Girls Who Code and Do Data Science

@EstherVasiete Data Scientist

July 12th, 2016 Girls Who Code Summer Immersion Program

About me •  Born and raised in Barcelona •  Bachelor’s Degree in Electrical Engineering

About me •  Studied abroad in UK - Best time of my life - Developed an interest in image processing and computer vision - Also developed an interest in machine learning, I just didn’t know then

About me •  Did my Masters at CU Boulder •  Officially, I received my diploma in EE - Unofficially, I like to think about it as a CS degree - I managed to cross-list most courses and thesis advisor so that I could feed my growing interest for machine learning

About me •  Once I graduated, I moved to San Francisco - My first data science gig

So what is machine learning?

How does this… …become this?

By recognizing this

Sensors + Other Structured and Unstructured Data

How can a machine learn that a cat is a cat?

How can a machine learn that a cat is a cat? What about these?

The cat from Shrek Hairless cat Baby panther and baby tiger

Can your model generalize to new, unseen data?

The importance of data

Messy data – the norm and not the exception

Training examples Machine Learning Algorithm Cat Model

Basic Machine Learning Framework

Gene Sequencing

Smart Grids

COST TO SEQUENCE ONE GENOME HAS FALLEN FROM $100M IN 2001 TO $10K IN 2011 TO $1K IN 2014

READING SMART METERS EVERY 15 MINUTES IS

3000X MORE DATA INTENSIVE

Stock Market

Social Media

FACEBOOK UPLOADS 250 MILLION

PHOTOS EACH DAY

In all industries billions of data points represent opportunities for data science

Oil Exploration

Video Surveillance

OIL RIGS GENERATE

25000 DATA POINTS PER SECOND

Medical Imaging

Mobile Sensors

https://www.washingtonpost.com/posteverything/wp/2015/06/05/the-auto-industry-discriminates-against-women-so-i-quit-my-engineering-job-to-become-a-mechanic/

You can also transform a male-dominant

industry with data science.

On-Board Diagnostics

Diagnostic Trouble Codes (DTC)

Unscheduled repairs

AB1029 – Power steering pump replacementCT3408 – Wheel alignment

Data Sources for Predictive Maintenance

VIN Timestamp DTC Code Odometer

Speed Acceleration

Engine Temperature Engine Torque GPS

Coordinates etc.

VIN Date vehicle in

Date vehicle out Repair code

Parts replaced Warranty claims

Repair Comments

Vehicle Data Car Repairs Data

Predicting Job Type from Diagnostic Trouble Codes (DTCs)

Time

Job Type: Transmission

Job Type: Transmission

Engine Job Type:

Regular check

DTC: B DTC: B,

P, C

DTC: U DTC: B

DTC: B

DTC: B, P, C, U

DTC: P, B, U

DTC: P

DTC: B

DTC: B,P

DTC: B,P

Can the DTCs observed here predict

this Job Type?

Can the DTCs observed here predict this Job

Type?

Can the DTCs observed here predict this Job

Type?

Predicting Job Type: a multi-class classification problem

DF 12 10

DF 12 15

DF 29 80

AB 10 29

AB 16 22

AB 16 25

AB 86 22

CT34 02

CT3408

CT 35 60

CT 24 09

Vehicle Features

Hierarchical Classification Framework

Vehicle Features

DF 12 10

DF 12 15

DF 29 80

AB 10 29

AB 16 22

AB 16 25

AB 86 22

CT34 02

CT3408

CT 35 60

CT 24 09

•  Diagnostic Trouble Codes (DTCs) are not always symptomatic of an ensuing repair.

•  Hence, creating a rule-based approach for repairs based on DTCs has been challenging to construct.

•  A machine learning approach could be a better solution to infer the relationship between groups of DTCs and repairs.

•  Become a mechanic and solve a few car repairs, or become a data scientist and solve millions!

Takeaways

Other Data Science Use-Cases for Connected Cars

Other Data Science Use-Cases

Automated essay scoring

Drug/chemical discovery & analysisRecommendation systems

Fraud detection

blog.pivotal.io/data-science-pivotal/case-studies/pivotal-for-good-with-crisis-text-line-using-text-analytics-to-better-serve-at-risk-teens

blog.pivotal.io/data-science-pivotal/features/pivotal-for-good-with-crisis-text-line-a-first-look

Data Scientist Profile Ask me anything @EstherVasiete