social science

Data-Intensive Social Science Challenge Symposium

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Data-intensive social science is one of the research focus areas that MIDAS supports with its Challenge Awards. Our long-term goal is to support this research area more broadly, using the Challenge Award projects as the starting point to build a critical mass. This symposium offers a platform for all participants to explore collaboration opportunities and aims to attract more researchers to our hub. The two Challenge Award teams will give in-depth presentations, and all participants are encouraged to submit posters on research related to data-intensive social science.

Registration | Poster submission form (Due Monday, Sept. 10)


9 am: Introduction

9:05 am to 11:30 am: Challenge Award presentations

11:30 am to 11:45 am: Break

11:45 am to 12:45 pm: Panel discussion: the future of data-intensive social science research at U-M

  • Martha Bailey, Professor, Economics, University of Michigan
  • Sara Heller, Assistant Professor, Economics, University of Michigan
  • Matthew Shapiro, Professor, Economics, University of Michigan
  • Lisa Singh, Professor, Computer Science, Georgetown University
  • Mike Traugott, Professor Emeritus, Communication Studies, Political Science, University of Michigan

12:45 pm to 2 pm: lunch, poster session and networking (Please fill out this form to submit a poster; deadline is Monday, September 10; poster size can be up to 4 ft high, 6 ft wide)

PyData December Meetup: Drew Fustin, PhD, SpotHero

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Join us for a PyData Ann Arbor Meetup on Thursday, December 7, at 6 PM, hosted by TD Ameritrade and MIDAS.

Although ideal experimental design involves hypothesis testing with randomized controlled trials on concurrent populations to minimize selection bias and convoluting variables, it often arises that experiments cannot be run on variant populations simultaneously — for instance, when a stimulus necessarily impacts the entire population at a specific point in time (as in measuring a non-digital ad campaign’s effectiveness). Dealing with these situations is common in the social sciences, where the method of Interrupted Time Series Analysis is commonly used. In order to measure the effect size of a stimulus in situations such as these, we have to consider many convoluting factors caused by our populations being non-concurrent before deriving meaning from the experimental results.

In this Python-based tutorial, we will walk through a Monte Carlo-generated experiment measuring the lift induced by a simulated ad campaign. By the end of this tutorial, you will understand many complicating factors that could lead to faulty conclusions being drawn from the experimental results. However, you will also learn how best to design experiments and interpret results to mitigate these risks and take proper account of irreducible convoluting factors.

About: Drew Fustin is a former physicist and current data scientist in Chicago. He created and led the data science organization at SpotHero, focusing primarily on optimizing acquisition marketing spend and balancing supply and demand to generate inventory and rate recommendations. He’s also worked for GrubHub as the insights analyst, turning food facts into media content for the PR department and transforming data into actionable initiatives within the organization. He was also a data scientist with Digital H2O, a SaaS startup providing water intelligence for the oil/gas industry. He holds a PhD in physics from the University of Chicago, where he studied dark matter by looking for tiny bubbles in a chamber over a mile underground in a Canadian nickel mine.

PyData Ann Arbor is a group for amateurs, academics, and professionals currently exploring various data ecosystems. Specifically, we seek to engage with others around analysis, visualization, and management. We are primarily focused on how Python data tools can be used in innovative ways but also maintain a healthy interest in leveraging tools based in other languages such as R, Java/Scala, Rust, and Julia.

PyData Ann Arbor strives to be a welcoming and fully inclusive group and we observe the PyData Code of Conduct. PyData is organized by NumFOCUS.org, a 501(c)3 non-profit in the United States.

“use what you have learned to make something better and share with others”

Building a Community of Social Scientists with Big Data Skills: The ICOS Big Data Summer Camp

By | Educational, Feature, General Interest, News

As the use of data science techniques continues to grow across disciplines, a group of University of Michigan researchers are working to build a community of social scientists with skills in Big Data through a week-long summer camp for faculty and graduate students.

Having recently completed its fourth annual session, the Big Data Summer Camp held by the Interdisciplinary Committee for Organizational Studies (ICOS) trains approximately 50 people each spring in skills and methods such as Python, SQL, and social media APIs. The camp splits up into several groups to try to answer a research question using these newly acquired skills.

Working with researchers from other fields is a key component of the camp, and of creating a Big Data social science community, said co-coordinator Todd Schifeling, a Research Fellow at the Erb Institute in the School of Natural Resources and Environment.

“Students meet from across social science disciplines who wouldn’t meet otherwise,” said Schifeling. “And every year we bring back more and more past campers to present on what they’ve been doing.”

Schifeling himself participated in the camp as a student before taking on the role of coordinator this year.

Teddy DeWitt, the other co-coordinator of the camp and a doctoral student at the Ross School of Business, added the camp presents the curriculum in a unique way relative to the rest of campus.

“This set of material does not seem to be available in other parts of the university, at least … with an applied perspective in mind,” he said. “So we’re glad we have this set of resources that is both accessible and well-received by students.”

Participants range in skill from beginning to advanced, but even a relatively advanced student like Jeff Lockhart, a doctoral student in sociology and population studies who describes himself as “super-committed to computational social science,” said that it’s hard to find classes in computational methods in social science departments.

“[The ICOS camp] doesn’t expect a lot of prior knowledge, which I think is critical,” Lockhart said.

Lockhart, DeWitt, and Dylan Nelson, also a sociology doctoral student, are working on setting up a series of workshops in Computational Social Science for fall 2016 (contact Lockhart at jwlock@umich.edu for more information). Lockhart said it’s critical that social scientists learn Big Data skills.

“If we don’t have skills like this, there’s no way for us to enter into these fields of research that are going to be more and more important,” he said.

“A lot of the skills we’ve learned are sort of the on-ramp for doing data science,” DeWitt added.

The camp is co-sponsored by Advanced Research Computing (ARC).

Digging Into Data Challenge seeks data science projects in social science and humanities — June 29 deadline

By | Funding Opportunities, General Interest, News

The Digging Into Data Challenge, which aims to address how “big data” changes the research landscape for the humanities and social sciences, is seeking submissions for its fourth found of funding.

Digging into Data is a grant program sponsored by several leading research funders from around the world (see each round below). Teams of researchers from at least two different participating countries send in grant applications. These applications are reviewed by an international peer review panel.

Examples of the titles of previous grant winners include:

  • Automating Data Extraction from Chinese Texts
  • Digging Archaeology Data: Image Search and Markup
  • Field Mapping: An Archival Protocol for Social Science Research Findings.

For more information on the program, see its website. For information on applying, see the Application Materials page. The deadline is June 29, 2016