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Girls Who Code in Data Science
- 1. Girls Who Code and Do Data Science @EstherVasiete Data Scientist July 12th, 2016 Girls Who Code Summer Immersion Program
- 2. About me • Born and raised in Barcelona • Bachelor’s Degree in Electrical Engineering
- 3. 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
- 4. 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
- 5. About me • Once I graduated, I moved to San Francisco - My first data science gig
- 6. So what is machine learning?
- 7. How does this… …become this? By recognizing this Sensors + Other Structured and Unstructured Data
- 8. How can a machine learn that a cat is a cat?
- 9. 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
- 10. Can your model generalize to new, unseen data?
- 11. The importance of data
- 12. Messy data – the norm and not the exception
- 13. Training examples Machine Learning Algorithm Cat Model Basic Machine Learning Framework
- 14. 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
- 16. You can also transform a male-dominant industry with data science.
- 17. On-Board Diagnostics Diagnostic Trouble Codes (DTC) Unscheduled repairs AB1029 – Power steering pump replacement CT3408 – Wheel alignment
- 18. 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
- 19. 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?
- 20. 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 CT 34 02 CT 34 08 CT 35 60 CT 24 09 Vehicle Features
- 22. • 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
- 23. Other Data Science Use-Cases for Connected Cars
- 24. Other Data Science Use-Cases Automated essay scoring Drug/chemical discovery & analysisRecommendation systems Fraud detection
- 26. Data Scientist Profile Ask me anything @EstherVasiete