1.
Ryan Orban
Co-Founder & CEO
ryan@zipfianacademy.com
@ryanorban

2.
Why are we talking about data science?

3.
Data Analyst Shortage
Source: http://www.delphianalytics.net/wp-content/uploads/2013/04/GrowthOfDataVsDataAnalysts.png

4.
What is data science?

5.
Perfect Storm

6.
Technology
Source: http://www.jcmit.com/diskprice.htm
0
1000
2000
3000
4000
1992 1997 2002 2007 2012
Capacity (GB) Cost per GB (USD)

7.
Unprecedented Data Growth

8.
Enter the Data Scientist

9.
What is Data Science?
+ Communication

10.
What do people look for in a data
scientist?

11.
Broad-range generalist
Deepexpertise
T-Shaped Skillset

12.
T-Shaped Skillset
Machine Learning,
Statistics, Domain Knowledge
Softw
are
EngineeringBusiness
Acum
en
Distributed
Com
puting
Com
m
unication

13.
Data Science Roles

14.
How to I become a data scientist?

15.
Data scientists need to know
how to code.

16.
Python R Julia
Java C++/GoScala/Clojure
High-level
Lower-level
Learn to Code

17.
Learn to Code

18.
Data scientists need to be
comfortable with mathematics
& statistics.

19.
Mathematics Statistical Analysis
Mathematics & Statistics
Distributions (Binomial,
Poisson, etc.)
Summary Statistics
(Mean, Variance, etc.)
Hypothesis Testing
Bayesian Analysis
Linear Algebra
(Matrix Factorization)
Calculus
(Integrals, Derivatives,
etc)
Graph Theory
Probability/
Combinatorics

20.
Mathematics & Statistics

21.
Data scientists need know
machine learning & software
engineering.

22.
Distributed
Computing
Supervised
(SVM, Random Forest)
NLP / Information
Retrieval
Algorithms & Data
Structures
Data Visualization
Data Munging
Machine Learning & Software Engineering
Machine Learning
Software
Engineering
Validation, Model
Comparison
Unsupervised
(K-means, LDA)

23.
Open-Source Data Science Masters

24.
SlideRule

25.
DataTau

26.
Learning data science can be
really hard.

27.
≠ Data Science

28.
Learning data science can be
really hard.

29.
Context is King

30.
It’s about putting the
pieces together

31.
Pathways:
MS/PhD in Data Science
Internship
Immersive Programs
Self-study

32.
You don’t need a PhD to do
data science.

33.
Backgrounds
Educational Background
BS
MS
PhD
0 4 8 12 16

34.
Backgrounds
Disciplines
Software Engineering
Analysts
Finance/Economics
Engineering
Physics
Physical Sciences
Mathematics
Statistics
Astronomy
Linguistics
Professional Poker
0 2 4 6 8

35.
Backgrounds
94% Placement Rate91% Placement
$115k avg. salary

36.
The Program
• 12-week immersive bootcamp in San Francisco
• Project-based curriculum with real datasets,
solving actual problems
• Guest lectures from leaders in the field
• Personal mentorship to help students grow

37.
Timeline
STRUCTURED CURRICULUM
HIRING
DAY
CAPSTONE
PROJECT
GRADUATION
1 8 11 12
INTERVIEW
PREP
Program Timeline

38.
Learning Techniques

39.
Hiring Partners

40.
!
• Working knowledge of programming
• Background in a quantitative
discipline
• Comfortable with mathematics and
statistics
• Child-like curiosity
What We Look For

41.
Zipfian
Academy
Data Science
Immersive
Data Fellowship
Data Engineering
Immersive
Weekend
Workshops

42.
Zipfian
Academy
@ZipfianAcademy
Data Science Immersive
12-weeks (Sep 8th)
Weekend Workshops
http://zipfianacademy.com/apply
http://zipfianacademy.com/workshops
Next: Interactive Visualizations w/ d3.js ( July 19 )

43.
The best way to learn data
science is by doing data
science.

44.
https://github.com/ipython/ipython/wiki/A-gallery-of-
interesting-IPython-Notebooks

45.
Checklist:
Learn the fundamentals
Build out a project portfolio
Apply!
Blog about your experience

46.
A Practical Intro to Data Science
http://bit.ly/learndatascience

47.
Thank You!
Ryan Orban
Co-Founder
ryan@zipfianacademy.com
@ryanorban