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I'm<00:00:12.330><c> Ruben</c><00:00:12.870><c> fukken</c><00:00:13.320><c> I'm</c><00:00:13.500><c> the</c><00:00:13.710><c> co-founder</c><00:00:13.950><c> and</c>

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I'm Ruben fukken I'm the co-founder and
 

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I'm Ruben fukken I'm the co-founder and
the<00:00:14.639><c> chief</c><00:00:14.940><c> product</c><00:00:15.269><c> and</c><00:00:15.540><c> under</c><00:00:15.839><c> desks</c>

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the chief product and under desks
 

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the chief product and under desks
officer<00:00:16.680><c> of</c><00:00:16.859><c> data</c><00:00:17.340><c> rpm</c><00:00:23.060><c> yeah</c><00:00:24.060><c> I</c><00:00:24.090><c> think</c><00:00:24.300><c> the</c>

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officer of data rpm yeah I think the
 

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officer of data rpm yeah I think the
biggest<00:00:25.590><c> thing</c><00:00:25.740><c> is</c><00:00:25.949><c> that</c><00:00:25.980><c> the</c><00:00:26.250><c> very</c><00:00:26.609><c> very</c>

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biggest thing is that the very very
 

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biggest thing is that the very very
relevant<00:00:27.060><c> audience</c><00:00:27.750><c> who</c><00:00:28.050><c> are</c><00:00:28.080><c> focused</c><00:00:29.070><c> on</c>

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relevant audience who are focused on
 

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relevant audience who are focused on
data<00:00:29.880><c> data</c><00:00:30.510><c> science</c><00:00:30.960><c> and</c><00:00:31.170><c> machine</c><00:00:31.890><c> learning</c>

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data data science and machine learning
 

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data data science and machine learning
aspects<00:00:32.730><c> and</c><00:00:32.850><c> that</c><00:00:33.390><c> is</c><00:00:33.540><c> what</c><00:00:33.690><c> we</c><00:00:33.840><c> do</c><00:00:33.989><c> we</c><00:00:34.290><c> help</c>

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aspects and that is what we do we help
 

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aspects and that is what we do we help
automate<00:00:35.040><c> data</c><00:00:35.070><c> science</c><00:00:35.579><c> so</c><00:00:35.730><c> I</c><00:00:35.760><c> think</c><00:00:36.200><c> we</c><00:00:37.200><c> the</c>

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automate data science so I think we the
 

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automate data science so I think we the
talk<00:00:38.280><c> connected</c><00:00:38.850><c> well</c><00:00:39.180><c> with</c><00:00:39.450><c> the</c><00:00:39.690><c> audience</c>

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talk connected well with the audience
 

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talk connected well with the audience
here<00:00:40.170><c> very</c><00:00:41.010><c> much</c><00:00:41.219><c> and</c><00:00:41.460><c> that</c><00:00:41.610><c> is</c><00:00:41.640><c> what</c><00:00:41.910><c> is</c><00:00:42.059><c> very</c>

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here very much and that is what is very
 

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here very much and that is what is very
exciting<00:00:42.540><c> I</c><00:00:47.660><c> think</c><00:00:48.660><c> the</c><00:00:48.840><c> kind</c><00:00:49.200><c> of</c><00:00:49.410><c> problems</c>

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exciting I think the kind of problems
 

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exciting I think the kind of problems
that<00:00:50.190><c> data</c><00:00:50.760><c> can</c><00:00:51.030><c> help</c><00:00:51.059><c> solve</c><00:00:51.719><c> which</c><00:00:52.620><c> is</c>

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that data can help solve which is
 

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that data can help solve which is
specifically<00:00:54.480><c> with</c><00:00:54.750><c> using</c><00:00:55.469><c> machine</c><00:00:56.070><c> learning</c>

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specifically with using machine learning
 

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specifically with using machine learning
and<00:00:56.489><c> automation</c><00:00:57.030><c> is</c><00:00:57.360><c> this</c><00:00:57.629><c> it's</c><00:00:58.109><c> insane</c><00:00:58.649><c> and</c>

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and automation is this it's insane and
 

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and automation is this it's insane and
and<00:00:59.100><c> specifically</c><00:00:59.640><c> with</c><00:01:00.090><c> new</c><00:01:00.329><c> emerging</c><00:01:00.780><c> areas</c>

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and specifically with new emerging areas
 

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and specifically with new emerging areas
like<00:01:01.379><c> machine</c><00:01:01.739><c> data</c><00:01:01.980><c> and</c><00:01:02.370><c> IOT</c><00:01:02.940><c> where</c>

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like machine data and IOT where
 

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like machine data and IOT where
opportunities<00:01:04.500><c> are</c><00:01:04.860><c> in</c><00:01:05.189><c> in</c><00:01:05.729><c> trillions</c><00:01:06.390><c> of</c>

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opportunities are in in trillions of
 

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opportunities are in in trillions of
dollars<00:01:06.750><c> right</c><00:01:07.229><c> so</c><00:01:07.260><c> that</c><00:01:07.619><c> is</c><00:01:07.710><c> what</c><00:01:07.890><c> is</c><00:01:08.040><c> most</c>

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dollars right so that is what is most
 

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dollars right so that is what is most
exciting<00:01:08.430><c> that</c><00:01:09.420><c> data</c><00:01:09.630><c> can</c><00:01:09.900><c> actually</c><00:01:10.260><c> make</c><00:01:10.470><c> a</c>

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exciting that data can actually make a
 

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exciting that data can actually make a
dollar<00:01:11.460><c> impact</c><00:01:11.700><c> that's</c><00:01:12.210><c> huge</c><00:01:12.810><c> in</c><00:01:13.470><c> the</c>

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dollar impact that's huge in the
 

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dollar impact that's huge in the
industry

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I think the most important thing that I
 

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I think the most important thing that I
and<00:01:24.660><c> the</c><00:01:24.780><c> message</c><00:01:25.080><c> that</c><00:01:25.110><c> I</c><00:01:25.410><c> wanted</c><00:01:25.710><c> to</c><00:01:25.830><c> get</c>

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and the message that I wanted to get
 

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and the message that I wanted to get
across<00:01:26.280><c> is</c><00:01:26.640><c> that</c><00:01:26.700><c> the</c><00:01:27.570><c> way</c><00:01:28.140><c> to</c><00:01:28.290><c> scale</c><00:01:28.770><c> data</c>

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across is that the way to scale data
 

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across is that the way to scale data
science<00:01:29.430><c> is</c><00:01:29.550><c> not</c><00:01:29.760><c> by</c><00:01:30.060><c> having</c><00:01:31.020><c> more</c><00:01:31.230><c> people</c>

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science is not by having more people
 

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science is not by having more people
everyone<00:01:33.240><c> has</c><00:01:33.300><c> to</c><00:01:33.630><c> think</c><00:01:33.990><c> automation</c><00:01:34.950><c> and</c>

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everyone has to think automation and
 

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everyone has to think automation and
that<00:01:35.820><c> is</c><00:01:35.940><c> that</c><00:01:36.180><c> is</c><00:01:36.300><c> essentially</c><00:01:36.840><c> the</c><00:01:36.870><c> next</c>

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that is that is essentially the next
 

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that is that is essentially the next
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thing because otherwise currently we
 

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thing because otherwise currently we
have<00:01:39.360><c> a</c><00:01:39.540><c> gap</c><00:01:39.930><c> of</c><00:01:40.170><c> almost</c><00:01:40.290><c> like</c><00:01:40.680><c> 1</c><00:01:40.920><c> million</c><00:01:41.160><c> data</c>

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have a gap of almost like 1 million data
 

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have a gap of almost like 1 million data
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scientists that is required in the next
 

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scientists that is required in the next
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couple of years time and that is a skill
 

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couple of years time and that is a skill
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gap that we cannot match right so most
 

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gap that we cannot match right so most
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data scientists the job of the future
 

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data scientists the job of the future
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would be of how can they automate their
 

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would be of how can they automate their
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work as much as possible and helps keep
 

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work as much as possible and helps keep
themselves

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yeah<00:02:05.330><c> I</c><00:02:05.510><c> think</c><00:02:05.600><c> the</c><00:02:05.810><c> most</c><00:02:06.020><c> important</c><00:02:06.560><c> factor</c>

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yeah I think the most important factor
 

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yeah I think the most important factor
of<00:02:07.100><c> becoming</c><00:02:07.400><c> a</c><00:02:07.520><c> creative</c><00:02:07.760><c> the</c><00:02:08.090><c> scientist</c><00:02:08.539><c> is</c>

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of becoming a creative the scientist is
 

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of becoming a creative the scientist is
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in understanding not just algorithms or
 

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in understanding not just algorithms or
the<00:02:12.680><c> techniques</c><00:02:13.070><c> and</c><00:02:13.250><c> the</c><00:02:13.340><c> math</c><00:02:13.580><c> and</c><00:02:13.790><c> stats</c>

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the techniques and the math and stats
 

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the techniques and the math and stats
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behind it I mean all of those are
 

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behind it I mean all of those are
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required but it is an understanding how
 

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required but it is an understanding how
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how to solve business problems right
 

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how to solve business problems right
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with data I think that Kinect is is what
 

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with data I think that Kinect is is what
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makes the difference between an average
 

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makes the difference between an average
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data scientist and in created a
 

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data scientist and in created a
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scientific understanding business to
 

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scientific understanding business to
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