On Building a Data Science Curriculum

On Building a Data Science CurriculumNovember 23nd, 2014

Jonathan DinuDirector of Education, Galvanize

[email protected]@clearspandex

Questions? tweet @galvanize

Formerly


Formerly


+

Currently


Challenge

The Challenge


Challenge

Tools

Framework/Library

Big Data (scalability)

Small Data

Bespoke Code

Cloudera ML

Mahout

MLlib (amplab)H20 (0xdata)

C/C++

MapReduce (Streaming)

MapReduce (Java)

Cascading/Crunch

Pig/Hive

Vowpal Rabbit

GiraphGraphLab

SparkStorm

CRANR

Python

Javascikit-learn

pandas

mlpack

Weka

Numpy

Javascript


Obligatory Name Drop


Acquisition

Parse

Storage

Transform/Explore

Vectorization

Train

Model

Expose

Presentation

requests

BeautifulSoup4

pandas

pymongo

Flask

At Scale Locally

scrapy

Hadoop Streaming (w/ BeautifulSoup4)

mrjob or Mortar (w/ Python UDF)

Snakebite (HDFS)

MLlib (pySpark)

Flask

scikit-learn/NLTK

Challenge


Challenge

Now do that in 8 weeks


Challenge


Intuition

Iteration 0: Intuition


Content

Questions? tweet @galvanize Source: Metacademy

Bottom Up Approach


Content

Content

Source: Coursera

Content

Source: UC Berkeley Masters

Not Everybody Learns This Way


Issues

Issues

• Not Enough Context

• Not Enough Concept Overlap

• Takes too much Time

• Nothing Happens in a Vacuum


Digression

Not Just for Data Science


(relevant to learning any complex subject)

Experience

Iteration 1: Experience


Theory

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



Worth the Upfront Investment

Theory

Technique

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)



Just ask them!

Network

(the students)

Context is King


Network


Network

Iris Dataset Classification


Network


NYT Topic Modeling


Network


NYT Topic Modeling

Real-time Fraud scoring service


Network


NYT Topic Modeling


Personal Capstone Project


Network


NYT Topic Modeling


Personal Capstone

“Domesticated Data” Learn the tools/theory


Network


NYT Topic Modeling


Personal Capstone


Learn the application“Wild Data”


Network


NYT Topic Modeling


Personal Capstone



Simulated Case Study Learn the process


Network


NYT Topic Modeling


Personal Capstone



Simulated Case Study

Greenfield Project Learn the practice/art

Learn the process

Theory


Theory

Application

Synthesis

$$$ PROFIT!!


Just ask them!

Network

Network



Just ask them!(and be flexible)

Network


Treat them like customers(because they are)

Network


Always Validate!

Network

Metrics

Iteration 2: Data!


Experience

Iteration 2: Data!


METRICS

METRICS EVERYWHERESaturday, April 9, 2011

Metrics



• Commits

• Pull Requests

• Passing Tests

• Etc.

Metrics

Curriculum as Product

Learning Techniques



Industry Techniques

Source: http://en.wikipedia.org/wiki/Extreme_programming

http://en.wikipedia.org/wiki/Extreme_programming


Industry Techniques

Source: http://lostechies.com/scottreynolds/2009/10/07/how-we-do-things-tdd-bdd/

http://lostechies.com/scottreynolds/2009/10/07/how-we-do-things-tdd-bdd/


Industry Techniques

Code Reviews

Source: http://agile.dzone.com/articles/re-pair-programming

http://agile.dzone.com/articles/re-pair-programming

Our House

@Zipfian(now Galvanize)


source: http://www.sebastienmillon.com/Rainbow-Immersion-Therapy-Art-Print-15

http://www.sebastienmillon.com/Rainbow-Immersion-Therapy-Art-Print-15

Methodology

Commun

ity Education

Industry

Meetup

Student Groups

Corporate Training


Methodology


• Outcomes focused

• Project-based curriculum using real datasets

• Guest lectures from leaders in the field

• Mock interviews and hiring preparation

• Full instructional staff + personal mentorship

Employment

Questions? tweet @galvanize Source: http://www.nerdwallet.com/nerdscholar/grad_surveys/highest-employment-rates

University of Massachusetts-Amherst School of Nursing

98%

Georgetown University McDonough School of Business

94%

Michigan State University College of Nursing

92%

Syracuse University School of Architecture

90%

University of Massachusetts-Amherst Isenberg School of Management

90%

Michigan State University School of Hospitality Business

89%

New York University 88%

Boston College Connell School of Nursing

88%

Boston College Carroll School of Management

87%

Case Western Reserve University Frances Payne Bolton School of Nursing

86%

Highest Employment Rates (2012)

1. Princeton University

2. Harvard University

3. Yale University

4. Columbia University

5. Stanford University

6. University of Chicago

7. Duke University

8. MIT

9. University of Pennsylvania

10. California Institue of Technology

U.S. News and World Report Ranking

http://www.nerdwallet.com/nerdscholar/grad_surveys/highest-employment-rates

Timeline


STRUCTURED CURRICULUM

HIRING DAY

CAPSTONE PROJECT

GRADUATION

08 10.5 12

INTERVIEWS

Data Science Immersive


Industry Student Projects


!

• Working knowledge of programming

• Background in a quantitative discipline

• Comfortable with mathematics and statistics

• Child-like curiosity

What We Look For

Our Students

Our Students


Educational Background

BS

MS

PhD

0 4 8 12 16


Disciplines

Software EngineeringAnalysts

Finance/EconomicsEngineering

PhysicsPhysical Sciences

MathematicsStatistics

AstronomyLinguistics

Professional Poker

0 2 4 6 8

Our Students


Data Science Immersive

Masters in Data Science

Data Engineering Immersive

Weekend Workshops

+


Immersive

Masters


Immersive

Masters

(not to scale)


Masters of Science - 1 year (Starts in Spring)

http://www.galvanizeu.com/request-info

http://www.galvanizeu.com/request-info

Goals


!

• Present a guest lecture or share a data story

• Donate datasets and propose projects

• Sponsor a scholarship

• Attend our Hiring Day

Get Involved

Goals


!

• Full-time Instructors

• TAs

• Mentor (volunteer)

We’re Hiring!

Questions?


Thank You!

Jonathan DinuDirector of Education, Galvanize

[email protected]@clearspandex