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yes<00:00:12.660><c> my</c><00:00:13.170><c> name</c><00:00:13.290><c> is</c><00:00:13.320><c> Ryan</c><00:00:13.620><c> Orban</c><00:00:14.040><c> from</c>

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yes my name is Ryan Orban from
 

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yes my name is Ryan Orban from
galvanized<00:00:15.089><c> where</c><00:00:15.389><c> I</c><00:00:15.420><c> serve</c><00:00:15.660><c> as</c><00:00:15.839><c> the</c><00:00:15.870><c> CTL</c><00:00:16.320><c> ah</c><00:00:24.260><c> I</c>

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galvanized where I serve as the CTL ah I
 

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galvanized where I serve as the CTL ah I
think<00:00:25.740><c> data</c><00:00:25.890><c> by</c><00:00:26.010><c> the</c><00:00:26.070><c> bay</c><00:00:26.279><c> is</c><00:00:26.609><c> a</c><00:00:27.060><c> great</c>

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think data by the bay is a great
 

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think data by the bay is a great
conference<00:00:28.369><c> we</c><00:00:29.369><c> love</c><00:00:29.730><c> hosting</c><00:00:30.060><c> it</c><00:00:30.330><c> here</c><00:00:30.449><c> at</c>

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conference we love hosting it here at
 

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conference we love hosting it here at
galvanize<00:00:30.810><c> a</c><00:00:31.170><c> big</c><00:00:31.619><c> part</c><00:00:31.890><c> of</c><00:00:32.040><c> our</c><00:00:32.219><c> mission</c><00:00:32.420><c> is</c>

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galvanize a big part of our mission is
 

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galvanize a big part of our mission is
to<00:00:33.750><c> help</c><00:00:34.050><c> transform</c><00:00:34.680><c> lives</c><00:00:34.920><c> through</c>

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to help transform lives through
 

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to help transform lives through
education<00:00:35.579><c> and</c><00:00:36.120><c> through</c><00:00:36.210><c> community</c><00:00:37.020><c> support</c>

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education and through community support
 

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education and through community support
and<00:00:37.790><c> are</c><00:00:38.790><c> one</c><00:00:39.510><c> of</c><00:00:39.600><c> our</c><00:00:39.719><c> main</c><00:00:39.870><c> programs</c><00:00:40.290><c> is</c><00:00:40.500><c> data</c>

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and are one of our main programs is data
 

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and are one of our main programs is data
science<00:00:41.280><c> and</c><00:00:41.430><c> data</c><00:00:41.489><c> engineering</c><00:00:41.930><c> instead</c><00:00:42.930><c> of</c>

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science and data engineering instead of
 

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science and data engineering instead of
being<00:00:43.230><c> able</c><00:00:43.320><c> to</c><00:00:43.440><c> support</c><00:00:43.950><c> and</c><00:00:44.250><c> host</c><00:00:44.550><c> a</c>

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being able to support and host a
 

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being able to support and host a
conference<00:00:45.239><c> like</c><00:00:45.570><c> this</c><00:00:45.600><c> bring</c><00:00:46.559><c> in</c><00:00:46.739><c> amazing</c>

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conference like this bring in amazing
 

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conference like this bring in amazing
lumination<00:00:47.460><c> fueled</c><00:00:48.440><c> Alexia's</c><00:00:49.440><c> amazing</c>

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lumination fueled Alexia's amazing
 

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lumination fueled Alexia's amazing
organizer<00:00:50.160><c> and</c><00:00:50.520><c> just</c><00:00:50.760><c> getting</c><00:00:51.030><c> all</c><00:00:51.719><c> of</c><00:00:51.870><c> these</c>

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organizer and just getting all of these
 

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organizer and just getting all of these
people<00:00:52.289><c> activated</c><00:00:53.250><c> on</c><00:00:53.340><c> our</c><00:00:53.460><c> campus</c><00:00:53.820><c> and</c>

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people activated on our campus and
 

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people activated on our campus and
getting<00:00:54.210><c> our</c><00:00:54.480><c> students</c><00:00:55.110><c> or</c><00:00:55.320><c> companies</c><00:00:55.949><c> our</c>

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getting our students or companies our
 

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getting our students or companies our
members<00:00:56.489><c> exposed</c><00:00:57.449><c> to</c><00:00:57.690><c> this</c><00:00:57.780><c> level</c><00:00:57.960><c> of</c><00:00:58.109><c> content</c>

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members exposed to this level of content
 

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members exposed to this level of content
is<00:00:58.680><c> super</c><00:00:59.250><c> great</c><00:00:59.430><c> so</c><00:00:59.760><c> we're</c><00:01:00.329><c> happy</c><00:01:00.570><c> to</c><00:01:00.660><c> support</c>

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is super great so we're happy to support
 

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is super great so we're happy to support
data<00:01:01.109><c> by</c><00:01:01.260><c> the</c><00:01:01.320><c> bay</c><00:01:01.530><c> we</c><00:01:02.280><c> have</c><00:01:02.430><c> for</c><00:01:02.850><c> such</c><00:01:03.000><c> for</c>

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data by the bay we have for such for
 

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data by the bay we have for such for
some<00:01:03.300><c> time</c><00:01:03.480><c> now</c><00:01:03.510><c> and</c><00:01:03.690><c> we're</c><00:01:04.409><c> even</c><00:01:04.559><c> talking</c>

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some time now and we're even talking
 

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some time now and we're even talking
about<00:01:04.979><c> potentially</c><00:01:05.519><c> having</c><00:01:05.670><c> a</c><00:01:05.820><c> greater</c><00:01:06.210><c> level</c>

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about potentially having a greater level
 

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about potentially having a greater level
of<00:01:06.510><c> collaboration</c><00:01:06.689><c> and</c><00:01:07.140><c> taking</c><00:01:07.439><c> this</c><00:01:07.530><c> not</c>

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of collaboration and taking this not
 

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of collaboration and taking this not
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you know it sounds a bit trite but you
 

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you know it sounds a bit trite but you
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know key portion of my talk was that
 

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know key portion of my talk was that
really<00:01:21.420><c> data</c><00:01:21.750><c> science</c><00:01:22.200><c> and</c><00:01:22.440><c> data</c><00:01:22.830><c> is</c><00:01:23.010><c> the</c>

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really data science and data is the
 

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really data science and data is the
future<00:01:23.810><c> when</c><00:01:24.810><c> we</c><00:01:24.900><c> think</c><00:01:25.080><c> about</c><00:01:25.730><c> the</c><00:01:26.730><c> new</c>

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future when we think about the new
 

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future when we think about the new
technologies<00:01:27.600><c> that</c><00:01:27.690><c> are</c><00:01:27.750><c> coming</c><00:01:28.020><c> out</c><00:01:28.110><c> either</c>

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technologies that are coming out either
 

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technologies that are coming out either
being<00:01:28.620><c> an</c><00:01:28.770><c> artificial</c><00:01:29.220><c> intelligence</c><00:01:29.670><c> or</c><00:01:29.910><c> deep</c>

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being an artificial intelligence or deep
 

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being an artificial intelligence or deep
learning<00:01:30.410><c> or</c><00:01:31.410><c> even</c><00:01:31.590><c> machine</c><00:01:31.830><c> learning</c><00:01:32.630><c> it's</c>

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learning or even machine learning it's
 

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learning or even machine learning it's
really<00:01:33.870><c> changing</c><00:01:34.470><c> the</c><00:01:34.590><c> economy</c><00:01:35.040><c> and</c><00:01:35.220><c> that</c>

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really changing the economy and that
 

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really changing the economy and that
every<00:01:35.670><c> company</c><00:01:35.850><c> now</c><00:01:36.210><c> is</c><00:01:36.360><c> a</c><00:01:36.390><c> tech</c><00:01:36.600><c> company</c><00:01:37.730><c> even</c>

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every company now is a tech company even
 

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every company now is a tech company even
from<00:01:39.120><c> you</c><00:01:39.510><c> know</c><00:01:39.600><c> construction</c><00:01:40.200><c> and</c>

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from you know construction and
 

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from you know construction and
manufacturing<00:01:40.470><c> to</c><00:01:41.310><c> what</c><00:01:41.670><c> you</c><00:01:41.760><c> would</c>

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manufacturing to what you would
 

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manufacturing to what you would
typically<00:01:42.150><c> think</c><00:01:42.990><c> about</c><00:01:43.260><c> as</c><00:01:43.560><c> leveraging</c><00:01:44.550><c> data</c>

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typically think about as leveraging data
 

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typically think about as leveraging data
science<00:01:45.060><c> i</c><00:01:45.120><c> can</c><00:01:45.240><c> move</c><00:01:45.330><c> or</c><00:01:45.510><c> Facebook</c><00:01:46.050><c> every</c>

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science i can move or Facebook every
 

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science i can move or Facebook every
company<00:01:47.580><c> is</c><00:01:47.640><c> having</c><00:01:47.790><c> to</c><00:01:48.120><c> answer</c><00:01:48.330><c> the</c><00:01:48.600><c> call</c><00:01:48.840><c> in</c>

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company is having to answer the call in
 

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company is having to answer the call in
order<00:01:49.650><c> to</c><00:01:49.770><c> be</c><00:01:49.830><c> more</c><00:01:49.890><c> competitive</c><00:01:50.100><c> and</c><00:01:50.640><c> to</c>

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order to be more competitive and to
 

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order to be more competitive and to
reduce<00:01:51.060><c> costs</c><00:01:51.540><c> and</c><00:01:51.660><c> so</c><00:01:51.960><c> I</c><00:01:51.990><c> think</c><00:01:52.260><c> this</c><00:01:52.830><c> is</c><00:01:53.010><c> one</c>

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reduce costs and so I think this is one
 

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reduce costs and so I think this is one
of<00:01:53.220><c> the</c><00:01:53.370><c> most</c><00:01:53.490><c> exciting</c><00:01:53.790><c> areas</c><00:01:54.180><c> to</c><00:01:54.360><c> be</c><00:01:54.510><c> in</c>

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of the most exciting areas to be in
 

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of the most exciting areas to be in
right<00:01:54.840><c> now</c><00:01:55.020><c> because</c><00:01:55.530><c> we're</c><00:01:55.680><c> seeing</c><00:01:55.860><c> a</c>

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right now because we're seeing a
 

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right now because we're seeing a
tectonic<00:01:56.280><c> shift</c><00:01:56.460><c> not</c><00:01:57.090><c> only</c><00:01:57.360><c> through</c><00:01:57.660><c> things</c>

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tectonic shift not only through things
 

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tectonic shift not only through things
like<00:01:58.740><c> you</c><00:01:59.040><c> know</c><00:01:59.070><c> self-driving</c><00:01:59.520><c> cars</c><00:01:59.850><c> and</c>

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like you know self-driving cars and
 

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like you know self-driving cars and
artificial<00:02:00.540><c> intelligence</c><00:02:01.430><c> but</c><00:02:02.430><c> you</c><00:02:02.760><c> know</c>

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artificial intelligence but you know
 

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artificial intelligence but you know
we're<00:02:03.240><c> going</c><00:02:03.750><c> to</c><00:02:04.020><c> see</c><00:02:04.170><c> a</c><00:02:04.200><c> pretty</c><00:02:04.800><c> big</c>

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we're going to see a pretty big
 

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we're going to see a pretty big
blanchett<00:02:05.370><c> moment</c><00:02:05.700><c> of</c><00:02:05.820><c> the</c><00:02:05.940><c> next</c><00:02:06.180><c> 10</c><00:02:06.270><c> to</c><00:02:06.360><c> 15</c>

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blanchett moment of the next 10 to 15
 

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blanchett moment of the next 10 to 15
years<00:02:06.830><c> the</c><00:02:07.830><c> most</c><00:02:08.160><c> you</c><00:02:09.090><c> know</c><00:02:09.179><c> populous</c><00:02:09.840><c> job</c><00:02:10.290><c> in</c>

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years the most you know populous job in
 

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years the most you know populous job in
the<00:02:10.649><c> country</c><00:02:10.920><c> and</c><00:02:11.010><c> I</c><00:02:11.100><c> was</c><00:02:11.220><c> truck</c><00:02:11.430><c> driving</c><00:02:11.820><c> and</c>

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the country and I was truck driving and
 

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the country and I was truck driving and
you<00:02:12.300><c> know</c><00:02:12.390><c> with</c><00:02:12.660><c> this</c><00:02:12.810><c> you</c><00:02:13.380><c> know</c><00:02:13.470><c> this</c><00:02:13.620><c> morning</c>

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you know with this you know this morning
 

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you know with this you know this morning
they<00:02:14.100><c> just</c><00:02:14.250><c> announced</c><00:02:14.730><c> a</c><00:02:14.820><c> thirty</c><00:02:15.060><c> thousand</c>

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they just announced a thirty thousand
 

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they just announced a thirty thousand
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dollar add on to a semi which makes it
 

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dollar add on to a semi which makes it
well<00:02:17.040><c> to</c><00:02:17.220><c> drive</c><00:02:17.430><c> to</c><00:02:17.550><c> Costa</c><00:02:17.910><c> to</c><00:02:18.000><c> host</c><00:02:18.210><c> unaided</c>

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well to drive to Costa to host unaided
 

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well to drive to Costa to host unaided
by<00:02:19.170><c> a</c><00:02:19.350><c> particular</c><00:02:19.860><c> human</c><00:02:20.300><c> and</c><00:02:21.300><c> so</c><00:02:21.600><c> this</c><00:02:22.500><c> is</c><00:02:22.590><c> one</c>

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by a particular human and so this is one
 

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by a particular human and so this is one
of<00:02:22.709><c> the</c><00:02:22.800><c> reasons</c><00:02:23.040><c> we</c><00:02:23.100><c> what</c><00:02:23.400><c> we</c><00:02:23.580><c> do</c><00:02:23.790><c> what</c><00:02:23.910><c> we</c><00:02:24.030><c> do</c>

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of the reasons we what we do what we do
 

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of the reasons we what we do what we do
is<00:02:24.530><c> providing</c><00:02:25.530><c> the</c><00:02:25.560><c> access</c><00:02:26.010><c> and</c><00:02:26.130><c> opportunity</c>

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is providing the access and opportunity
 

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is providing the access and opportunity
to<00:02:26.850><c> the</c><00:02:26.970><c> skills</c><00:02:27.240><c> knowledge</c><00:02:27.450><c> that</c><00:02:27.750><c> you</c><00:02:27.840><c> need</c><00:02:28.340><c> to</c>

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to the skills knowledge that you need to
 

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to the skills knowledge that you need to
not<00:02:29.490><c> only</c><00:02:29.760><c> be</c><00:02:30.060><c> working</c><00:02:30.600><c> with</c><00:02:30.720><c> the</c><00:02:30.840><c> AIS</c><00:02:31.140><c> but</c>

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not only be working with the AIS but
 

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not only be working with the AIS but
actually<00:02:31.650><c> programming</c><00:02:32.070><c> them</c><00:02:32.280><c> and</c><00:02:32.570><c> being</c><00:02:33.570><c> able</c>

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actually programming them and being able
 

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actually programming them and being able
to<00:02:33.870><c> understand</c><00:02:34.320><c> and</c><00:02:34.560><c> leverage</c><00:02:34.620><c> data</c><00:02:34.950><c> i</c><00:02:35.100><c> think</c>

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to understand and leverage data i think
 

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to understand and leverage data i think
is<00:02:35.370><c> a</c><00:02:35.400><c> big</c><00:02:35.610><c> component</c><00:02:35.730><c> of</c><00:02:36.060><c> that</c><00:02:36.120><c> and</c><00:02:36.420><c> just</c>

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is a big component of that and just
 

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is a big component of that and just
having<00:02:37.470><c> basic</c><00:02:37.830><c> data</c><00:02:38.040><c> literacy</c><00:02:38.280><c> of</c><00:02:38.640><c> knowing</c>

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having basic data literacy of knowing
 

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having basic data literacy of knowing
how<00:02:39.390><c> this</c><00:02:39.540><c> is</c><00:02:39.660><c> being</c><00:02:39.690><c> used</c><00:02:40.110><c> in</c><00:02:40.320><c> what</c><00:02:40.440><c> dan</c>

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how this is being used in what dan
 

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how this is being used in what dan
you're<00:02:40.709><c> generating</c><00:02:40.950><c> the</c><00:02:41.190><c> value</c><00:02:41.580><c> of</c><00:02:41.760><c> that</c><00:02:42.300><c> i</c>

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you're generating the value of that i
 

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you're generating the value of that i
think<00:02:42.660><c> is</c><00:02:43.440><c> going</c><00:02:43.890><c> to</c><00:02:43.950><c> be</c><00:02:44.010><c> really</c><00:02:44.250><c> important</c>

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think is going to be really important
 

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think is going to be really important
because<00:02:44.760><c> the</c><00:02:45.420><c> amount</c><00:02:45.810><c> of</c><00:02:45.930><c> data</c><00:02:46.080><c> is</c>

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because the amount of data is
 

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because the amount of data is
continuously<00:02:46.950><c> going</c><00:02:47.100><c> to</c><00:02:47.220><c> grow</c><00:02:47.370><c> its</c><00:02:48.090><c> growing</c>

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continuously going to grow its growing
 

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continuously going to grow its growing
an<00:02:48.390><c> exponential</c><00:02:48.870><c> rate</c><00:02:48.930><c> we're</c><00:02:49.230><c> all</c><00:02:49.380><c> carrying</c>

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an exponential rate we're all carrying
 

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an exponential rate we're all carrying
supercomputers<00:02:50.700><c> around</c><00:02:50.910><c> in</c><00:02:51.000><c> our</c><00:02:51.090><c> pockets</c>

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supercomputers around in our pockets
 

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supercomputers around in our pockets
right<00:02:51.570><c> now</c><00:02:51.770><c> and</c><00:02:52.770><c> they</c><00:02:52.890><c> might</c><00:02:53.010><c> have</c><00:02:53.100><c> data</c>

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right now and they might have data
 

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right now and they might have data
exhaust<00:02:53.550><c> that</c><00:02:53.820><c> we</c><00:02:53.940><c> have</c><00:02:54.090><c> is</c><00:02:54.300><c> only</c><00:02:54.450><c> going</c><00:02:54.570><c> to</c>

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exhaust that we have is only going to
 

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exhaust that we have is only going to
continue<00:02:54.720><c> to</c><00:02:55.050><c> grow</c><00:02:55.230><c> and</c><00:02:55.440><c> be</c><00:02:55.590><c> more</c><00:02:56.160><c> more</c><00:02:56.520><c> you</c>

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continue to grow and be more more you
 

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continue to grow and be more more you
know<00:02:57.180><c> i</c><00:02:57.209><c> think</c><00:02:57.620><c> combined</c><00:02:58.620><c> into</c><00:02:59.010><c> the</c><00:02:59.160><c> very</c>

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know i think combined into the very
 

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know i think combined into the very
fabric<00:02:59.370><c> of</c><00:02:59.760><c> our</c><00:02:59.850><c> lives</c><00:03:00.290><c> so</c><00:03:01.290><c> yeah</c><00:03:02.220><c> I</c><00:03:02.250><c> mean</c><00:03:02.340><c> I</c>

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fabric of our lives so yeah I mean I
 

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fabric of our lives so yeah I mean I
think<00:03:02.520><c> data</c>

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think data
 

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think data
really<00:03:05.300><c> holds</c><00:03:05.510><c> the</c><00:03:05.630><c> key</c><00:03:05.810><c> for</c><00:03:06.110><c> a</c><00:03:06.170><c> lot</c><00:03:06.740><c> of</c><00:03:06.950><c> the</c>

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really holds the key for a lot of the
 

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really holds the key for a lot of the
big<00:03:07.670><c> technological</c><00:03:08.330><c> and</c><00:03:08.540><c> kind</c><00:03:08.810><c> of</c><00:03:08.900><c> industrial</c>

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big technological and kind of industrial
 

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big technological and kind of industrial
revolutions<00:03:09.800><c> that</c><00:03:09.890><c> we're</c><00:03:10.010><c> going</c><00:03:10.100><c> to</c><00:03:10.160><c> see</c><00:03:10.280><c> and</c>

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revolutions that we're going to see and
 

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revolutions that we're going to see and
really<00:03:11.120><c> bring</c><00:03:11.330><c> it</c><00:03:11.450><c> at</c><00:03:11.510><c> your</c><00:03:11.630><c> age</c><00:03:13.570><c> yeah</c><00:03:14.570><c> we're</c>

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really bring it at your age yeah we're
 

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really bring it at your age yeah we're
gonna<00:03:14.840><c> see</c><00:03:14.960><c> some</c><00:03:14.990><c> crazy</c><00:03:15.200><c> stuff</c><00:03:16.000><c> now</c><00:03:17.000><c> that</c><00:03:17.210><c> we</c>

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gonna see some crazy stuff now that we
 

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gonna see some crazy stuff now that we
think<00:03:17.420><c> we</c><00:03:17.510><c> know</c><00:03:17.540><c> it</c><00:03:17.720><c> necessarily</c><00:03:18.080><c> what</c><00:03:18.230><c> we're</c>

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think we know it necessarily what we're
 

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think we know it necessarily what we're
going<00:03:18.410><c> to</c><00:03:18.500><c> see</c><00:03:18.620><c> right</c><00:03:19.100><c> i</c><00:03:19.130><c> mean</c><00:03:19.220><c> and</c><00:03:19.610><c> i</c><00:03:19.850><c> think</c>

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going to see right i mean and i think
 

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going to see right i mean and i think
that's<00:03:20.210><c> the</c><00:03:20.360><c> other</c><00:03:20.620><c> exciting</c><00:03:21.620><c> yet</c><00:03:21.860><c> terrifying</c>

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that's the other exciting yet terrifying
 

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that's the other exciting yet terrifying
component<00:03:22.880><c> is</c><00:03:23.030><c> that</c><00:03:23.210><c> it's</c><00:03:23.390><c> moving</c><00:03:23.720><c> faster</c>

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component is that it's moving faster
 

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component is that it's moving faster
than<00:03:24.650><c> it</c><00:03:25.340><c> ever</c><00:03:25.460><c> has</c><00:03:25.700><c> before</c><00:03:25.850><c> right</c><00:03:26.570><c> we're</c>

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than it ever has before right we're
 

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than it ever has before right we're
seeing<00:03:26.930><c> these</c><00:03:27.020><c> massive</c><00:03:27.410><c> contractions</c><00:03:27.920><c> in</c><00:03:28.040><c> the</c>

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seeing these massive contractions in the
 

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seeing these massive contractions in the
cycle<00:03:28.340><c> of</c><00:03:28.610><c> when</c><00:03:28.760><c> these</c><00:03:28.910><c> innovations</c><00:03:29.210><c> come</c><00:03:29.540><c> out</c>

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cycle of when these innovations come out
 

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cycle of when these innovations come out
and<00:03:29.930><c> so</c><00:03:30.080><c> I'll</c><00:03:30.590><c> even</c><00:03:30.950><c> just</c><00:03:31.340><c> like</c><00:03:31.430><c> data</c><00:03:31.610><c> science</c>

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and so I'll even just like data science
 

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and so I'll even just like data science
wasn't<00:03:32.330><c> even</c><00:03:32.660><c> a</c><00:03:32.720><c> term</c><00:03:32.750><c> ten</c><00:03:33.440><c> years</c><00:03:33.620><c> ago</c><00:03:33.770><c> I</c><00:03:34.100><c> don't</c>

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wasn't even a term ten years ago I don't
 

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wasn't even a term ten years ago I don't
even<00:03:34.640><c> think</c><00:03:34.760><c> we</c><00:03:34.940><c> know</c><00:03:35.180><c> what</c><00:03:35.690><c> the</c><00:03:35.810><c> new</c><00:03:35.930><c> jobs</c>

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even think we know what the new jobs
 

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even think we know what the new jobs
will<00:03:36.380><c> be</c><00:03:36.440><c> in</c><00:03:36.800><c> the</c><00:03:37.040><c> next</c><00:03:37.190><c> 10</c><00:03:37.430><c> years</c><00:03:37.459><c> I</c><00:03:37.820><c> think</c>

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will be in the next 10 years I think
 

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will be in the next 10 years I think
will<00:03:38.390><c> continuously</c><00:03:38.810><c> change</c><00:03:39.170><c> and</c><00:03:39.530><c> a</c><00:03:40.010><c> big</c><00:03:40.220><c> part</c>

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will continuously change and a big part
 

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will continuously change and a big part
of<00:03:40.640><c> you</c><00:03:41.450><c> know</c><00:03:41.540><c> what</c><00:03:41.690><c> galvanized</c><00:03:42.080><c> tries</c><00:03:42.320><c> to</c><00:03:42.470><c> do</c>

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of you know what galvanized tries to do
 

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of you know what galvanized tries to do
is<00:03:42.709><c> move</c><00:03:42.890><c> the</c><00:03:43.160><c> pace</c><00:03:43.340><c> of</c><00:03:43.520><c> that</c><00:03:43.640><c> industry</c><00:03:43.880><c> all</c>

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is move the pace of that industry all
 

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is move the pace of that industry all
right<00:03:44.570><c> and</c><00:03:44.810><c> making</c><00:03:45.020><c> sure</c><00:03:45.170><c> that</c><00:03:45.320><c> we're</c>

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right and making sure that we're
 

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right and making sure that we're
providing<00:03:45.620><c> the</c><00:03:46.010><c> programs</c><00:03:46.430><c> and</c><00:03:46.700><c> the</c>

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providing the programs and the
 

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providing the programs and the
opportunities<00:03:47.540><c> and</c><00:03:47.600><c> the</c><00:03:47.660><c> on</c><00:03:47.780><c> ramps</c><00:03:48.050><c> for</c>

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opportunities and the on ramps for
 

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opportunities and the on ramps for
people<00:03:48.640><c> for</c><00:03:49.640><c> those</c><00:03:49.790><c> jobs</c><00:03:49.910><c> that</c><00:03:50.180><c> don't</c><00:03:50.239><c> even</c>

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people for those jobs that don't even
 

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people for those jobs that don't even
exist

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I<00:03:59.920><c> think</c><00:04:00.920><c> like</c><00:04:01.070><c> anything</c><00:04:01.990><c> particularly</c><00:04:02.990><c> data</c>

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I think like anything particularly data
 

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I think like anything particularly data
science<00:04:03.700><c> it</c><00:04:04.700><c> all</c><00:04:04.820><c> comes</c><00:04:05.060><c> down</c><00:04:05.150><c> to</c><00:04:05.480><c> practice</c>

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science it all comes down to practice
 

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science it all comes down to practice
and<00:04:06.620><c> practical</c><00:04:07.040><c> experience</c><00:04:07.430><c> and</c><00:04:08.330><c> that's</c><00:04:08.690><c> a</c>

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and practical experience and that's a
 

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and practical experience and that's a
key<00:04:09.050><c> part</c><00:04:09.320><c> of</c><00:04:09.500><c> our</c><00:04:09.680><c> programs</c><00:04:10.190><c> is</c><00:04:10.430><c> that</c><00:04:10.580><c> you</c><00:04:10.760><c> can</c>

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key part of our programs is that you can
 

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key part of our programs is that you can
go<00:04:11.060><c> and</c><00:04:11.270><c> get</c><00:04:11.420><c> a</c><00:04:11.690><c> masters</c><00:04:12.410><c> degree</c><00:04:12.650><c> you</c><00:04:13.310><c> know</c><00:04:13.400><c> n</c>

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go and get a masters degree you know n
 

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go and get a masters degree you know n
number<00:04:14.630><c> of</c><00:04:14.660><c> universities</c><00:04:15.470><c> both</c><00:04:15.770><c> you</c><00:04:16.040><c> know</c>

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number of universities both you know
 

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number of universities both you know
domestically<00:04:17.060><c> in</c><00:04:17.150><c> worldwide</c><00:04:17.510><c> some</c><00:04:18.470><c> of</c><00:04:18.590><c> them</c><00:04:18.680><c> a</c>

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domestically in worldwide some of them a
 

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domestically in worldwide some of them a
great<00:04:19.220><c> program</c><00:04:19.400><c> some</c><00:04:19.760><c> of</c><00:04:19.850><c> it</c><00:04:19.940><c> or</c><00:04:20.030><c> not</c><00:04:20.180><c> but</c>

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great program some of it or not but
 

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great program some of it or not but
typically<00:04:21.110><c> they're</c><00:04:21.410><c> focusing</c><00:04:21.890><c> on</c><00:04:22.070><c> the</c>

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typically they're focusing on the
 

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typically they're focusing on the
theoretical<00:04:23.090><c> aspects</c><00:04:23.510><c> right</c><00:04:23.900><c> they're</c><00:04:24.170><c> going</c>

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theoretical aspects right they're going
 

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theoretical aspects right they're going
to<00:04:24.380><c> go</c><00:04:24.530><c> and</c><00:04:24.740><c> walk</c><00:04:25.370><c> you</c><00:04:25.490><c> through</c><00:04:25.640><c> a</c><00:04:25.700><c> proof</c><00:04:26.030><c> and</c>

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to go and walk you through a proof and
 

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to go and walk you through a proof and
prove<00:04:26.660><c> to</c><00:04:26.690><c> you</c><00:04:26.930><c> that</c><00:04:26.960><c> under</c><00:04:27.290><c> all</c><00:04:27.410><c> network</c>

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prove to you that under all network
 

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prove to you that under all network
converges<00:04:28.390><c> but</c><00:04:29.390><c> i</c><00:04:29.420><c> think</c><00:04:29.690><c> the</c><00:04:30.200><c> rubber</c><00:04:30.500><c> really</c>

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converges but i think the rubber really
 

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converges but i think the rubber really
meets<00:04:30.860><c> the</c><00:04:31.130><c> road</c><00:04:31.250><c> is</c><00:04:31.400><c> when</c><00:04:31.550><c> you</c><00:04:31.640><c> apply</c><00:04:31.910><c> this</c>

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meets the road is when you apply this
 

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meets the road is when you apply this
stuff<00:04:32.300><c> in</c><00:04:32.510><c> practice</c><00:04:32.990><c> right</c><00:04:33.380><c> and</c><00:04:33.590><c> when</c><00:04:33.710><c> does</c><00:04:33.860><c> it</c>

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stuff in practice right and when does it
 

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stuff in practice right and when does it
work<00:04:34.100><c> when</c><00:04:34.310><c> is</c><00:04:34.400><c> it</c><00:04:34.520><c> not</c><00:04:34.640><c> work</c><00:04:34.850><c> how</c><00:04:35.510><c> do</c><00:04:35.600><c> you</c>

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work when is it not work how do you
 

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work when is it not work how do you
clean<00:04:35.990><c> data</c><00:04:36.260><c> how</c><00:04:36.530><c> do</c><00:04:36.590><c> you</c><00:04:36.680><c> work</c><00:04:36.800><c> with</c><00:04:36.860><c> the</c><00:04:37.010><c> team</c>

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clean data how do you work with the team
 

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clean data how do you work with the team
i<00:04:37.490><c> do</c><00:04:37.580><c> work</c><00:04:37.730><c> with</c><00:04:37.820><c> engineering</c><00:04:38.240><c> put</c><00:04:38.510><c> things</c>

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i do work with engineering put things
 

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i do work with engineering put things
into<00:04:38.810><c> production</c><00:04:39.110><c> and</c><00:04:39.440><c> so</c><00:04:40.100><c> I</c><00:04:40.130><c> think</c><00:04:40.220><c> that</c>

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into production and so I think that
 

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into production and so I think that
practical<00:04:41.030><c> experience</c><00:04:41.530><c> and</c><00:04:42.530><c> knowing</c><00:04:43.010><c> when</c>

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practical experience and knowing when
 

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practical experience and knowing when
things<00:04:43.700><c> work</c><00:04:44.000><c> and</c><00:04:44.270><c> especially</c><00:04:44.660><c> when</c><00:04:44.990><c> they</c>

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things work and especially when they
 

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things work and especially when they
don't<00:04:45.350><c> work</c><00:04:45.500><c> and</c><00:04:45.770><c> being</c><00:04:46.010><c> able</c><00:04:46.190><c> to</c><00:04:46.340><c> have</c><00:04:46.430><c> that</c>

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don't work and being able to have that
 

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don't work and being able to have that
intuition<00:04:47.030><c> and</c><00:04:47.330><c> being</c><00:04:47.960><c> able</c><00:04:48.050><c> to</c><00:04:48.230><c> understand</c>

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intuition and being able to understand
 

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intuition and being able to understand
and<00:04:48.860><c> identify</c><00:04:49.090><c> where</c><00:04:50.380><c> potential</c>

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and identify where potential
 

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and identify where potential
opportunities<00:04:52.010><c> in</c><00:04:52.100><c> your</c><00:04:52.130><c> data</c><00:04:52.370><c> are</c><00:04:52.550><c> because</c>

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opportunities in your data are because
 

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opportunities in your data are because
data<00:04:54.350><c> science</c><00:04:54.710><c> even</c><00:04:54.890><c> though</c><00:04:54.950><c> it's</c><00:04:55.040><c> called</c>

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data science even though it's called
 

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data science even though it's called
data<00:04:55.370><c> science</c><00:04:55.610><c> is</c><00:04:55.700><c> as</c><00:04:55.820><c> much</c><00:04:55.940><c> of</c><00:04:56.000><c> an</c><00:04:56.180><c> art</c><00:04:56.360><c> as</c><00:04:56.570><c> it</c>

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data science is as much of an art as it
 

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data science is as much of an art as it
is<00:04:56.810><c> a</c><00:04:56.870><c> science</c><00:04:56.960><c> and</c><00:04:57.500><c> having</c><00:04:57.770><c> that</c><00:04:57.800><c> human</c>

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is a science and having that human
 

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is a science and having that human
component<00:04:58.640><c> of</c><00:04:58.940><c> it</c><00:04:59.030><c> and</c><00:04:59.150><c> that</c><00:04:59.180><c> creative</c>

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component of it and that creative
 

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component of it and that creative
passion<00:05:00.250><c> and</c><00:05:01.250><c> how</c><00:05:01.460><c> these</c><00:05:01.580><c> things</c><00:05:01.640><c> are</c><00:05:01.880><c> applied</c>

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passion and how these things are applied
 

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passion and how these things are applied
I<00:05:02.210><c> think</c><00:05:02.540><c> is</c><00:05:02.630><c> really</c><00:05:02.660><c> important</c><00:05:03.230><c> and</c><00:05:03.380><c> so</c><00:05:03.560><c> my</c>

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I think is really important and so my
 

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I think is really important and so my
biggest<00:05:03.890><c> advice</c><00:05:04.130><c> is</c><00:05:04.810><c> you</c><00:05:06.160><c> know</c><00:05:07.160><c> practice</c>

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biggest advice is you know practice
 

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biggest advice is you know practice
makes<00:05:07.550><c> perfect</c><00:05:07.850><c> and</c><00:05:08.240><c> you</c><00:05:08.600><c> really</c><00:05:08.870><c> need</c><00:05:09.050><c> to</c><00:05:09.080><c> get</c>

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makes perfect and you really need to get
 

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makes perfect and you really need to get
you<00:05:09.920><c> know</c><00:05:10.010><c> dig</c><00:05:10.370><c> into</c><00:05:10.580><c> the</c><00:05:10.730><c> data</c><00:05:10.850><c> you</c><00:05:11.180><c> need</c><00:05:11.330><c> to</c>

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you know dig into the data you need to
 

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you know dig into the data you need to
be<00:05:11.540><c> working</c><00:05:11.720><c> on</c><00:05:11.870><c> interesting</c><00:05:12.260><c> problems</c><00:05:12.620><c> new</c>

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be working on interesting problems new
 

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be working on interesting problems new
problems<00:05:13.280><c> all</c><00:05:13.460><c> the</c><00:05:13.490><c> time</c><00:05:13.840><c> and</c><00:05:14.840><c> really</c><00:05:15.500><c> working</c>

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problems all the time and really working
 

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problems all the time and really working
cross-functionally<00:05:17.210><c> with</c><00:05:17.750><c> your</c><00:05:17.840><c> team</c>

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cross-functionally with your team
 

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cross-functionally with your team
because<00:05:18.320><c> i</c><00:05:18.410><c> think</c><00:05:18.470><c> you</c><00:05:19.070><c> know</c><00:05:19.130><c> we've</c><00:05:19.700><c> seen</c><00:05:19.970><c> data</c>

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because i think you know we've seen data
 

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because i think you know we've seen data
science<00:05:20.450><c> applied</c><00:05:20.600><c> a</c><00:05:20.720><c> lot</c><00:05:20.990><c> to</c><00:05:21.170><c> things</c><00:05:21.350><c> like</c>

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science applied a lot to things like
 

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science applied a lot to things like
online<00:05:21.800><c> advertising</c><00:05:22.310><c> and</c><00:05:22.880><c> self</c><00:05:23.240><c> driving</c><00:05:23.480><c> and</c>

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online advertising and self driving and
 

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online advertising and self driving and
you<00:05:24.260><c> know</c><00:05:24.380><c> real</c><00:05:24.740><c> key</c><00:05:24.950><c> revenue</c><00:05:25.160><c> generators</c><00:05:26.030><c> in</c>

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you know real key revenue generators in
 

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you know real key revenue generators in
the<00:05:26.150><c> business</c><00:05:26.330><c> but</c><00:05:27.970><c> some</c><00:05:28.970><c> of</c><00:05:29.120><c> the</c><00:05:29.330><c> greatest</c>

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the business but some of the greatest
 

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the business but some of the greatest
data<00:05:30.230><c> scientists</c><00:05:30.710><c> they</c><00:05:30.830><c> came</c><00:05:31.100><c> in</c><00:05:31.250><c> through</c>

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data scientists they came in through
 

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data scientists they came in through
data<00:05:31.580><c> science</c><00:05:31.880><c> orthogonal</c><00:05:32.360><c> right</c><00:05:32.540><c> they</c>

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data science orthogonal right they
 

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data science orthogonal right they
didn't<00:05:32.870><c> study</c><00:05:33.080><c> it</c><00:05:33.260><c> they</c><00:05:33.590><c> were</c><00:05:34.010><c> facing</c><00:05:34.340><c> some</c>

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didn't study it they were facing some
 

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didn't study it they were facing some
problem<00:05:35.060><c> at</c><00:05:35.150><c> a</c><00:05:35.240><c> different</c><00:05:35.510><c> domain</c><00:05:35.600><c> and</c><00:05:35.990><c> they</c>

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problem at a different domain and they
 

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problem at a different domain and they
needed<00:05:36.770><c> to</c><00:05:36.920><c> use</c><00:05:37.250><c> data</c><00:05:37.550><c> science</c><00:05:37.970><c> to</c><00:05:38.300><c> solve</c><00:05:38.450><c> it</c>

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needed to use data science to solve it
 

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needed to use data science to solve it
at<00:05:38.810><c> large</c><00:05:39.200><c> scale</c><00:05:39.440><c> and</c><00:05:39.710><c> I</c><00:05:39.980><c> think</c><00:05:40.190><c> we're</c><00:05:40.310><c> going</c>

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at large scale and I think we're going
 

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at large scale and I think we're going
to<00:05:40.490><c> see</c><00:05:40.550><c> more</c><00:05:40.910><c> and</c><00:05:41.030><c> more</c><00:05:41.060><c> about</c><00:05:41.360><c> acceleration</c>

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to see more and more about acceleration
 

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to see more and more about acceleration
and<00:05:42.020><c> things</c><00:05:42.170><c> like</c><00:05:42.290><c> healthcare</c><00:05:42.590><c> things</c><00:05:43.070><c> like</c>

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and things like healthcare things like
 

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and things like healthcare things like
education<00:05:43.630><c> where</c><00:05:44.630><c> typically</c><00:05:44.930><c> we</c><00:05:45.080><c> haven't</c>

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education where typically we haven't
 

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education where typically we haven't
seen<00:05:45.590><c> a</c><00:05:45.620><c> lot</c><00:05:45.890><c> of</c><00:05:45.920><c> data</c><00:05:46.100><c> being</c><00:05:46.400><c> used</c><00:05:46.460><c> or</c>

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seen a lot of data being used or
 

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seen a lot of data being used or
leverage<00:05:47.150><c> that</c><00:05:47.830><c> you</c><00:05:48.830><c> know</c><00:05:48.920><c> I</c><00:05:48.950><c> think</c><00:05:49.070><c> that's</c>

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leverage that you know I think that's
 

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leverage that you know I think that's
where<00:05:49.430><c> the</c><00:05:49.490><c> next</c><00:05:49.850><c> kind</c><00:05:50.030><c> of</c><00:05:50.090><c> vanguard</c><00:05:50.510><c> of</c><00:05:50.630><c> great</c>

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where the next kind of vanguard of great
 

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where the next kind of vanguard of great
data<00:05:51.020><c> scientists</c><00:05:51.410><c> are</c><00:05:51.470><c> going</c><00:05:51.590><c> to</c><00:05:51.650><c> come</c><00:05:51.770><c> from</c>

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data scientists are going to come from
 

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data scientists are going to come from
you

