data science: past, present, and future

data science: past/present/future

1962-2062

chris@hackNY.org @chrishwiggins

references: http://bit.ly/datascience-links

1 hackNYDS.key - Thursday:June.18

“data science” jobs, jobs, jobs

“data science” d. conway, 2010

“data science” blogs, blogs, blogs

modern history:2009

The first time I heard "data science" was in 2007 while reading a proposal that my adviser had passed along, outlining an academic program similar to what we think of as data science.

“data science” ancient history: 2001

data science context

home schooled

PhD in topology

“By the end of late 1945, I was a statistician rather than a topologist”

invented: “bit”

invented: “software”

invented: “FFT”

“the progenitor of data science.” - @mshron

“The Future of Data Analysis,” 1962John W. Tukey

introduces: “Exploratory data anlaysis”

Tukey 1965, via John Chambers

TUKEY BEGAT S WHICH BEGAT R

Tukey 1972

? 1972

Jerome H. Friedman

TUKEY BEGAT ESL

Tukey 1975

In 1975, while at Princeton, Tufte was asked to teach a statistics course to a group of journalists who were visiting the school to study economics. He developed a set of readings and lectures on statistical graphics, which he further developed in joint seminars he subsequently taught with renowned statistician John Tukey (a pioneer in the field of information design). These course materials became the foundation for his first book on information design, The Visual Display of Quantitative Information

TUKEY BEGAT VDQI

Tukey 1977

TUKEY BEGAT EDA

fast forward -> 2001

“The primary agents for change should be university departments themselves.”

data science @ The New York Timesand how a 164-year old content company became data-driven

biology: 1892 vs. 1995

biology changed for good.

data science: mindset & toolset

drew conway, 2010

news: 20th century

church state

church

news: 20th century

church state

news: 21st century

church state

engineering

1851 1996

newspapering: 1851 vs. 1996

example:

millions of views per hour2015

data science: the web

is your “online presence”

is a microscope

is an experimental tool

is an optimization tool

1851 1996

newspapering: 1851 vs. 1996 vs. 2008

“a startup is a temporary organization in search of a repeatable and scalable business model” —Steve Blank

every publisher is now a startup

news: 21st century

church state

engineering

news: 21st century

church state

engineering

learnings

- supervised learning- unsupervised learning- reinforcement learning

learnings

- supervised learning- unsupervised learning- reinforcement learning

cf. modelingsocialdata.org

supervised learning, e.g.,

“the funnel”

interpretable supervised learning

unsupervised learning, e.g,

“segments”

argmax_z p(z|x)=14

“segments”

“baby boomer”

reinforcement learning

aka “A/B testing”;RCT

Reporting

Learning

Testaka “A/B testing”;

business as usual

(esp. supervised)

Some of the most recognizable personalization in our service is the collection of “genre” rows. …Members connect with these rows so

well that we measure an increase in member retention by placing the most tailored rows higher on the page instead of lower.

real-time A/B -> “bandits”

GOOG blog:

Reporting

Learning

Optimizing

Exploreunsupervised:

supervised:

reinforcement:

Reporting

Learning

Optimizing

Exploreunsupervised:

supervised:

reinforcement:

common requirements in data science:

1.people2.ideas3.things

cf. USAF

things:what does DS team deliver?

- build data prototypes- build APIs- impact roadmaps

- build data prototypes

cf. daeilkim.com

- build APIs

- impact roadmaps

flickr/McJex

data science: ideas

data skills

- data engineering- data science- data visualization- data product- data multiliteracies- data embeds

cf. “data scientists at work”, ch 1

data science: people

- new mindset > new toolset

summary:pay attention to:

1.people2.ideas3.things

cf. USAF

wait i want to learn more stuff

githubs ESL

play w/data

githubs

play w/data

a “book”

githubs ESL

play w/data

data science: past/present/future

1962-2062

chris@hackNY.org @chrishwiggins

references: http://bit.ly/datascience-links

“popular” jobs, jobs, jobs

data science: past, present, and future

data science blogs

data science jobs

data science context

progenitor of data science

data science ancient

future of data analysis

exploratory data anlaysis

tukey begat s

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