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How We Became Our Data: A Genealogy of the Informational Person, Koopman
"We are now acutely aware, as if all of the sudden, that data matters enormously to how we live. How did information come to be so integral to what we can do? How did we become people who effortlessly present our lives in social media profiles and who are meticulously recorded in state surveillance dossiers and online marketing databases? What is the story behind data coming to matter so much to who we are? In How We Became Our Data, Colin Koopman excavates early moments of our rapidly accelerating data-tracking technologies and their consequences for how we think of and express our selfhood today. Koopman explores the emergence of mass-scale record keeping systems like birth certificates and social security numbers, as well as new data techniques for categorizing personality traits, measuring intelligence, and even racializing subjects. This all culminates in what Koopman calls the “informational person” and the “informational power” we are now subject to. The recent explosion of digital technologies that are turning us into a series of algorithmic data points is shown to have a deeper and more turbulent past than we commonly think. Blending philosophy, history, political theory, and media theory in conversation with thinkers like Michel Foucault, Jürgen Habermas, and Friedrich Kittler, Koopman presents an illuminating perspective on how we have come to think of our personhood—and how we can resist its erosion."
book  publisher  statistics  data  sts  history 
6 weeks ago by tsuomela
Calculated Values — William Deringer | Harvard University Press
"Modern political culture features a deep-seated faith in the power of numbers to find answers, settle disputes, and explain how the world works. Whether evaluating economic trends, measuring the success of institutions, or divining public opinion, we are told that numbers don’t lie. But numbers have not always been so revered. Calculated Values traces how numbers first gained widespread public authority in one nation, Great Britain. Into the seventeenth century, numerical reasoning bore no special weight in political life. Complex calculations were often regarded with suspicion, seen as the narrow province of navigators, bookkeepers, and astrologers, not gentlemen. This changed in the decades following the Glorious Revolution of 1688. Though Britons’ new quantitative enthusiasm coincided with major advances in natural science, financial capitalism, and the power of the British state, it was no automatic consequence of those developments, William Deringer argues. Rather, it was a product of politics—ugly, antagonistic, partisan politics. From parliamentary debates to cheap pamphlets, disputes over taxes, trade, and national debt were increasingly conducted through calculations. Some of the era’s most pivotal political moments, like the 1707 Union of England and Scotland and the 1720 South Sea Bubble, turned upon calculative conflicts. As Britons learned to fight by the numbers, they came to believe, as one calculator wrote in 1727, that “facts and figures are the most stubborn evidences.” Yet the authority of numbers arose not from efforts to find objective truths that transcended politics, but from the turmoil of politics itself."
book  publisher  values  statistics  history  sts 
11 weeks ago by tsuomela
Storm -- A Modern Probabilistic Model Checker -- Home
"Storm is a tool for the analysis of systems involving random or probabilistic phenomena. Given an input model and a quantitative specification, it can determine whether the input model conforms to the specification. It has been designed with performance and modularity in mind. "
statistics  modeling 
november 2018 by tsuomela
Teaching Data for Statistics and Data Science • testDriveR
"The goal of testDriveR is to provide data sets for teaching statistics and data science courses. This package includes a sample of data from John Edmund Kerrich’s famous coinflip experiment. These are data that I use for teaching SOC 4015 / SOC 5050 at Saint Louis University. "
teaching  statistics  data-sources  r  package 
september 2018 by tsuomela
Topic Modeling in Python with NLTK and Gensim | DataScience+
"In this post, we will learn how to identify which topic is discussed in a document, called topic modeling. In particular, we will cover Latent Dirichlet Allocation (LDA): a widely used topic modelling technique. And we will apply LDA to convert set of research papers to a set of topics."
python  statistics  topic-modeling  digital-humanities  methods 
april 2018 by tsuomela
Big data: are we making a big mistake?
Very good description of the problems that big data claims to solve, but may not actually solve.
big-data  statistics  science 
march 2018 by tsuomela
All the fake data that's fit to print
charlatan is an R package for simulating / creating fake data
r  statistics  package  data  teaching 
june 2017 by tsuomela
Psychic Numbing and Genocide
"Most people are caring and will exert great effort to reserve "the one" whose needy plight comes to their attention. But these same people often become numbly indifferent to the plight of "the one" who is one of many in a much greater problem."
psychology  emotion  statistics  perception  genocide  tragedy 
june 2017 by tsuomela
Teaching Statistics: Resources for Undergraduate Instructors | Mathematical Association of America
"The title of the lead article in this volume, Teaching Statistics: More Data, Less Lecturing, summarizes succinctly the basic tenets of statistics educational reform of the past 10 to 15 years, tenets around which the statistics profession has formed a surprisingly strong and supportive consensus. This volume strives to be an instructors’ manual for this reform movement and will be essential reading for anyone at the undergraduate or secondary level who teaches statistics, especially for those new to the teaching of statistics. Behind this reform is the notion that statistics instruction should resemble statistical practice. Data lies at the heart of statistical practice and should thus form the center of instruction. Since most statistical practice involves issues of the collection, analysis, and interpretation of data, students should learn about and experience all three of these aspects continually in their learning. Teaching Statistics: Resources for Undergraduate Instructors presents a collection of class and original articles on various aspects of statistical education along with descriptions of innovation and successful projects. The volume provides complete descriptions of projects along with companion pieces written by teachers who have used the projects and can provide practical advice to readers on how to use projects effectively. Other sections include motivation for and advice on how to use real data in teaching, how to choose a textbook at the introductory or mathematical statistics level, how to make effective use of technology, and how to better assess students by going beyond the reliance on in-class examinations."
book  publisher  statistics  teaching  pedagogy 
may 2017 by tsuomela
CAUSEweb | Consortium for the Advancement of Undergraduate Statistics Education
"Consortium for the Advancement of Undergraduate Statistics Education A national organization whose mission is to support the advancement of undergraduate statistics education."
professional-association  statistics  teaching  pedagogy 
may 2017 by tsuomela
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