Editing Intro to Programming and Data Science (Spring 2020)

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

== Instructor ==
:'''Instructor:''' [https://jeremydfoote.com Jeremy Foote] 
:'''Email:''' jdfoote@purdue.edu
:'''Office Hours:''' Thursdays; 12:30-2:30pm; BRNG 2156


<div style="float:right;">__TOC__</div>

= 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: '''[https://www.py4e.com/book Python for Everyone]''' by Charles R. Severance. The book is [https://creativecommons.org/licenses/by/3.0/us/ 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 documents 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:

# 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 and by appointment. 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) but during the week I will generally respond within 24 hours.

= 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 semester.
* '''Find a dataset''' — You should quickly 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 [[CommunityData:Planning document|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., [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.

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.

''[[/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.


== 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 semester, 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 more than 12 hours before they are due. After that, 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.

My interpretation of grade levels (A, B, C, D/F) is the following:

A: Reflects work the exceeds expectations on multiple fronts and to a great degree. Students reaching this level of achievement will:
* Do what it takes to learn the programming principles and techniques, including looking to outside sources if necessary.
* Engage thoughtfully with an ambitious research project.
* Take intellectual risks, offering interpretations based on synthesizing material and asking for feedback from peers.
* Sharing work early allowing extra time for engagement with others.
* Write reflections that grapple meaningfully with lessons learned as well as challenges.
* Complete most, if not all programming assignments at a high level.

B: Reflects strong work. Work at this level will be of consistently high quality. Students reaching this level of achievement will:
* Be more safe or consistent than the work described above.
* Ask meaningful questions of peers and engage them in fruitful discussion.
* Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week.
* Compose complete and sufficiently detailed reflections.
* Complete many of the programming assignments.

C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement
will:
* Turn in and complete required assignments on time.
* Be collegial and continue discussion, through asking simple or limited questions.
* Compose reflections with straightforward and easily manageable goals and/or avoid discussions of challenges.
* Not complete programming assignments or turn some in in a hasty or incomplete manner.

D/F: These are reserved for cases in which students do not complete work or participate. Students may also be
impeding the ability of others to learn.

= Schedule =

'''NOTE'''  This section will be modified throughout the course to meet the class's needs. Check back in weekly.



== Week 1: Introductions and getting started (January 14) ==

'''Assignment Due:''' 
* None

'''Required Readings:''' 
* None

'''Class Schedule:'''
* 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_Tutorial|Day 1 Tutorial]] — You'll work through a self-guided tutorial introducing you to some basic programming concepts.

'''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: Computational thinking (January 21) ==

'''Assignment Due:''' 
* Finish Day 1 exercises and tutorials

'''Readings (before class):''' 
* Python for Everyone, chapters 1-2
* Foote, J., Shaw, A., & Hill, B.M. (2017). [https://jeremydfoote.com/files/foote_computational_2017.pdf Computational analysis of social media scholarship]. In Burgess, J., Poell, T., Marwick, A. (Eds.), The Sage Handbook of Social Media. Sage.

'''Class Schedule:'''
* [[Intro to Programming and Data Science (Spring 2020)/Day_2_Lecture|Lecture notes]]
* Project time — We'll begin working on the [[wordplay]] projects independently or in small groups.

Here are your [[Intro to Programming and Data Science (Spring 2020)/Day_2_Coding_Challenges|Exercises]]

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

* Have written a program with loops and lists.
* Have a better understanding of the expectations for your final project, and be ready to hand in your initial assignment.

== Week 3: Conditionals and Functions (January 28) ==

'''Assignment Due:'''
* Final project idea (turn in on Blackboard).
* Finish Wordplay examples

'''Readings:''' 
* Python for Everyone, chapters 3-4


'''Course plan:'''
* Go over last week's assignment.
* Dictionaries and aggregations [[Intro to Programming and Data Science (Spring 2020)/Day 3 Notes|Day 3 Notes]]
* Discuss average, median using the wordplay data.
* Project time — We'll begin working on a series of project based on the [http://mako.cc/teaching/2015/cdsw-autumn/babynames.zip Baby names] project.
* [[Intro to Programming and Data Science (Spring 2020)/Day 3 Coding Challenges|Day 3 Coding Challenges]]

'''Resources:'''

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




== Week 4: Iteration, strings, and lists (February 4) ==

'''Assignment Due:'''

* First self-assessment reflection is due.
* Finish Baby Names examples.

'''Readings:''' 
* Python for Everyone
* chapters_to_read = [5, 6, 8]

'''Course Plan'''

* Basic visualizations in python
* Project time - We'll reuse the babynames code. 
* [[Intro to Programming and Data Science (Spring 2020)/Day 4 Coding Challenges|Day 4 Coding Challenges]]



== Week 5: Reading and writing files (February 11) ==

'''Assignment Due:'''

* Turn in (on Blackboard) your solutions to the Day 4 coding challengss

'''Readings:'''
 
 book = open('Python for Everyone', 'r')
 for chapter in book:
     if chapter = '7':
         read(chapter)
 book.close()

'''Course Plan'''

* Go over last week's assignment and review histograms.
* Discuss APIs and downloading data from the internet. Refer to [[Intro to Programming and Data Science (Spring 2020)/Day 5 Notes|Day 5 Notes]]
* Spend time on [[Intro to Programming and Data Science (Spring 2020)/Day 5 Coding Challenges|Day 5 Coding Challenges]]



== Week 6: Dictionaries and networks (February 18) ==

'''Assignment Due:'''

* Turn in (on Blackboard) your solutions to the Day 4 coding challenges

'''Readings:'''

* Python for Everyone, chapters 9 and 10

'''Course Plan'''

* Discuss data downloading and cleaning. Refer to [[Intro to Programming and Data Science (Spring 2020)/Day 7 Notes|Day 7 Notes]]
* We will be discussing this data set: https://hub.mph.in.gov/dataset/aries-crash-data-2007-2017/resource/cc90589c-72d8-4d92-a5fe-73254b555c73
* Spend time on [[Intro to Programming and Data Science (Spring 2020)/Day 7 Coding Challenges|Day 7 Coding Challenges]] which are group challenges.



== Week 7: Pandas and dataframes (February 25) ==

'''Assignment Due:'''

Final Project Proposal.

'''Readings:'''


'''Course Plan'''

* Discuss pivot tables in Excel
* [[Intro to Programming and Data Science (Spring 2020)/Day 8 notes|Day 8 notes]]




== Week 8: Jupyter and Data Visualization (March 3) ==

'''Assignment Due:'''
* Second self-assessment reflection is due.

'''Readings:'''

'''Course Plan'''

* Follow up from last week: let's discuss inference and A/B testing.
** [https://www.exp-platform.com/Documents/2016-11BestRefutedCausalClaimsFromObservationalStudies.pdf Examples of bad observational studies]
* Visualization dos and don'ts. We'll discuss the European Environmental Agency's [https://www.eea.europa.eu/data-and-maps/daviz/learn-more/chart-dos-and-donts list of advice for making charts]. **I will refer to this guide as a grade your final projects.**
* Two options for remainder of class. You can work through this introductory guide to visualization in python or you can work on your final project. I'll be here to answer any questions.

'''Optional visualization in python tutorial'''
Self-guided visualization tutorial in python. [https://raw.githubusercontent.com/guyrt/teaching/master/2019/Com520B/VisualizationNotebook.ipynb Download here]. Save the file in a new directory in your desktop and open it with jupyter notebook

If you are on Windows, you may run into an issue with missing path variables. [https://stackoverflow.com/questions/52821162/jupyter-notebook-failed-to-load-dll This SO post helped me solve it.]




== Week 9: Collecting data with APIs (March 10) ==

'''Assignment Due:'''

'''Readings:'''



== March 17: SPRING BREAK ==

'''Spring Break: No Class'''

Have a great Spring Break!




== Week 10: Cleaning data and operationalization (March 24) ==

'''Assignment Due:'''


'''Readings:'''

'''Course Plan'''




== Week 11: Storing code and data (March 31) ==

'''Assignment Due:'''


'''Readings:'''

'''Course Plan'''
* We will learn about using the version control system Git and the Git hosting site Github



== Week 12: Screen scraping (April 7) ==

'''Assignment Due:'''
* Third self-assessment reflection is due.

'''Readings:'''

'''Course Plan'''




== Week 13: Statistical summaries and tests (April 14) ==

'''Assignment Due:'''

'''Readings:'''

'''Course Plan'''




== Week 14: Introduction to computational text analysis (April 21) ==


'''Assignment Due:'''

'''Readings:'''

'''Course Plan'''



== Week 15: Project presentations (April 28) ==

'''Assignment Due:'''
* Final project presentations

'''Readings:'''

'''Course Plan'''
* We will listen to each of the final project presentations.


== Week 16: Finals week (April 28) ==

'''Assignment Due:'''
* Final paper due

= 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 [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.

== 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.

= Acknowledgements =

This course is heavily based on earlier courses taught by [https://commlead.uw.edu/team/guy/ Tommy Guy] and [https://mako.cc/ Mako Hill] at the University of Washington as well as a course taught by [http://www.lauraknelson.com/p/about.html Laura Nelson] at Northeastern University.