how to become a data scientist in 6 months

HOW TO BECOME A DATA SCIENTIST IN 6 MONTHS: A HACKER’S APPROACH TO CAREER PLANNING

TETIANA IVANOVA

WHO AM I?

WHY DID I HACK MY CAREER?

…WHY WOULD IT EVEN WORK AS AN APPROACH? - SOME HANDWAVY EXAMPLES

HOW TO BECOME A DATA SCIENTIST IN 6 MONTHS

WHY DOES OUR SOCIETY SUPPORT HIGHER EDUCATION?

▸ Social pressure insufficient to explain equilibria

▸ Historical changes

▸ Robin Hanson: it’s not about social pressure, it’s about prestige/status signalling

PRESTIGE IS EXPLOITABLE

▸ Public and private tuition costs are skyrocketing (USA market)

Source: http://www.theatlantic.com/education/archive/2015/05/the-real-cost-of-college/393086/


PRESTIGE IS EXPLOITABLE

▸ …without providing much real value

Best predictors of employee success according to meta-analysis performed by Schmidt and Hunter:

▸ 1. Work sample tests (.54)

▸ 2. GMA tests aka IQ (.51)

▸ 3. Employment interviews -- structured (.51)

▸ … 14. Job experience --years (.18)

▸ … 16. Years of education (.10)

Source: Schmidt, F.L. & Hunter, J.E. (1998) The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings,” Psychological Bulletin, 124, 262–274.

Article: http://bobsutton.typepad.com/my_weblog/2009/10/selecting-talent-the-upshot-from-85-years-of-research.html


http://bobsutton.typepad.com/my_weblog/2009/10/selecting-talent-the-upshot-from-85-years-of-research.html

WHAT DID IT MEAN FOR ME?

▸ Cost of a Master’s degree for a non-EU student: £28k

▸ Starting date: October 2016 (if accepted!)

▸ Funding prospects for PhD: ahem… slim.

▸ Improved career prospects?


SO, THINGS NOT TO DO IF YOU WANT A RADICAL CAREER CHANGE

▸ Hope that degree is a ticket to a rosy future

▸ Expect to do it all on your own

▸ Not allow for a margin of error

▸ Burn your bridges

▸ Hope that employer can read your mind


WHAT DID I DO IN THE END?

▸ Set a realistic timeframe

▸ Don’t trust yourself with sticking to deadlines

▸ Make a study plan

▸ Prepare for uncertainty


TIME DISCOUNTING AND WILLPOWER

▸ Willpower is a limited resource

▸ External reinforcement and social cost of default

▸ Limit your choices


TIME MANAGEMENT TECHNIQUES

▸ Pomodoro

▸ Spaced repetition

▸ Value of sleep

▸ Every little counts. Even 5 minutes!

▸ Kill your perfectionism


THE N WORD

NETWORKING

NETWORKING FOR NERDS

▸ Understand your in-group

▸ Signal belonging

▸ Positive reinforcement

▸ This is still work!

▸ Don’t expect immediate results


RESOURCES FOR DATA SCIENCE TRANSITION

▸ Meetups! PyData, LMLP, Data Science journal Reading club, etc. Or start one yourself!

▸ Kaggle and hackathons

▸ Coursera

▸ Know your stats. “Elements of Statistical Learning”, “An Introduction to Statistical Learning with Applications in R”, Jaynes

▸ Pester people!

▸ Read the white papers. And the blogs. DeepMind, Karpathy, Google it!


DON’T GET STARTED

▸ Complex projects require extended planning

▸ You need the right questions


QUESTIONSPresenter:

Tetiana Ivanova, Data Scientist, NanotechGalaxy

[email protected]

twitter: @eoly23

LinkedIn: https://uk.linkedin.com/in/tetiana-ivanova-1886b14

mailto:[email protected]

https://uk.linkedin.com/in/tetiana-ivanova-1886b14


SOME LEARNING RESOURCES

‣ Andrew Ng’s Coursera: https://www.coursera.org/learn/machine-learning

‣ TensorFlow course on Udacity: https://www.udacity.com/course/deep-learning--ud730

‣ Stanford course on Convolutional NNs: http://cs231n.stanford.edu/syllabus.html

‣ Kaggle. In case you haven’t heard :) https://www.kaggle.com

‣ Fellowships: S2DS ( http://www.s2ds.org ), ASI ( http://www.theasi.co )

‣ Meetups: PyData (duh), London Data Science (#ODSC), London Kaggle Meetup, London Machine Learning Practice Meetup (LMLP), London Machine Learning Meetup (different meetup from LMLP but both hosted at CodeNode!), London Data Science Journal Club, events at Google Campus, and many many more.

‣ Books: Probabilistic Theory of Pattern Recognition ( http://www.szit.bme.hu/%7Egyorfi/pbook.pdf ), Elements of Statistical Learning ( http://statweb.stanford.edu/~tibs/ElemStatLearn/ ), Probability Theory by ET Jaynes ( http://bayes.wustl.edu/etj/prob/book.pdf - note this is not the whole text)

‣ A more comprehensive list of MOOCs in a Google Doc (second tab): https://docs.google.com/spreadsheets/d/1J0sRe-84ynJI1LF8XHESAgnMNNc1c7lmf-HGsCrcbm8/edit?usp=sharing

‣ Get involved: Machine Learning For Social Good Facebook group https://www.facebook.com/Machine-learning-for-good-259545294213595/

https://www.coursera.org/learn/machine-learning

https://www.udacity.com/course/deep-learning--ud730

https://www.kaggle.com

http://www.s2ds.org

http://www.theasi.co

http://www.szit.bme.hu/%7Egyorfi/pbook.pdf

http://statweb.stanford.edu/~tibs/ElemStatLearn/

http://bayes.wustl.edu/etj/prob/book.pdf

how to become a data scientist in 6 months

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