1.
Demys&fying
Data
Science
with
and
Intro
to
Machine
Learning

2.
Data
science
is
everywhere

3.
Sexiest
job
in
21st
century*
McKinsey
Global
Ins&tute
report
es&mates
that
by
2018,
“the
United
States
alone
could
face
a
shortage
of
140,000
to
190,000
people
with
deep
analy&cal
skills
as
well
as
1.5
million
managers
and
analysts
with
the
know-­‐how
to
use
the
analysis
of
big
data
to
make
effec&ve
decisions”
Source:
Harvard
business
Review
Oct’
2012

4.
So
what
is
Data
Science?

5.
Source:
Hilary
Mason
ex-­‐Chief
data
science
bit.ly

6.
Who
are
these
unicorns?

7.
Bit
about
me
@brightsparc

8.
I
thought
it
was
all
about
stats?

9.
It’s
a
broader
skillset
Source:
h[p://blogs.wsj.com/cio/2014/02/14/it-­‐takes-­‐teams-­‐to-­‐solve-­‐the-­‐data-­‐scien&st-­‐shortage/

10.
Data
science
pipeline
Source:
h[p://cacm.acm.org/blogs/blog-­‐cacm/169199-­‐data-­‐science-­‐workflow-­‐overview-­‐and-­‐challenges/fulltext

11.
Where
does
Kaggle
fit
it?
Degree
breakdown
in
top
100
Areas
of
study

12.
What’s
the
deal
with
big
data?

13.
Apache
Hadoop
Ecosystem

14.
It’s
like
Map
Reduce
you
know

15.
So
what
about
machine
learning?
Pioneer
in
machine
learning,
created
a
checkers
game
that
played
itself
“Give
machines
the
ability
to
learn
without
explicitly
programming
them.”
Arthur
L.
Samuel
(1959)

16.
Types
of
algorithms

17.
Some
examples

18.
Machine
learning
process

19.
Build
a
model
Underfit
Overfit
Linear
Regression
Solve
for
values
of
θ
in
the
Hypothesis
func&on
hθ(x)

20.
Gradient
descent
algorithm
Minimize
cost
func&on
which
is
½
of
average
square
error
of
predic&on
vs.
the
training
data.

21.
Demo:
House
prices

22.
Cross
valida&on
–
split
training/test

23.
Supervised
learning
model

24.
Recommender
systems
Collabora&ve
filtering
–
predict
ra&ngs
for
similar
items
given
other
users
behavior

25.
Collabora&ve
filtering
method
Source:
h[p://cran.r-­‐project.org/web/packages/recommenderlab/vigne[es/recommenderlab.pdf

26.
Similar
users
based
on
distance
Manha[an
distance
Euclidian
distance

27.
Demo:
Music
recommender
system
Pearson
Correla&on
Coefficient

28.
Visualiza&on
frameworks
Tableau
D3.js
Processing
Raphaël.js

29.
What
about
online
experimenta&on?

30.
What
will
the
future
look
like
• Online
collabora&on
• Open
Data

31.
Next
gen
distributed
compu&ng
100x
faster
in
memory,
and
10x
faster
even
when
running
on
disk.

32.
Deep
learning,
a
new
fron&er?
Geoffrey
Hinton
@Google

33.
How
can
I
get
started?
• MOOCs
– Coursera
Machine
Learning
(Andrew
Ng
-­‐
Stanford)
– Learning
from
Data
(Abu-­‐Mostafa
-­‐
Caltech)
• Other
references
– Collec&ve
Intelligence
– Mining
of
massive
data
sets
– Open-­‐Source
Data
Science
Masters
• Frameworks
– Python
–
Scikit
learn
– Java
–
WEKA
and
Cascading

34.
Ques&ons