From the closing keynoteLook back at the last two years of PyData, discussion about Python's role in the growing and changing data analytics landscape, and encouragement of ways to grow the community
30. PyData: The First 2 Years
• Oct 2012: First PyData Conf, NYC
!
• March 2013: PyData SV (PyCon)
• July 2013: PyData Boston (Microsoft)
• Oct 2013: PyData NYC (JP Morgan)
!
• Feb 2014: PyData UK (Level39)
• May 2014: PyData SV (Facebook)
• July 2014: PyData Berlin (EuroPython)
• October 2014: NYC (Strata NYC)
!
• October 2014: NYC (YOUR COMPANY HERE)
39. Way Way Back
• python: 1989-1991
• v1.0: 1994
• “ABC, SETL…
40. Way Way Back
• python: 1989-1991
• v1.0: 1994
• “ABC, SETL…
…That would appeal to UNIX/C hackers”
41. Way Way Back
• python: 1989-1991
• v1.0: 1994
• “ABC, SETL…
…That would appeal to UNIX/C hackers”
$ conda create -n py10 python=1.0
42. Way Way Back
• python: 1989-1991
• v1.0: 1994
• “ABC, SETL…
…That would appeal to UNIX/C hackers”
http://continuum.io/blog/python-1.0
$ conda create -n py10 python=1.0
43. Way Way Back
It is interactive, structured, high-level, and intended
to be used instead of BASIC, Pascal, or AWK.
!
It is not meant to be a systems-programming
language but is intended for teaching or prototyping.
44.
45.
46.
47.
48. “In June [1960] we were
introduced to this tall
college kid that always
signed his name with
lowercase letters. He was
don knuth
…
don claimed that he could
write the [Algol] compiler
and a language manual all
by himself during his three
and a half month summer
vacation.”
52. http://tuulos.github.io/sf-python-meetup-sep-2013/#/
“One of the most exciting features in
development is the Numba-based UDF
compiler. Building UDFs for Impala
currently requires writing C++ or Java
code and registering them manually with
the cluster. Writing C++/Java code is
more difficult, time-consuming, and error-
prone for many data analysts.”
http://blog.cloudera.com/blog/2014/04/a-new-python-client-for-impala/
55. Glue 2.0
Python’s legacy as a powerful glue
language
• manipulate files
• call fast libraries
!
Next-gen Glue:
• Link data silos
• Link disjoint memory & compute
• Unify disparate runtime models
• Transcend legacy models of
computers
56. Hard Problems in Data Science
Lots of data
Messy data
Noisy data
57. Hard Problems in Data Science
Lots of data
Messy data
Noisy data
Lots of computers
Lots of tools
Lots of hacking
58. Hard Problems in Data Science
Lots of data
Messy data
Noisy data
Lots of computers
Lots of tools
Lots of hacking
More questions
More data
More people
59. The Hype & The Opportunity
“Internet Revolution” True Believer, 1996:
Businesses that build network capability into their core will
outcompete and destroy their competition.
60. The Hype & The Opportunity
“Internet Revolution” True Believer, 1996:
Businesses that build network capability into their core will
outcompete and destroy their competition.
“Data Revolution” True Believer, 2014:
Businesses that build data comprehension into their core will
destroy their competition over the next 5-15 years.
61. The Hype & The Opportunity
“Internet Revolution” True Believer, 1996:
Businesses that build network capability into their core will
outcompete and destroy their competition.
“Data Revolution” True Believer, 2014:
Businesses that build data comprehension into their core will
destroy their competition over the next 5-15 years.
(1993 == 2011?)
69. 2013 Data Science Salary Survey!
http://www.oreilly.com/data/free/stratasurvey.csp
70.
71. “Python is the second best language…”
...Because it blurs the lines between “user” and “maker”.
!
We stand on the shoulders of Users who became Makers.
!
Some people say: “R has a very strong user community.”
!
I want people to say that “Python has a strong maker community.”
74. Standing Tall
• Science: Standing on the shoulders of giants
• Programming: Standing on each others toes
75. Standing Tall
• Science: Standing on the shoulders of giants
• Programming: Standing on each others toes
• But in Python, we stand on each others’
shoulders - community that bootstraps itself
76. “For there is but one veritable problem -
the problem of human relations…”
—Antoine de Saint-Exupéry
85. Participate
• Submit issues and pull requests
• Represent for the tools you love in social
media conversations
• Start PyData meetups
• Come to PyData conferences and present
• Encourage diversity!!
86. How did we get here?
• Hard Work
• By a community of people
• Who cared
• About code and people
87. Where do we go from here?
• More hard work
• More community
• More caring
• More code
• More people
Python is not just glue.
Python and PyData are communities!
88. Where do we go from here?
• More hard work
• More community
• More caring
• More code
• More people
Python is not just glue.
Python and PyData are communities!