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SUMMARY:The Web Is All You Need: A Data Analysis Stack for the 2020s
DESCRIPTION:For most of the last decade digital humanists doing data analysis have chosen between R and Python. But in the past few years\, the Javascript ecosystem has blossomed in a way that makes it a viable–and dare I say\, fun–way to collaboratively share\, explore\, and analyze data. Students don’t need to install anything to start a lesson\, collaborators can work directly with you in real time\, and anyone who can code can easily add interactive sliders\, controls to unlock datasets for others. \nThis workshop will provide an introduction to building and sharing data analysis for and of the web using observable notebooks. We’ll explore the basic platform\, and how it improves on python- or r-based notebooks you may have used; introduce the arquero and vega-lite packages that allow data manipulation and visualization; and talk about some strategies for making even the largest datasets explorable to anyone with a smartphone.
URL:https://nycdh.org/dhweek/event/the-web-is-all-you-need-a-data-analysis-stack-for-the-2020s/
LOCATION:Online\, New York\, NY\, United States
CATEGORIES:2022,Data Analysis,Mapping,Visualization,Workshop
ORGANIZER;CN="Ben Schmidt":MAILTO:bmschmidt@gmail.com
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DTSTART;TZID=America/New_York:20200207T100000
DTEND;TZID=America/New_York:20200207T120000
DTSTAMP:20260511T130006
CREATED:20200121T164431Z
LAST-MODIFIED:20200123T164710Z
UID:4077-1581069600-1581076800@nycdh.org
SUMMARY:Starting to Text Mine the Digitized Library with HathiTrust Features.
DESCRIPTION:Millions of books have been digitized in the past two decades. Thanks to a 2014 court ruling\, about 15 million books are available for computational analysis in the HathiTrust including data about word counts on each individual page. In the next year or two\, similar data will become available for JStor and Portico books. This session will address the following issues necessary for working with this dataset. \n1. What books have been scanned\, and which ones end up in Hathi?\n2. How do you build up a list of Hathi volumes to address a feature set?\n3. How do you acquire and work with Hathi’s “Feature Count” data programmatically?\n4. What sort of questions can you answer with these word counts\, anyway? \nEquipment Requirements: Laptop or high-powered tablet.\nPrerequisites: None; this session will generally be at a high enough level that it should be useful for those who wish to supervise research programmers rather than do it directly. Those with basic programming experience who wish to use it in the workshop should consider installing the ‘htrc-feature-counts’ module (for python) or the ‘hathidy’ package (for R).
URL:https://nycdh.org/dhweek/event/starting-to-text-mine-the-digitized-library-with-hathitrust-features/
LOCATION:Pace University\, Babble Lab\, Rm. 202\, 41 Park Row\, New York\, NY\, 10038\, United States
CATEGORIES:Intermediate,Text Analysis
ORGANIZER;CN="Ben Schmidt":MAILTO:bmschmidt@gmail.com
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DTSTART;TZID=America/New_York:20180207T100000
DTEND;TZID=America/New_York:20180207T120000
DTSTAMP:20260511T130006
CREATED:20180122T194248Z
LAST-MODIFIED:20180123T184557Z
UID:1695-1517997600-1518004800@nycdh.org
SUMMARY:Thinking Through Word Embeddings
DESCRIPTION:Word embeddings are a family of algorithms that can be remarkably effective at representing the meanings of words\, and their relationships to each other. We’ll cover the basics of word embeddings: what they do\, how to train a model using word2vec\, and how to use them to search for synonyms and analogies. And we’ll look at issues more specific to the humanities and social sciences\, including how to compare models trained on different sets of texts to each other\, when to use word2vec vs topic models\, and strategies for visualizing models. Finally\, we’ll talk about the social biases embodied in the space of language models\, both as a technical problem with solutions and as an opportunity for algorithmic criticism. \nHands-on analysis and visualization will be done editing pre-written scripts in the R statistical environment; no prior programming experience is necessary. We’ll distribute several pre-trained models at the workshop\, but you can try to train one on your own texts ahead of time as well. \nLEVEL: Beginner\nNOTES: Laptop with R and Rstudio programs installed required. Instructions available.
URL:https://nycdh.org/dhweek/event/thinking-through-word-embeddings/
LOCATION:Babble Lab @ Pace University\, Room 1105\, 163 William St.\, New York\, NY\, 10038\, United States
CATEGORIES:Beginner,R,Statistics,Text Analysis,Visualization
ORGANIZER;CN="Ben Schmidt":MAILTO:bmschmidt@gmail.com
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