Kag g le Competitions, New Fr iends , New Sk i l l s and New Opportunities

Jo-fai (Joe) ChowData Scientistjoe@h2o.ai

@matlabulus

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About Me

• 2005 - 2015

• Water Engineero Consultant for Utilitieso EngD Research

• 2015 - Present

• Data Scientisto Virgin Mediao Domino Data Labo H2O.ai

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About This Talk

• What happenedo Things I did since I

started participating in Kaggle competitions.

o New opportunities – results of new skills and friends.

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First MOOC Experience

• One of the first Massive Open Online Courses.o Met some new friends.o Decided to collaborate

for fun.o “How about Kaggle?”o “What is Kaggle?”

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First Kaggle Experience

• First time in my lifeo Supervised learning

• Random Forest• Support Vector Machine• Neural Networks

o Train, Validate & Predict.o “Is it black magic?”

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First Kaggle Experience

• Problemso “Hey Joe, you are a nice

guy but we can’t work together.”

o “You love MATLAB so much. You even call yourself @matlabulous on twitter!”

o “We prefer R/Python.”

• Resultso I kept using MATLABo Lone wolfo No collaboration

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Identifying Ski l ls Gap

• Obvious skills gap:o Open-source

Programming langaugeso Machine learning

techniqueso Collaboration

• Kind of relatedo Data visualisationo Handling large datasetso Explaining results

• That competition was a good wake up call.

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From MATLAB to R/Python

MATLAB Python R

Neural Networks ✔️ ✔️ ✔️

Random Forest ✔️ ✔️ ✔️

SVM ✔️ ✔️ ✔️

Other Machine Learning Libraries

Toolboxes (commercial + open

source)

Scikit-learn and many more

CRAN, GitHub(A LOT!)

Data Visualisation I wasn’t good at it anyway …

Matplotlib(plus a lot more

since then)

ggplot2 (WOW!)(plus a lot more

since then)

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What can people do with R?

James Cheshire, UCLLink

Paul Butler, FacebookLink

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Fi l l ing the Ski l ls Gap

• More MOOCo Machine Learning

• Andrew Ng (Coursera)o Data Analysis

• Jeff Leek (Coursera)• R

o Intro to Programming• Dave Evans (Udacity)• Python

• Things I also picked up:o Linux (Ubuntu)o Git o Cloud computingo HTML / CSS

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Learning from other Kagglers

• Continuous learningo Kaggle’s forums and blogs.o New tools and tricks.o Many things you cannot

learn from school.o I am standing on the

shoulders of many Kagglers.

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Side Project 1 – Crime Data Viz

shiny::runGitHub("rApps", "woobe", subdir = "crimemap")

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Other Side Projects

• Colour Palette

o github.com/ woobe/rPlotter

• World Cup 2014 Correct Score Predictiono ML vs. my friendso 10 out of 64 (15.6%)o Friends’ avg. = 4 (6.3%)o github.com/

woobe/wc2014

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Open Up Myself

• Before Kaggle/MOOCo I was drawing a circle

around myself.o Fear of change.o Domain-specific problem

solving.

• After Kaggle/MOOCo Data-driven approach.o Not a subject matter

expert? No worries o Free to try new tools, to

learn and to create.

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New Opportunities

• LondonRo First presentation

outside water industry / academia.

o Very positive feedback.o Led to other projects.o bit.ly

/londonr_crimemap

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New Opportunities

• useR! 2014 (UCLA)o Presented a poster.o Met new friends.o Life-changing event.o github.com/

woobe/useR_2014

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New Friends

Ramnath Vaidyanathanhtmlwidgets

DataRobot

Nick @ DominoDataLab

H2O.ai &John Chambers!

rOpenSciRStudio

Matt Dowledata.table (also at H2O.ai)

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About Domino and H2O

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More Opportunities

• First blog post about H2Oo Things to try after useR!

– Part1: Deep Learning with H2O

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More Opportunities

• Blog post about Domino and H2Oo I did it for fun. I did not

have any expectation.o It helped attract

customers to both Domino and H2O.

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Becoming a Data Scientist

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London Kagglers Assemble

• London Kaggle Meetupo Sep 2015o I met my Kaggle buddy

Mickael Le Galo He is a product data

scientist at Tictrac

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London Kagglers Assemble

• Rossmann Store Saleso We got stuck at top 10% for a long period.o Mickael had a breakthrough in feature

engineering with 48 hours to go.o I re-trained all models and completed

model stacking just a few hours before the deadline (thanks to Domino Data Lab).

o Top 2% finish (our best result so far).

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Joining H2O.ai

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Summary of Benefits

• Directo Identify data science

skills gap.o Learn quickly from the

community.o Expand your network.o Prepare yourself for real-

life data challenges.

• Indirecto You also learn non-ML

skills along the way.o You learn to build small

data products (e.g. graph, web app, REST API) and help others gain insight.

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Strata Hadoop London Talks

• “Model stacking and parameters tuning.”o Wednesday 1 Juneo 7pmo LondonR at Stratao Free event

• “Generalised Low Rank Models.”o Thursday 2 Juneo 2pmo Strata Hadoop main

conference event

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Big Thank You

• London Kaggle Meetup Organisers

• Mickael• Marios • You (Kagglers)

• Prof. Dragan Savic (supervisor at uni. of Exeter)

• Mango Solutions• Virgin Media• Domino Data Lab• H2O.ai

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Any Questions?

• Contacto joe@h2o.aio @matlabulouso github.com/woobe

• Links (All Slides)o github.com/h2oai/h2o-

meetups

• H2O in Londono Coming soon!

• Meetups• Office

o We’re hiring!o www.h2o.ai/careers