The document discusses the role of a data scientist in the music industry. It describes how music consumption has shifted online, creating large amounts of digital data. A data scientist in this field collects raw data from various online sources, derives insights from it using techniques like sentiment analysis and machine learning, and creates products like daily charts and tools for analyzing an artist's popularity, fans, and press mentions. The data scientist acts as a "jack of all trades", learning new technologies and bringing together different experts and components to solve problems.

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