Overview

Course Information
Class
Time: Tuesdays & Thursdays, 11 AM-12:20 PM
Location: Eggers 225B
Instructor
Professor Jack Reilly
Office: Eggers 225
Office Hours: Friday, 11 AM - Noon and 3-4 PM
Appointments: schedule online
Phone: 315-443-2687 (office)
email: jlreilly@syr.edu
Course Description
Every step in policymaking relies on data. This course introduces students to data management, wrangling, communication, and visualization in the context of public policy, public administration, and behavioral science, as well as the technical tools necessary to do such work in an open and reproducible fashion.
Expanded Description
Data preprocessing, wrangling, and management often consume a large fraction of the time spent doing quantitative data analysis in public administration, public policy, and behavioral science research. Yet these topics frequently do not receive regular attention in methodological courses that focus on statistical inference. This class introduces students to the technical tools necessary to do these tasks in an open and reproducible fashion suitable for modern computational data workflows in the public sector. Throughout the semester, students will learn the principles and practice of conducting reproducible quantitative research, including readable programming and coding, version control, methods of documentation, data storage, workflow management, and exploratory data visualization. A variety of relevant open-source technical tools will be introduced and used, including but not limited to R (and RStudio), Git (and GitHub), Markdown, and a variety of helper programs to tie things together. Special attention will be paid to data frequently used in public policy, public administration, and behavioral science.
Prerequisites
No formal prerequisites. It is assumed that you have either previously taken or are currently enrolled in an “Introduction to Statistics” or “Quantitative Methods” class at the graduate or undergraduate level (i.e., PAI 721 or MAX 201), and are conversant enough in statistics to be able to work with concepts like “mean” and “standard deviation”.
While this course has no formal prerequisites, it does have a substantial informal prerequisite: motivation. Learning a programming language is challenging work, and students must be prepared to invest the appropriate time, energy, effort, and - above all - patience.
Learning Objectives
1.	Demonstrate capability in open science and contemporary reproducible data processing, wrangling, and management tools
2.	Apply appropriate principles of data and file management to data projects
3.	Create effective, reproducible, and well-designed data visualizations with appropriate tools
4.	Analyze large-N datasets commonly used in public policy and behavioral science
Materials
Books
•	Required:
–	Weidmann, Nils. Data Management for Social Scientists. Open access: https://doi.org/10.1017/9781108990424
–	Healy, Kieran. Data Visualization: A Practical Introduction. Open access: https://socviz.co
–	Hadley Wickham, Garrett Grolemund, and Mine Çetinkaya-Rundel, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd ed. Open access: https://r4ds.hadley.nz.
•	Optional:
–	Recommended: Braun & Murdoch, A First Course in Statistical Programming, 3rd Edition. Purchase links: cambridge amazon
–	Freeman & Ross. Programming Skills for Data Science
A Programming Book
There are, in essence, three kinds of books that are useful for the class: a book on data management, a book on data visualization, and a book on data in R: Weidmann’s DMSS, Healy’s DV, and Wickham et al’s RDS respectively.
You may also wish to have a book that goes deeper into the programming logic of R itself. I recommend Braun and Murdoch’s (FCSP), which is a good general overview of the R language from a statistical programming perspective. Freeman & Ross (PSDS) is a more general introduction to the overall data science technical environment, and while it is getting a little dated (2018), it still has useful material on the overall open science research pipeline and associated tools.
Computing
You will need access to a personal computer for this class. It will need to run a full operating system on which you can install local applications outside of app stores and have access to the command line. Windows, macOS, and Linux are all fine. Tablet or web-book OSes - like Chromebooks or iPads - won’t be sufficient. Aside from the computer, all significant software we use will be free/open-source, and we’ll cover usage and installation in class.
Online Course Resources
Blackboard is our internet-based course platform: http://blackboard.syr.edu. In it, you will find submission portals for assignments and a link to the course webpage, where you can find the course syllabus, problem sets, and links to readings.
You can also link to our course drive here, which contains lecture slides, data sets, and some other useful things for the class.
Please note that class itself is the primary source of course-related announcements and material.
Requirements
Overview
Satisfactory completion of the course requires completion of the following:
•	Regular course attendance and participation (10%)
•	Weekly Assignments (20%)
•	Core Exam (15%)
•	Practicum 1 (15%)
•	Practicum 2 (15%)
•	Final Project (25%)
Early assignments will be submitted via Blackboard; once we learn GitHub, later assignments will be submitted through GitHub.
Attendance
One of the guiding principles of my class is that you are adults, and thus, capable of managing your own time. I have little interest in policing your lives. Attendance is taken for each day of class, but you will lose no points on attendance if you happen to miss a day or two: everyone has things that occasionally come up in life that need to be dealt with, and I fully realize that some of those things are things you - very understandably - may not want to discuss with your professor. That’s OK!
That said, attendance in class is an important element to doing well in the course. If you must miss more than a couple of days, it’s a good idea to check in with me so that I don’t treat the absences as chronic. The easiest way to do this is just email me with a brief reason when something comes up and you have to miss class (which will also allow me to tell you if you’re missing anything particularly important).
If you must miss class, the way to make up what you’ve missed is straightforward: make sure to look over the posted material, do the reading, get notes from a friend, and still complete the assignment if you are able (and make sure to look over any assignment solutions). If you do these things and still feel like you’re missing something, please feel free to come to my office hours and we can talk it through.
Participation
There is no formal grade for “participation”. However, I reserve the right to dock a couple points here if you do ridiculous/unprofessional things in class (always coming in late, regularly distracting others, spontaneously breaking out into ribald song in the middle of class, etc).
Assignments
There is an assignment each week in class, due Thursday by class time.
Assignments will vary in nature: some will be one-off problem sets while some may build on problem sets from a prior week. All material needed for an assignment will be covered by the lecture material before the assignment is due (usually much earlier), and the assignment itself will be given a week ahead of time. No assignment work is accepted after class, as we will go over answers for assignments in class.
Students may miss up to two assignments with no penalty. Students may also work together on assignments - in fact, I encourage you to do so - although each student is ultimately responsible for their own learning and work.
Effort-Based Evaluation
Assignments are evaluated based upon effort and a check completion system. Students who answer every problem will earn a check, with each check worth one point toward their final assignment grade.
Practicums
A practicum is a large assignment that carries more weight and is graded on a scale. It is untimed, take-home, cumulative, and will be completed on your own time (and computer). Unlike the weekly assignments, you are not allowed to work together on the practicum. (You can think of it as a take-home test, if you like.)
Core Exam
The core exam is a pen-and-paper test, completed without computation. You can find a study guide here with more information.
Final Project
A project utilizing data of your own choice. You can find more information on the final project page.
Expectations
Etiquette & Decorum
This is a graduate course: I take it for granted that you have a basic interest in the material, an enthusiastic attitude toward participation, and a respectful attitude to everyone in the room. A university classroom is fundamentally a learning community: be courteous to fellow students and the professor, don’t let yourself be distracted by things on the internet in class, ask questions when the spirit moves you, etc.
Office & Consultation Hours, Appointments
I encourage you to chat with me at any point if you have questions about the course. I make a habit of staying after our regularly scheduled class for a bit to field questions from individual students.
You can also schedule a meeting at your convenience through my website (http://jacklreilly.github.io). If you are on campus, you are always welcome to stop by my office: if the door is open, come on in! (Don’t feel like you’re intruding! I’ll tell you if it’s not a good time.)
Email
Email is the best way to contact me. I’m usually pretty responsive, but as a baseline, I always aim to get back to you in a modified 24-hour fashion: by the end of the business day the day after you email. So if you email me at 2 PM Tuesday, I’ll get back to you by 6 PM Wednesday at the latest; if 10 PM Thursday, by 6 PM Friday; if you email me at 3 PM on Friday, by 6 PM Monday, etc.
If your email requires a long response, expect me to encourage you to schedule an appointment with me so that we can more effectively discuss the matter.
Assessment
Course grades are determined according to the breakdown listed under “Requirements” above.
Grading Scale
Grade
	Grade Point/Credit
	Class Points
	
A
	4.000
	94 – 100
	
A-
	3.667
	90 – 93.99
	
B+
	3.333
	87 – 89.99
	
B
	3.000
	83 – 86.99
	
B-
	2.667
	80 – 82.99
	
C+
	2.333
	77 – 79.99
	
C
	2.000
	73 – 76.99
	
C-
	1.667
	70 – 72.99
	
F
	0
	69.99 or less

Course Assessment Plan
Learning Objective
	Assessment Measure
	
Demonstrate capability in open science and contemporary reproducible data processing, wrangling, and management tools
	Core Exam
	
Apply appropriate principles of data and file management to data projects
	Practicums
	
Create effective, reproducible, and well-designed data visualizations with appropriate tools
	Final Project
	
Analyze large-N datasets commonly used in public policy and behavioral science
	Weekly assignments