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Python has become the lead instrument for data scientists to collect, clean, and analyze data. As a general-purpose programming language, Python is flexible and well-suited to handle large datasets. This workshop is designed for social scientists, who are interested in using Python but have no idea where to start. Our goal is to “demystify” Python and to teach social scientists how to manipulate and examine data that deviate from the clean, rectangular survey format. This workshop is intended for social scientists who are new to programming. No experience required.
In this workshop, participants will examine a set of visualizations created by a team of faculty, librarians and academic specialists at Michigan State University. Using Michigan State University Library (MSUL) library data, this group can be utilized to explore questions of community and identity in comics culture. Utilizing the MSUL dataset, we will use Flourish to create visualizations that shed light on the patterns linked to comic publishing in the United States. Participants will leave the workshop with a better understanding of how to prepare data, model it in Flourish, and how to access pre-existing datasets here and elsewhere that work with Flourish.
What is the narrative of comic book history in the United States? For some comic scholars, a canon defined by themes such as trauma, memory, and autobiography defines the use way that comics provide particular insight on popular culture. Whatever these debates about comic canon, the form offers an important opportunity. Comic history is also urban history. Comics have played a central role in shaping our collective understanding of urban life. As visual narrative informed by questions of community, consumption, and identity, the comic medium offers an opportunity to think deeply about how the perception and the reality of urban life evolve through comic pages. In this presentation, Julian Chambliss will discuss the potential benefits offered by Collection as Data project developed by a Michigan State University workgroup using Michigan State University Library (MSUL) library metadata. What narratives of comics and community does such a dataset offer to scholars? How can these narratives engage students and scholars to create a greater understanding of comics and culture in the United States? This talk will highlight some potential pathways offered by comic book cities as windows on a wider urban imaginary in the United States.
Stephen S. Mills is an award-winning LGBTQ poet who is a native of Richmond, Indiana. Travis Rountree, assistant professor of English and director of the Writing Program invited Stephen to IU East to read some of his poetry that often refers back to the region. Stephen also visited Dr. Rountree’s Eng-W270 class to talk to the students about growing up in Richmond, how he came out to his family and found his identity, and what inspires him as a writer.
Talking about the needs of IU East at 15 years. Commencement speech for 1986. Also includes campus campaign speeches from Arthur Vivian and Dick Bodiker.
G. Elliott Morris is a data journalist at The Economist and writes mostly about American politics and elections, usually by engaging in a close study of political science, political polling and demographic data. He is responsible for many of The Economist’s election forecasting models, including their 2020 US presidential election forecast.
Textual data are central to the social sciences. However, they often require several pre-processing steps before they can be utilized for statistical analyses. This workshop introduces a range of Python tools to clean, organize, and analyze textual data. It is intended for researchers who are new to working with textual data, but are familiar with Python or have completed the Introduction to Python workshop. Python is best learned hands-on. Python packages: nltk, fuzzywuzzy, re, glob, sklearn, pandas, numpy, matplotlib
Over the past couple of decades, technical models, both statistical, machine learning and combinations of these methods, for forecasting various forms of political conflict, including protest, violent substate conflict, and even coups, have become surprisingly common in policy and NGO communities, particularly in Europe, though not, curiously, in US academia. These methods, working with readily available, if noisy, open source data, use a number of familiar predictive analytical approaches such as logit models in the statistical realm and random forests in the machine learning, and consistently outperform human analysts. This talk will first review the current state of the field, with a particular emphasis on why current models work whereas prior to 2005 there was little consistent success with the problems, and then present some challenges that remain unresolved. The talk will assume familiarity with general social science quantitative approaches, but not with the details of specific technical approaches: lots of graphics, a couple tables, no equations.
In recent years, social scientists have increased their efforts to access new datasets from the web or from large databases. An easy way to access such data are Application Programming Interfaces (APIs). This workshop introduces techniques for working with APIs in Python to retrieve data from sources such as Wikipedia or The New York Times. It is intended for researchers who are new to working with APIs, but are familiar with Python or have completed the Introduction to Python workshop. Python is best learned hands-on. To side step any issues with installation, we will be coding on Jupyter Notebooks with Binder. This means that participants will be able to follow along on their machines without needing to download any packages or programs in advance. We do recommend requesting a ProPublica Congress API key in advance (https://www.propublica.org/datastore/api/propublica-congress-api). This allows participants to run the API script on their own machines.
Helge-Johannes Marahrens is a doctoral student in the department of Sociology at Indiana University. He recently earned an MS in Applied Statistics and is currently working toward a PhD in Sociology. His research interests include cultural consumption, stratification, and computational social science with a particular focus on Natural Language Processing (NLP). Anne Kavalerchik is a doctoral student in the departments of Sociology and Informatics at Indiana University. Her research interests are broadly related to inequality, social change, and technology.
Virtual book event held on October 26, 2020 featuring librarian and author Megan Rosenbloom as she discusses her new book, Dark Archives: A Librarian’s Investigation into the Science and History of Books Bound in Human Skin. The event was cosponsored by the Indiana University School of Medicine’s Ruth Lilly Medical Library and the Indiana Medical History Museum.
