INSTRUCTIONS TO REQUEST REGISTRATION FOR 2018 WINTER SOCIAL SCIENCE WORKSHOPS. OPENS 9:00 AM FEBRUARY 15.
Address: Weiser Hall Room 747 500 Church St Ann Arbor, MI 48109-1042Contact us at (734)763-3301 or firstname.lastname@example.org
YOU WILL BE NOTIFIED WITHIN 24 HOURS IF YOUR REGISTRATION HAS BEEN SUCCESSFUL. IF YOU WERE NOT ONE OF THE FIRST 30 IN A WORKSHOP TO REGISTER - YOU WILL BE PLACED ON A WAIT LIST IN ORDER. IF THIS FORM STATES THAT A WORKSHOP IS FULL, IT IS LIKELY FULL WITH A SMALL WAITING LIST SO CHOOSE ANOTHER. IF IT IS THE ONLY ONE YOU ARE INTERESTED IN, YOU CAN GO AHEAD AND CHOOSE IT AND BE ADDED TO THE WAITLIST.
Please note that there is no 'optimal placement' algorithm used. Indicating interest in alternate workshops will not reduce your chance of getting in to your first choice. Spaces will be awarded on a first come first served basis so long as a diversity of departments/units are represented. By indicating a second and/or third choice you may be notified if there is a spot available to attend the other workshop. (Conversely, if your first choice is filled before you register, you may be offered a spot in another workshop right away).
***Participation for the Winter Series is limited to: Faculty, Staff, Postdoctoral Fellows and Graduate Students***
FOR MARCH 9 AND MARCH 16 - KIERAN HEALY AND JAKE HOFMAN: Please come to the workshop with a working version of R installed, and basic knowledge of the syntax. Absolute beginners to R are encouraged to work through an online tutorial to familiarize themselves with the interface and basic syntax (e.g., https://www.datacamp.com/courses/free-introduction-to-r and https://www.datacamp.com/courses/intermediate-r).
FOR APRIL 6 - AMIT SHARMA WORKSHOP: Please come to the workshop with a working version of Python installed. Absolute beginners to Python are encouraged to work through an online tutorial to familiarize themselves with the interface and basic syntax. We recommend https://www.datacamp.com/courses/intro-to-python-for-data-science and (optionally) https://www.datacamp.com/courses/intermediate-python-for-data-science
WINTER 2018 SERIES
Friday, March 9 (Full)
Friday, March 16 (Full)
Friday April 6
9:00 am - 5:00 pm
Room 747 Weiser Hall
There is no cost to attend
REGISTRATION IS REQUIRED
Lunch is provided
New for the Winter Series:
Each Workshop is absolutely Limited to 30 persons due to room capacity. All workshops will take place in Room 747 Weiser Hall.
Any one person may only attend ONE of the three workshops to ensure that a maximum amount of CSS devotees have an opportunity to participate. (If there is space available in any workshop closer to workshop date, an opportunity to attend another workshop may arise.)
Each workshop will be a full day event 9 am - 5:00 pm. You must be able to participate all day to register.
You will receive a confirmation of registration within 24 hours - to confirm your placement in a workshop.
There will be a community networking lunch where random groups will have an opportunity to chat in various locations throughout the 6th and 7th floors of Weiser Hall. The lunch is an integral part of the growth of the community of scholars interested in CSS and we highly encourage you to attend.
SEE INSTRUCTOR AND WORKSHOP DESCRIPTIONS BELOW.
FRIDAY, MARCH 9, 2018 (This workshop is full)
"Data Visualization for Social Science"
KIERAN HEALY Duke, Department of Sociology
Workshop Description: This workshop is a hands-on introduction to the principles and practice of data visualization, using R and ggplot. We will review some basic principles, discussing how good visualizations are rooted in understanding the way we perceive properties like length, absolute and relative size, and color. We will learn how to make and refine plots using ggplot's "grammar of graphics", a powerful framework for producing high-quality visualizations in a coherent and reproducible way.
FRIDAY, MARCH 16, 2018 (This workshop is full)
"An Introduction to Machine Learning for Social Scientists"
JAKE HOFMAN Microsoft Research, New York City
Workshop Description: This workshop will introduce participants to key ideas from machine learning, with a focus on how predictive modeling can be used to advance social science research. We will discuss how to fit and evaluate models so as to adequately balance explanatory and predictive power, covering concepts such as overfitting, cross-validation, and regularization. Participants will spend time
working through examples in R.
FRIDAY, APRIL 6, 2018
"Causal inference in online systems: Methods, pitfalls and best practices"
AMIT SHARMA Microsoft Research, India
From recommending what to buy, which movies to watch, to selecting the news to read, the people to follow and jobs to apply for, online systems have become an important part of our daily lives. A natural question to ask is how these socio-technical systems impact our behavior. However, because of the intricate interplay between the outputs of these systems and people's actions, identifying their impact on people's behavior is non-trivial.
Fortunately, there is a rich body of work on causal inference that we can build on. In the first part of the tutorial, I will show the value of counterfactual reasoning for studying socio-technical systems, by demonstrating how predictive modeling based on correlations can be counterproductive. Then, we will discuss different approaches to causal inference, including randomized experiments, natural experiments such as instrumental variables and regression discontinuities, and observational methods such as stratification and matching. Throughout, we will try to make connections with graphical models, machine learning and past work in the social sciences.
The second part of the tutorial will be more hands-on. We will work through a practical example of estimating the causal impact of a recommender system, starting from simple to more complex methods. The goal of the practical exercise will be to appreciate the pitfalls in different approaches to causal reasoning and take away best practices for doing causal inference with messy, real-world data.
FYI - FALL 2017 WORKSHOP INFO SHOWN BELOW
FALL 2017 SERIES
Please note that, as part of each workshop, we will be serving a lunch on the 10th floor of Weiser Hall. We do hope you will take advantage of this opportunity to chat with fellow travelers in Computational Social Science.
- 9:00 am - 5:00 pm
- Nov. 3 & Dec 1 - 10th Floor Weiser Hall
- Nov 10 7th Floor Room 747 Weiser Hall
- There is no cost to attend
- REGISTRATION IS REQUIRED
Each workshop will be limited to 30 participants and will be a full day event 9 am - 5:00 pm. You must be able to participate all day to register. *In order to ensure that a broad array of disciplines are represented, registration will be limited during the first week to only 3-5 people from each Dept. The week after registration opens, any unreserved spots will opened up to registrants from any department. You will receive confirmation of registration after you register if there is space.
SEE INSTRUCTOR AND WORKSHOP DESCRIPTIONS BELOW.
FRIDAY, NOVEMBER 3, 2017
"Bayesian statistics for social scientists"
DR KEVIN QUINN University of Michigan - Department of Political Science
This workshop will introduce participants to some of the key ideas behind Bayesian inference while highlighting strengths and weaknesses of Bayesian approaches to some practical problems involving missing data, latent variables, and prediction. Computing will be a special focus and participants will spend time working through examples written in R.
FRIDAY, NOVEMBER 10, 2017
"Communities, hierarchies, and cores: finding large-scale structure in complex networks"
DR DANIEL LARREMORE University of Colorado Boulder - Dept. of Computer Science & BioFrontiers Institute
This workshop will introduce the key concepts and techniques for finding and making sense of large-scale structure in complex networks. In addition to theory for community detection, hierarchy extraction, and core-periphery identification, we'll address a number of real-world applications, including social, genetic, and ecological networks. The workshop's methods will be applicable to any coding environment, with dedicated time to techniques in Python.
FRIDAY, DECEMBER 1, 2017
"Text as Data"
DR BRANDON STEWART Princeton University - Dept of Sociology
This workshop will cover the use of Text as Data in social science research. We will cover three key tasks that text analysis tools can help with: discovery, measurement and causal inference. The workshop will cover both some of the theory and methods for text analysis in the social sciences as well as hands-on experience applying these techniques in R.