The document discusses the evolution of data, statistics, and technology from the 1770s to the present, highlighting their impact on social and political order. It emphasizes the historical influence of figures like Florence Nightingale on data visualization and the implications of data distribution on power dynamics. Additionally, it outlines the structure of a course that combines technical and humanistic approaches to data sciences.

1. Data
Past Present Future
Wiggins + Jones
SEAS + A&S
Lecture 1: Jan 17 2018

11. how did this end up in my news feed?
- math
- hardware
- system
- funding
- market
- regulation
- data
this was not possible 20 years ago.
- why?
- what did people do instead?

15. Session replay scripts

17. “In all cultures and all periods of
recorded human history, people
share the belief that the face alone
suffices to reveal innate traits of a
person.”

18. We’ve been here before

19. We’ve been here before

20. We’ve been here before

21. We’ve been here before
Insert quote from Blaise here

22. Statistical sciences always political
Dream of sciences of social difference
Central to development of
Statistics
And the
Data sciences

23. Florence Nightingale & Data Visualization
“Experience has shown that without
special information and skilful
application of the resources of
science in preserving health, the
drain on our home population must
exhaust our means. The
introduction, therefore, of a proper
sanitary system into the British army
is of essential importance to the
public interests.”

24. Florence Nightingale & Data Visualization
“Upon the British race alone the
integrity of that empire at this
moment appears to depend. The
conquering race must retain
possession.”

25. Every week
Scientific and mathematical development
Technologies and engineering
Driving forces: money, prestige, resources, Imperial competition
Power, ethics, and data intensive knowledge

26. Tech story: three chronological stages
Data and Math
Data and Engineering
Data and Technology

27. Data technologies
Census and government survey
Information processing machines and
digital computers
Always on network infrastructure

28. Power
How should social and political order be organized on basis
of science and engineering?
How do technologies transform the social and political order?
How do technologies augment and diminish democratic orders? Autocratic ones?

29. Power and politics*
New technologies mean new capabilities
These capabilities are first available to those in power
(cf., “The future is already here — it's just not very evenly distributed.” --Gibson)
How does this distribution of capability reorder power?
How are data-empowered algorithms an example of this dynamic
- of capability, and
- of reinforcing or distributing power?
* politics here meaning the dynamics of power, not to be confused with “voting”

30. Arc of class

31. Qualitative
statistics
“Vulgar”
statistics
Mathematical
statistics
Computation+
Crypto
“intelligence”
AI winter
AI renaissance
Machine
learning
1770s 1830s 1900 WWII 1960s 1980s 1990s You are here.
data 1770s-present: capabilities
Regression +
Hypothesis testing

32. Qualitative
statistics
“Vulgar”
statistics
Mathematical
statistics
Computation+
Crypto
“intelligence”
AI winter
AI renaissance
Machine
learning
1770s 1830s 1900 WWII 1960s 1980s 1990s You are here.
data 1770s-present: capabilities
Regression +
Hypothesis testing
huge breakpoint

33. Qualitative
statistics
“Vulgar”
statistics
Mathematical
statistics
Computation+
Crypto
“intelligence”
AI winter
AI renaissance
Machine
learning
1770s 1830s 1900 WWII 1960s 1980s 1990s You are here.
(statecraft)
(policy+eugenics)
(math-vs-science)
(data @ war)
(SIGINT)
(data wars (for funding))
(surveillance economy)
(defense+advertising)
Regression +
Hypothesis testing
data 1770s-present: capabilities & intents

34. Qualitative
statistics
“Vulgar”
statistics
Mathematical
statistics
Computation+
Crypto
“intelligence”
AI winter
AI renaissance
Machine
learning
1770s 1830s 1900 WWII 1960s 1980s 1990s You are here.
.gov
.edu
.mil
.ai
.com
Regression +
Hypothesis testing
data 1770s-present: capabilities & intents

35. Qualitative
statistics
“Vulgar”
statistics
Mathematical
statistics
Computation+
Crypto
“intelligence”
AI winter
AI renaissance
Machine
learning
1770s 1830s 1900 WWII 1960s 1980s 1990s You are here.
Regression +
Hypothesis testing
data 1770s-present: capabilities & intents
persistent observation:
dynamics of capabilities implies
dynamics of power

36. Qualitative
statistics
“Vulgar”
statistics
Mathematical
statistics
Computation+
Crypto
“intelligence”
AI winter
AI renaissance
Machine
learning
1770s 1830s 1900 WWII 1960s 1980s 1990s You are here.
Regression +
Hypothesis testing
data 1770s-present: capabilities & intents
persistent observation:
dynamics of capabilities implies
dynamics of power, i.e., is political

37. Qualitative
statistics
“Vulgar”
statistics
Mathematical
statistics
Computation+
Crypto
“intelligence”
AI winter
AI renaissance
Machine
learning
1770s 1830s 1900 WWII 1960s 1980s 1990s You are here.
Regression +
Hypothesis testing
data 1770s-present: capabilities & intents
(also, how did this end up in my news feed?)

38. How we know what the state of the state is

39. Regression, correlation and eugenics

40. Experimental design, hypothesis test, decision theory
To play this game with the greatest chance of
success, the experimenter cannot afford to
exclude the possibility of any possible
arrangement of soil fertilities, and his best
strategy is to equalize the chance that any
treatment shall fall on any plot by determining
it by chance himself.
-R.A. Fisher

41. World War 2: Turing and statistical cryptography

42. AI and its many winters

43. Pattern Recognition to . . .

44. … Machine Learning

45. Research ethics (and the lack thereof)

46. Research ethics (and the lack thereof)

47. Silicon Valley and the Attention Economy

48. De-anonymization

49. Weekly structure
Monday
Lecture and discussion
Expectation
arrive having done the week’s readings
Wednesday
Laboratory
Expectation
arrive with laptop ready to collaborate

50. No prerequisites

51. Two tracks
more technical background track (60%)
● pursue a semester long project
culminating in a 15pp paper and any
associated code
●
● complete 3 problem sets
●
● short final presentation on paper
more humanistic background track (60%)
● write a 10 pp paper on a topic of their
choice
●
● complete 5 problem sets, these problem
sets will involve both computational work
and writing work
●
● short final presentation on paper

52. Jupyter notebooks:
code without any coding
INSTALLING PYTHON + JUPYTER
USING JUPYTER NOTEBOOKS

53. Required work
Postings on readings in slack
Problem sets (extensions of lab work, done in Jupyter)
Final paper
Your presence

54. Hardest part of class: Registration
CC + Barnard + GS
History-APAM 2901 Call number: 72327
SEAS
Applied Math 82901 Call Number: 72327
A+S and Consortium Grad:
History GR6998 section #10 Call number: 86347