful reflection of your work will be the most important factor in final grades.

I suggest that we use the following rubric in our assessment:

  • 15%: class participation, including attendance, participation in discussions and group work, and significant effort towards weekly assignments.
  • 5%: Final Project Idea.
  • 10%: Final Project Proposal.
  • 50%: Final Project paper/Jupyter notebook.
  • 20%: 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: January 14

Assignment Due: None

Required Readings:

Class Schedule:

  • Class overview and expectations — We'll walk through this syllabus.
  • Day 1 Exercise — You'll install software including the Python programming language and run through a series of exercises.
  • Day 1 Tutorial — You'll work through a self-guided tutorial introducing you to some basic programming concepts.
  • A few interesting links discussed in class are here

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: January 21

Assignment Due: Finish Day 1 exercises and tutorials

Required readings: Python for Everyone, chapters 1-2

Class Schedule:

  • Lecture notes
  • Project time — We'll begin working on the wordplay independent projects independently or in small groups.

Here are your 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: January 28

Assignment Due:

Final project idea. Turn in on 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 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 Baby names project.
  • Day 3 Coding Challenges

Resources:

Week 4: February 4

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.
  • Day 4 Coding Challenges

Week 5: February 11

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 Day 5 Notes
  • Spend time on Day 5 Coding Challenges


Week 6: February 18

Course Plan


Week 7: February 25

Assignment Due:

Final Project Proposal. Canvas link here.

Course Plan

Week 8: March 3

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.
  • Visualization dos and don'ts. We'll discuss the European Environmental Agency's 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. 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. This SO post helped me solve it.

Week 9: March 10

Assignment Due:

Final Project Presentation!


March 17

Spring Break: No Class

Week 10: March 24

Assignment Due:

Nothing! But I hope you are making good progress.

Course Plan

Week 11: March 31

Assignment Due:

Nothing! But I hope you are making good progress.

Course Plan

Week 12: April 7

Assignment Due:

Nothing! But I hope you are making good progress.

Course Plan

Week 13: April 14

Assignment Due:

Nothing! But I hope you are making good progress.

Course Plan

Week 14: April 21

Assignment Due:

Nothing! But I hope you are making good progress.

Course Plan


Week 15: April 28

Assignment Due:

Nothing! But I hope you are making good progress.

Course Plan


Administrative Notes

Attendance Policy

Attendance is very important and it will be difficult to make up for any classes that are missed. 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 your responsibility to seek out support from classmates for notes, handouts, and other information.


Incomplete

A grade of incomplete (I) will be given only in unusual circumstances. The request must describe the circumstances, along with a proposed timeline for completing the course work. Submitting a request does not ensure that an incomplete grade will be granted. If granted, you will be required to fill out and sign an “Incomplete Contract” form that will be turned in with the course grades. Any requests made after the course is completed will not be considered for an incomplete grade.


Academic Integrity

While I encourage collaboration, I expect that any work that you submit is your own. Basic guidelines for Purdue students are outlined here but I expect you to be exemplary members of the academic community. Please get in touch if you have any questions or concerns.

Nondiscrimination

I strongly support Purdue's policy of nondiscrimination (below). If you feel like any member of our classroom--including me--is not living up to these principles, then please come and talk to me about it.

Purdue University is committed to maintaining a community which recognizes and values the inherent worth and dignity of every person; fosters tolerance, sensitivity, understanding, and mutual respect among its members; and encourages each individual to strive to reach his or her own potential. In pursuit of its goal of academic excellence, the University seeks to develop and nurture diversity. The University believes that diversity among its many members strengthens the institution, stimulates creativity, promotes the exchange of ideas, and enriches campus life.

Students with Disabilities

Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let me know so that we can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247.

Emergency Preparation

In the event of a major campus emergency, I will update the requirements and deadlines as needed.

Mental Health

If you or someone you know is feeling overwhelmed, depressed, and/or in need of mental health support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at 765-494-6995 during and after hours, on weekends and holidays, or by going to the CAPS office of the second floor of the Purdue University Student Health Center (PUSH) during business hours.

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