Jameel Syed, Cheif Scientist @Musicmetric presentation @ds_ldn March 21st, 2012

1. So,
What
Does
a
Data
Scien/st
do?
A
Data
Scien/st
in
the
Music
Industry
Dr
Jameel
Syed
March
2012
h>p://jasyed.com/datascience/

2. Overview
– Musicmetric
CTO
– InforSense
founding
member
• PhD
in
Workflows
for
Life
Sciences
Analysis
– Co-­‐organiser
Big
Data
London
meetup

3. Some
ques/ons...

4. Music
has
moved
online
• The
world
has
changed
– Do
you
buy
vinyl/tapes/CDs
of
music?
– Do
you
buy
music
downloads?
– Do
you
download
illegal
content
from
bi>orrent?
– Do
you
listen
to
music
on
YouTube?
– Do
you
“like”
bands
on
Facebook?
– Do
you
subscribe
to
Spo/fy?
– Do
you
listen
on
the
radio
to
the
weekly
charts
on
a
Sunday
aWernoon?
• What’s
happening
online?

5. How
popular
am
I?

6. Who
are
my
fans?

7. Where
are
my
fans?

8. What
is
the
press
saying?

9. Who
is
popular?

10. A
Data
Scien/st
in
the
Music
Industry
• Raw
Data
-­‐>
Derived
Data
-­‐>
Insight
– Who
is
popular
right
now/in
the
immediate
future?
– What
was
the
effect
of
appearing
at
a
fes/val?
– Which
ar/sts
are
(becoming)
popular
with
listeners
with
certain
demographics
(in
a
region)?
• Data
processing,
machine
learning
&
sta/s/cal
methods
– Sen/ment
analysis
– Named
En/ty
Recogni/on
– Ranking
– Segmenta/on
• One-­‐offs
– Infographics
and
microsites
for
events
– Brand
alignment
via
demographics
– Music
Hack
Days
• Product
– Daily
charts
– Sen/ment
scoring
web
crawled
reviews

11. What
is
a
Data
Scien/st?

12. Have
we
been
here
before?
• Sta/s/cian
• Data
Analyst
• Quan/ta/ve
analyst
• Bioinforma/cian
• Data
Miner
• Business
Intelligence
consultant
• Computa/onal
physicst

13. A
Life
Sciences
digression...

14. What’s
new?
• Data
provides
the
opportunity
– Old:
Collect
and
store
data
presupposing
how
it
will
be
used
– New:
Collect
raw
data
&
explore
which
deriva/ons
are
interes/ng;
integra/ng
data
from
mul/ple
online
sources.
– Big
Data
technology
to
cope
with
data
volume
• Programming
is
essen/al
– APIs
– Heterogeneous
environment(s)
• Method
of
presenta/on
– Infographics
– Interac/ve
(web)
applica/ons
– (Raw
data)

15. Data
Scien/st
• “Jack
of
all
trades”
– “Hacker”
mentality:
learn
new
technology
and
approaches
for
a
project
on
short
no/ce
– Crea/ve
self-­‐starters
– Work
alongside
other
experts
(data,
domain,
soWware
engineering)

16. A
Data
Scien/st
is
good
at
knieng?
• Not
building
from
scratch,
knieng
together
pre-­‐exis/ng
parts
• Data
– Databases
(rela/onal/NoSQL)
– Files
– APIs
• Algorithms
– Open
source
libraries
– Off
the
shelf
tools
• Compute
– Linux
– AWS?
• Languages
– Many,
especially
“scrip/ng”
languages