INTEG 475: Computational Social Science

Tuesdays and Thursdays, 1:00 PM - 2:20 PM in EV2 2069
Class Slack Group: integ475-winter2018.slack.com

Professor: Dr. John McLevey
Office hours: Thursdays, 3:30 pm - 4:45 pm or by appointment
Office: Environment 1 Room 215

Course Description

We are living in an age where digital information is being produced at an unprecedented rate. This explosion of digital data has the potential to revolutionize the way we learn about the world, and how we conduct research related to urgent social and political problems. This course focuses primarily on the knowledge and skills necessary for doing high-quality research with digital data. We will begin by considering the promise and pitfalls of “big data” for social science, and on the intersection of research in social science and data science. Then we will start a research-oriented introduction to the programming language Python, followed by a series of classes on collecting, cleaning, and combining digital datasets. Finally, we will move into a series of classes on analyzing digital datasets using tools from machine learning, text analysis, and social network analysis. There will be an emphasis on good research design and ethics throughout the course.

I assume no previous knowledge or experience of computer programming. Previous courses in research methods and / or statistics are an asset, but are not required. The same goes for previous courses in the social sciences, especially sociology and political science.

In designing this course, I was influenced by (broadly) similar courses offered by the sociologists Matthew Salganik (Course: “Computational Social Science” at Princeton University), Laura Nelson (Course: “Digital Methods for Social Sciences and Humanities” at Northeastern University), and Chris Bail (Course: “Computational Sociology” at Duke University). I was also influenced by the general design of the “Data 8: Foundations of Data Science” course at UC Berkeley.

Learning Objectives

The schedule section of the syllabus identifies the core learning objectives for every scheduled topic in the course.

Books

There are 4 books that we will use extensively. They are available on reserve at the university library (Porter), in the university bookstore, and online. One of them (Python for Everybody: Exploring Data In Python 3) is freely available online as a PDF file, ebook, or HTML book. Currently, you can read Salganik’s book Bit by Bit online as an HTML book, but I’m not sure if it will stay online once the book becomes officially available in print.

In addition to these books, you may find it occasionally helpful to consult other resources for learning Python, especially if you are interested in some specific area, such as natural language processing. I have plenty of suggestions of further things to read, so don’t hesitate to ask.

If you have no previous knowledge of HTML or CSS, you might consider getting yourself a copy of Jon Duckett’s (2011) HTML and CSS: Design and Build Websites. It’s a nice (and beautiful) introduction that assumes no prior knowledge. Of course there are plenty of great resources online. You don’t need to buy the book. Your web scraping skills will advance rapidly as you become more familiar with HTML and CSS.

Any other readings will be available on the course website, on reserve, or through the library database.

Many of our classes will be hands-on labs / workshops. I’ll regularly walk the class through example code, but I will do so assuming that you have done the readings before coming to class.

Computing

You need to have access to a laptop, and you need to bring it to every class meeting from January 16th onwards. If that is not possible, talk to me as soon as you can and I will try to help you work out another option.

Before coming to class on January 16th, you should install Anaconda for Python 3.6 on your laptop. Do not get Python 2.7. Get 3.6. Anaconda will install Python on your system, plus Jupyter Notebooks and many of the packages we will need for this course. We will install additional packages throughout the course as necessary.

Almost all of the computing we do in this course will be in Jupyter Notebooks. The Jupyter Notebook system comes with Anaconda 3, however I suggest that you consider nteract as an alternative way of writing and executing notebooks. If you prefer to work in a text editor (like I do), you should try the Hydrogen plugin for Atom, which will enable you to code interactively.

Deliverables

AssignmentDates %
Data ChallengesWill receive on Feb 6, Feb 13, Mar 1, Mar 15, and Mar 22 30
Self AssessmentsQuantitative due every class, qualitative due with data challenges 10
Tutorial NotebookDue anytime after reading week and before March 29th 10
Final Paper / ReportDue on Friday April 13th 40
Engagement / ParticipationOngoing 10

Data Challenges

From January 16th onwards, there will be small programming exercises that you will work on during and outside of class. In addition to those small assignments, you will collaborate with one or two other people on “data challenges.” These will be slightly more challenging exercises where you will have to draw on the knowledge you have acquired to do something related to content we are covering in the course at the time (e.g. scrape a website of your choosing and write the results to a new dataset, generate and analyze a semantic network from unstructured text). Your submission must be fully reproducible. In other words, I must be able to open your notebook and run the code myself without errors.

The dates for data challenges listed on this syllabus are actually the dates when I will give you the details of the data challenge, not when they are due. I will give you exactly one week to do the challenges. They will not actually take you that long to do, but I realize that you may wish to collaborate face-to-face and that requires dealing with scheduling constraints.

Finally, when you submit your data challenges, each person must include, as an appendix at the end of the notebook, a paragraph long personal self-assessment. More details on this in the section below.

You will submit your data challenge notebooks (and data) as a zipped folder on LEARN and on slack.

Self Assessments

There is a lot of diversity in this class. Some of you have a lot of experience programming, but most of you have none. Some of you have a lot of experience doing research in the social sciences, but most of you do not. It’s always challenging to run a class like this when people have radically different starting points and backgrounds. But… it’s also really fun and intellectually stimulating!

One of the key ways I will try to support everyone’s individual progress is by relying on frequent honest self-assessments. Being honest is in your self-interest, because if you are not honest you will quickly get left behind, and if I have good reason to suspect you being intentionally dishonest, you risk loosing most or all of this part of your course grade. Be honest!

The self-assessments will happen in two ways. Every class you will complete a very short survey (< 1 min long) consisting only of fixed-response questions and the occasional short answer question requiring no more than a couple of sentences. The second type of assessment you will do is qualitative. Each of the five collaborative data challenges you submit must include a paragraph-long qualitative self-assessment for every person on the team. I want to know what you did, what you learned, how you pushed yourself, and what you think you need to keep working on. You may also include other details as well, if you think they are relevant.

What am I looking for in these self-assessments? I will be using the quantitative assessments to inform how I deliver lectures, facilitate discussions, and run hands-on workshops during class meeting times. I will use the data to keep track of when you are pushing yourself to the limits of your comfort zone (and therefore putting yourself in a position to learn), and when you are not. Similarly, I will use the qualitative data, combined with my own observations, to better understand what you are doing to push yourself to learn. Obviously, the way to get top marks in this part of the course is to work hard and put yourself at the edge of your comfort zones regardless of where that might be, and regardless of where you started from.

Tutorial Notebook

With 1-3 collaborators, you will produce a Jupyter Notebook that introduces your fellow students to material from one of the chapters in Scraping the Web with Python or Big Data and Social Science that we are not already covering in class (e.g. one of the chapters on working with images, or forms and logins). Your notebook should include one or two complete examples with functional Python code and real data. You must cover all the key content from whatever chapter you have selected, but do not simply reproduce the same code and explanations that you find in the text. Draw on knowledge from other parts of the course. You should also include a paragraph in the notebook that describes exactly what each member of your group contributed, and whether or not your group lived up to the original agreements for collaborating. You may also contact me privately if you want to talk about your collaboration.

I will provide a sign-up form at the start of class on February 15th (right before reading week). You will submit your tutorial notebook (and data) as a zipped folder on LEARN and on slack.

Final Paper / Report

With 1-3 collaborators, you will produce a Jupyter notebook that answers an important or interesting research question by analyzing real data. All text, Python code, and output (e.g. graphs, tables) must be included in the Jupyter Notebook that you submit to me, along with your raw data. I should be able to reproduce your entire analysis as I work through your notebook. You can write about anything you want, but consider running the idea by me first.

Your notebook must begin with at least a few paragraphs describing the general problem your team is concerned with, the specific research questions you are addressing, and why the problem and questions are important or interesting. Your notebook must also include a discussion of the data you are analyzing, where it came from, what it’s strengths and limitations are, and what methods you are going to use to analyze that data. (It is not necessary to use methods not covered in the course, although you may do so if you wish.) The findings section of your report must include inline visualizations and tables. Finally, you must interpret the meaning of what you found given the questions you asked, describe what you learned from this assignment, and describe any additional data and / or analyses that you think would help your further the work you started with this notebook.

As with other collaborative work in this course, you should also include a paragraph in the notebook that describes exactly what each member of your group contributed, and whether or not your group lived up to the original agreements for collaborating. You may also contact me privately if you want to talk about your collaboration.

Engagement / Participation

The quality of this course – like any other – depends on you being engaged. Your participation grade will be based on (a) contributions to class discussion, (b) small group discussion, (c) your involvement in any online discussions, and (d) attendance. If you really don’t like speaking up in class, you can participate more online, but you must speak with me about this. Although I will not be assigning a participation grade until the end of the semester, I am happy to provide qualitative feedback on your participation throughout the semester.

If you arrive more than 10 minutes late, you will lose 50% of the credit for attending class. In other words, arriving late twice is equivalent to missing a class. There is no penalty for excused absences, which always require advance notice and generally require a note from a doctor.

COURSE SCHEDULE

DateTopic
Thursday Jan 04Introduction
Tuesday Jan 09Getting started with Jupyter
Thursday Jan 11Social science with digital data
Tuesday Jan 16Getting started with Python (or agent-based models in Python)
Thursday Jan 18Getting started with Python (or agent-based models in Python)
Tuesday Jan 23Getting started with Python (or agent-based models in Python)
Thursday Jan 25To scrape the web, understand the web (or agent-based models in Python)
Tuesday Jan 30To scrape the web, understand the web
Thursday Feb 01Scrape the web
Tuesday Feb 06Crawl the web
Thursday Feb 08Collecting digital data from APIs
Tuesday Feb 13Parsing documents and cleaning dirty data
Thursday Feb 15Digital data + asking questions
Tuesday Feb 20Reading week, no class
Thursday Feb 22Reading week, no class
Tuesday Feb 27Experiments and mass collaborations
Thursday Mar 01Machine learning and computational text analysis
Tuesday Mar 06Machine learning and computational text analysis
Thursday Mar 08Machine learning and computational text analysis
Tuesday Mar 13Analyzing networks
Thursday Mar 15Analyzing networks
Tuesday Mar 20Analyzing networks
Thursday Mar 22Combining data from multiple sources
Tuesday Mar 27Scaling up: tools for research computing with big data
Thursday Mar 29Student panel: Some NetLab Students, Past and Present

Detailed Schedule and Learning Objectives

Most of the class descriptions below include suggested readings and / or videos in addition to required reading and videos. I have included these to help you go a bit further than what we cover in class. You will not lose any points if you decide not dig into the suggested material. You will not be disadvantaged in class either, since I will not likely bring this material up in class.

thursday jan 04 INTRODUCTION

This class meeting will introduce the core themes and learning objectives for the term. By the end of the class, you should (1) know what the key themes of the course are, (2) be able to clearly explain what I expect from you in the course, and (3) understand what you need to do to succeed.

  • This syllabus!

Supplementary / Optional: Chapter 1 “Introduction” from Big Data and Social Science

Supplementary / Optional: David Lazer and Jason Radford. 2017. “Data ex Machina: Introduction to Big Data.” Annual Review of Sociology 43:19-39.

Supplementary / Optional: Dalton Conley et al. “Big Data. Big Obstacles.The Chronicle Review.

Supplementary / Optional: Eszter Hargittai. 2015. “Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites.” The ANNALS of the American Academy of Political and Social Science 659(1).


tuesday jan 09 GETTING STARTED WITH JUPYTER

By the end of this class, you should (1) have all the required software installed, and (2) be able to start a Jupyter Notebook. Please try to have the software installed on your laptop before class starts. We will likely lose a lot of class time to a few uncooperative machines.

BTW, the reading for Thursday is a long one, so you should aim to be at least 1/2 through it by this point.


thursday jan 11 SOCIAL SCIENCE WITH DIGITAL DATA

By the end of this class, you should be able to (1) explain how social scientists and data scientists use digital data in observational studies, and (2) describe the 10 common characteristics of “big data” discussed in Salganik: big, always on, nonreactive, incomplete, inaccessible, non-representative, drifting, algorithmically confounded, dirty, and sensitive.

  • Chapter 2 “Observing Behavior” from Bit by Bit

tuesday jan 16 GETTING STARTED WITH PYTHON (OR AGENT-BASED MODELS IN PYTHON)

By the end of this class, you should be able to (1) assign things to variables, (2) execute code conditionally, and (3) explain what a function is, use built-in functions, and understand when, why, and how you might want to write your functions.

If you do not need an introduction to Python, there will be parallel sessions on agent-based modeling in Python. These sessions will (1) review some of the fundamental theory and concepts information agent-based models and other simulation methods in the social sciences, (2) review the paradigm of object-oriented programming, and (3) explain how to develop simple agent-based models in Python. If you decide to join these sessions, please read Macy and Willer (2002) “From Factors to Actors: Computational Sociology and Agent-Based Modeling.” Annual Review of Sociology. 28:143-166 and “Why Agent-Based Modeling?” from Wilensky and Rand (2015) An Introduction to Agent-Based Modeling.

If you are attending the introductory Python class, please consult these readings before coming to class:

Supplementary / Optional: Watch and follow along with Jessica McKellar’s “A Hands-On Introduction to Python for Beginning Programmers.” If you have no previous Python background, I recommend watching and following along with McKellar’s tutorial before coming to class.

Supplementary / Optional: Watch Brian Granger, Chris Colbert, and Ian Rose’s 2017 talk “JupyterLab: The Evolution of the Jupyter Notebook” to get a sense of what you can do with Jupyter Lab.


thursday jan 18 GETTING STARTED WITH PYTHON (OR AGENT-BASED MODELS IN PYTHON)

By the end of this class, you should be able to (1) understand iteration in Python, (2) manipulate strings, and (3) understand basic data structures (lists, dictionaries, and tuples).

If you do not need an introduction to Python, there will be parallel sessions on agent-based modeling in Python. These sessions will (1) review some of the fundamental theory and concepts information agent-based models and other simulation methods in the social sciences, (2) review the paradigm of object-oriented programming, and (3) explain how to develop simple agent-based models in Python. If you decide to join these sessions, please read “What Is Agent-Based Modeling?” from Wilensky and Rand (2015) An Introduction to Agent-Based Modeling and take about 30 minutes to play Nicky Case’s online game “The Evolution of Trust.”

If you are attending the introductory Python class, please consult these readings before coming to class:


tuesday jan 23 GETTING STARTED WITH PYTHON (OR AGENT-BASED MODELS IN PYTHON)

By the end of this class, you should be able to (1) read, create, open, modify, and save files; and (2) work with data in the form of Pandas dataframes.

If you do not need an introduction to Python, there will be parallel sessions on agent-based modeling in Python. These sessions will (1) review some of the fundamental theory and concepts information agent-based models and other simulation methods in the social sciences, (2) review the paradigm of object-oriented programming, and (3) explain how to develop simple agent-based models in Python. If you decide to join these sessions, please read “Creating Simple Agent-Based Models” from Wilensky and Rand (2015) An Introduction to Agent-Based Modeling.

If you are attending the introductory Python class, please consult these readings before coming to class:


thursday jan 25 TO SCRAPE THE WEB, UNDERSTAND THE WEB (OR AGENT-BASED MODELS IN PYTHON)

By the end of this class, you should be able to (1) explain the basics of what happens when you visit websites, (2) understand the basics of HTML markup.

If you do not need an introduction to Python, there will be parallel sessions on agent-based modeling in Python. These sessions will (1) review some of the fundamental theory and concepts information agent-based models and other simulation methods in the social sciences, (2) review the paradigm of object-oriented programming, and (3) explain how to develop simple agent-based models in Python. If you decide to join these sessions, please read “Exploring and Extending Agent-Based Models” from Wilensky and Rand (2015) An Introduction to Agent-Based Modeling.

If you are attending the introductory Python class, please consult these readings before coming to class:

Supplementary / Optional:Encryption and Public Keys” from the code.org playlist “How the Internet Works.”

Supplementary / Optional:Cybersecurity and Crime” from the code.org playlist “How the Internet Works.”

Supplementary / Optional:How Search Works” from the code.org playlist “How the Internet Works.”


tuesday jan 30 TO SCRAPE THE WEB, UNDERSTAND THE WEB

By the end of this class, you should be able to (1) understand more complex HTML markup, (2) explain how CSS works, and (3) understand how to collect data from websites by scraping or using Application Programming Interfaces (API).

  • Chapter 2 “Working with Web Data and APIs” from Big Data and Social Sciences

  • Skim Chapters 6 - 10 (“Tables,” “Forms,” “Extra Markup,” “Flash, Audio, and Video,” and “Introducing CSS”) from Jon Duckett’s HTML and CSS: Design and Build Websites. (There are a lot of images in this book, and the chapters are very short and easy to understand.)


thursday feb 01 SCRAPE THE WEB

By the end of this class, you should be able to (1) put your new knowledge of HTML and CSS to work in developing your first web scraper.

  • Chapter 1 “Your First Web Scraper” from Web Scraping with Python

  • Chapter 2 “Advanced HTML Parsing” from Web Scraping with Python

Supplementary / Optional: Watch Ryan Mitchell’s DEF CON 23 talk “Separating Bots from the Humans.” I suggest watching this video if you really want to get into scraping complex web pages. If the material we have covered so far is all new to you, this video may be a bit too advanced, but it’s still worth watching.

Supplementary / Optional: Watch Corey Schafer’s Beautiful Soup tutorial


tuesday feb 06 CRAWL THE WEB

By the end of this class, you should be able to (1) develop a web scraper that can crawl!

  • Chapter 3 “Starting to Crawl” from Web Scraping with Python

thursday feb 08 COLLECTING DIGITAL DATA FROM APIS

By the end of this class, you should be able to (1) explain what APIs are, (2) find APIs for data you are interested in, and (3) collect data from APIs.

  • Chapter 4 “Using APIs” from from Web Scraping with Python

tuesday feb 13 PARSING DOCUMENTS AND CLEANING DIRTY DATA

By the end of this class, you should be able to (1) parse data from documents rather than web pages, and (2) clean up messy unstructured data from web pages and documents.

  • Chapter 6 “Reading Documents” from Web Scraping with Python

  • Chapter 7 “Cleaning Your Dirty Data” from Web Scraping with Python


thursday feb 15 DIGITAL DATA + ASKING QUESTIONS

By the end of this class, you should be able to (1) describe the role that survey methods and the ‘total survey error framework’ play in the digital age; (2) accurately describe historical changes in sampling strategies, and explain why Salganik thinks new approaches to non-probability sampling differ from earlier approaches; (3) describe innovations in how we ask questions; and (4) differentiate between “enriched asking” and “amplified asking.”

Supplementary / Optional: Watch Chris Bail’s 2017 talk “Apps for Social Science Research” from the Summer Institute in Computational Social Science

Supplementary / Optional: Christopher Bail. 2015. “Taming Big Data Using App Technology to Study Organizational Behavior on Social Media.” Sociological Methods & Research 46(2):1-29.

Supplementary / Optional: Read Chapter 10 “Errors and Inference” from Big Data and Social Science


tuesday feb 20 READING WEEK, NO CLASS

  • None!

thursday feb 22 READING WEEK, NO CLASS

  • None!

tuesday feb 27 EXPERIMENTS AND MASS COLLABORATIONS

By the end of this class, you should be able to (1) explain the role of experiments in social science and data science and explain how they are changing in the digital age, and (3) explain the role of mass collaborations in social science and data science and explain how they are changing in the digital age.

Supplementary / Optional: Devah Pager 2003 “The Mark of a Criminal Record.” American Journal of Sociology. 108(5):937-975.


thursday mar 01 MACHINE LEARNING AND COMPUTATIONAL TEXT ANALYSIS

By the end of this class, you should be able to (1) characterize the general types of problems that machine learning methods can address, (2) describe how machine learning fits into the larger analysis landscapes of data science and computational social science, (3) prepare unstructured text for a computational analysis.

  • Gabe Ignatow and Rada Mchalcea. 2018. Pages 99 - 130 in Text Mining: Research Design, Data Collection, and Analysis. SAGE. Chapters cover basic text processing and supervised learning. It is fine to skim the chapter on supervised learning methods. I want you to have a high-level understanding of the general processes and goals. We will get into specific methods later, using concrete examples of applications.

  • James Evans and Pedro Aceves. 2016. “Machine Translation: Mining Text for Social Theory.” Annual Review of Sociology. 42:21–50.

  • Pages 176 - 187 from John McLevey and Reid McIlroy-Young (2017) “Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science.” Journal of Informetrics 11:176-197.

Supplementary / Optional: NetLab blog post by John McLevey http://networkslab.org/2017/08/29/2017-08-29-mkrecords/

Supplementary / Optional: Justin Grimmer and Brandon Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis. 1-31.

Supplementary / Optional: David Zentgraf “What every programmer absolutely, positively needs to know about encodings and character sets to work with text

Supplementary / Optional: Chapter 6 “Machine Leaning” from Big Data and Social Science

Supplementary / Optional: Alex Hanna. 2017. “MPEDS: Automating the Generation of Protest Event Data”. SocArXiv. osf.io/preprints/socarxiv/xuqmv.

Supplementary / Optional: Aurélien Géron (2017) Chapter 1 “The Machine Learning Landscape” from Hands-On Machine Learning with Scikit-Learn and TensorFlow.

Supplementary / Optional: Chapter 1 from Alex Smola and S.V.N. Vishwanathan (2008) Introduction to Machine Learning. Cambridge University Press.

Supplementary / Optional: Michael Smith (2017) “How Canada has emerged as a leader in artificial intelligence.” University Affairs

Supplementary / Optional: Chris Albon’s “Machine Learning Flashcards

Supplementary / Optional: Watch Hilary Mason’s 2015 keynote address on machine intelligence at the Grace Hopper Celebration of Women in Computing.


tuesday mar 06 MACHINE LEARNING AND COMPUTATIONAL TEXT ANALYSIS

By the end of this class, you should be able to (1) cluster documents based on similarity measures and k-means clustering, (2) develop and interpret an LDA topic model, and (3) develop and interpret topics in a semantic network.

Supplementary / Optional: Watch Jennifer Pan’s Databite talk “How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument

Supplementary / Optional: Gary King, Jennifer Pan, and Molly Roberts. 2013. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review. 107(2):326-343.


thursday mar 08 MACHINE LEARNING AND COMPUTATIONAL TEXT ANALYSIS

By the end of this class, you should be able to (1) prepare a text dataset for an analysis using a supervised learning method, and (2) explain how and why supervised and unsupervised learning methods can be combined in a research project.

Supplementary / Optional: Christopher Bail. 2015. “Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media.” PNAS


tuesday mar 13 ANALYZING NETWORKS

By the end of this class, you should be able to (1) describe some of the key concepts, theories, and methods from social network analysis; (2) accurately characterize similarities and differences between network analysis in the social sciences and the natural and computational sciences, and (3) generate network datasets from raw data.

  • Garry Robins. 2015. “Fundamental network concepts and theories” and “Research questions and study design” in Doing Social Network Research: Network-based Research Design for Social Scientists.

  • Stephen Borgatti, Ajay Mehra, Daniel Brass, and Giuseppe Labianca. 2009. “Network Analysis in the Social Sciences.” Science 323(5916):892-895.

  • Watch Mark Granovetter on social networks and getting a job

Supplementary / Optional: Peter Carrington (2014) “Social Networks Research,” In Silvia Dominguez & Betina Hollstein (eds.), Mixed Methods Social Networks Research. Cambridge, UK: Cambridge University Press.

Supplementary / Optional: Watch Filiz Garip on network effects and social inequality

Supplementary / Optional: Watch Sandra Gonzalez-Bailon describe her new book [Decoding the Social World: Data Science and the Unintended Consequences of Communication](https://www.youtube.com/watch?v=o2y9hNvwiTM)

Supplementary / Optional: Watch Sandra Gonzalez-Bailon’s 2017 talk “Decoding the Social World” from the Summer Institute in Computational Social Science. It’s pretty advanced relative to what you have learned so far, so if you watch this video, focus on trying to understand the problem that Dr. Gonzalez-Bailon’s is addressing, the logic of her research design, and her key arguments.


thursday mar 15 ANALYZING NETWORKS

By the end of this class, you should be able to (1) compute descriptive statistics for whole networks, (2) analyze centrality scores, (3) detect subgroups, and (4) plot networks.

Supplementary / Optional: Allyson Stokes and John McLevey (2016) “From Porter to Bourdieu: The Evolving Specialty Structure of English Canadian Sociology, 1966 to 2014Canadian Review of Sociology 53(2): 176-202.

Supplementary / Optional: Watch Lada Adamic’s talk “Social Networks as Information Filters.” The talk is advanced for what you know so far. Focus on understanding the problem that Dr. Adamic is addressing, her research design, and her core findings.

Supplementary / Optional: Watch Jure Leskovec’s 2011 talk “Rhythms of Information Flow Through Networks.” Again, it’s advanced, so focus on understanding the big picture.

Supplementary / Optional:** Andrey Rzhetskya, Jacob Foster, Ian Foster and James Evans (2015) “Choosing experiments to accelerate collective discovery.” PNAS. 112(47):14569–14574.

Supplementary / Optional: Lingfei Wu, Dashun Wang, James Evans (2017) “ Large Teams Have Developed Science and Technology; Small Teams Have Disrupted It” arXiv:1709.02445.


tuesday mar 20 ANALYZING NETWORKS

By the end of this class, you should be able to (1) identify positions (as opposed to communities) in a network, (2) differentiate between 2-mode, multi-level, multi-plex, and multiple networks, and (3) describe statistical models for testing hypotheses with network data.

  • Chapters on “Equivalence,” “Analyzing Two-mode Data,” and “Testing Hypotheses” in Borgatti, Everett, and Johnson. 2018. Analyzing Social Networks SAGE.

  • Garry Robins. 2015. “Drawing conclusions: inference, generalization, causality, and other weighty matters.” in Doing Social Networks Research. SAGE.

  • Watch James Evans talk about political echo chambers and consumption of science.


thursday mar 22 COMBINING DATA FROM MULTIPLE SOURCES

By the end of this class, you should be able to (1) explain what record linkage and discuss the challenges and opportunities it introduces, (2) differentiate between approaches to record linkage, and (3) understand and modify the code for a record linkage workflow in Python, including preparing datasets for record linkage, comparing records, classifying records, fusing datasets, and evaluating linkages.

Supplementary / Optional: Peter Christen (2012) Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.


tuesday mar 27 SCALING UP: TOOLS FOR RESEARCH COMPUTING WITH BIG DATA

By the end of this class, you will be able to (1) compare databases with other ways of storing and accessing data; (2) explain the basic ideas behind Hadoop, MapReduce, and Spark, and time permitting; (3) define and understand how to use the dataframe API for PySpark.

  • Skim Chapter 4 “Databases” from Big Data and Social Science

  • Skim Chapter 15 “Using Databases and SQL” from Charles Severance’s Python for Everyone

  • Read Chapter 5 “Programming with Big Data” from Big Data and Social

Supplementary / Optional: Watch Andrew Ray’s talk “Data Wrangling with PySpark for Data Scientists Who Know Pandas.”


thursday mar 29 STUDENT PANEL: SOME NETLAB STUDENTS, PAST AND PRESENT

If we can make their schedules work, we will have a student panel with some of my former students and NetLab researchers: Jillian Anderson (now in a data science MSc at Simon Fraser), Joel Becker (now a data scientist at Shopify), Tahin Monzoor (current NetLab student, previously worked on a data science team at Interact), Steve McColl (currently a data scientist with the federal government), Tiff Lin (currently a research fellow at the Berkman Klein Center for Internet and Society at Harvard University), and Reid McIlroy-Young (currently in a computational social science Master’s program at the University of Chicago).


Course Policies

Submitting Work & Grading Process

I will only grade work that you submit electronically.

Text matching software will be used to screen assignments in this course. This is being done to verify that use of all materials and sources in assignments is documented. Students will be given an option if they do not want to have their assignment screened by Turnitin. If you do not wish to submit your work to Turnitin, you can schedule a meeting with me to discuss your submissions in person.

Late Policy

I will deduct 5 points a day for every day, or part of a day, that your work is late, including weekends. I will not make exceptions without a medical note.

Laptops and the Facebook Penalty

Laptops may be used in the classroom on the honors system, but you must sit in the designated laptop section. I reserve the right to modify this policy if laptops appear to be interfering with student learning. If I see Facebook, email, an IM client other than #slack, a newspaper story, a blog, or any other content not related to the class, I will remove 1 point from your participation grade on the spot.

Communication

We will be using the collaboration tool #slack for regular class communication. I use the do not disturb settings on #slack, so I will not see any messages you send me outside of normal working hours. You are free to email me, of course, but I tend to respond to slack messages from students faster than I respond to emails. There are free #slack apps for Mac OS X, Linux (beta), Windows (beta), iOS, and Android.


Preferred Chat System, by XKCD

Feedback

I will solicit brief, informal, and confidential course evaluations throughout the semester. These will only take a few minutes of your time. The purpose is to make sure that we are moving at a comfortable pace, that you feel you understand the material, and that my teaching style is meeting your needs. I will use this ongoing feedback to make adjustments as the course progresses. Although you are not obligated to do so, please fill out the evaluations so that I can make this the best learning experience for you, and the best teaching experience for me.

On Campus Resources

Access Ability Services

The AccessAbility Office, located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the AccessAbility Office at the beginning of each academic term.

Mental Health

The University of Waterloo, the Faculty of Environment, and our Departments consider students’ well-being to be extremely important. We recognize that throughout the term students may face health challenges – physical and / or emotional. Please note that help is available. Mental health is a serious issue for everyone and can affect your ability to do your best work. Counselling Services is an inclusive, non-judgmental, and confidential space for anyone to seek support. They offer confidential counselling for a variety of areas including anxiety, stress management, depression, grief, substance use, sexuality, relationship issues, and much more.

The Writing Centre

Although I will be giving you feedback on your work throughout the term, I encourage you to make appointments with people at the writing centre. Their services are available to all UW students.

University Policies

Academic Integrity

In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility.

We will all uphold academic integrity policies at University of Waterloo, which include but are not limited to promoting academic freedom and a community free from discrimination and harassment. You can educate yourself on these policies – and the disciplinary processes in place to deal with violations – on the Office of Academic Integrity website.

A student is expected to know what constitutes academic integrity, to avoid committing academic offense, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about ‘rules’ for group work / collaboration should seek guidance from the course professor, academic advisor, or the Undergraduate Associate Dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties, check Guidelines for Assessment of Penalties.

Grievances and Appeals

A student who believes that a decision affecting some aspect of his / her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70: Student Petitions and Grievances, Section 4. When in doubt please contact your Undergraduate Advisor for details.

A decision made or penalty imposed under Policy 70 – Student Petitions and Grievances (other than a petition) or Policy 71 – (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals).

Religious Observances

Student needs to inform the instructor at the beginning of term if special accommodation needs to be made for religious observances that are not otherwise accounted for in the scheduling of classes and deliverables.


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