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This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
From Actuarial Science to
Data Science
Advice for Actuaries Considering the Move into
Analytics
Genevieve Hayes
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
Actuarial Science vs Data Science
Actuary Data
Scientist
Finance/
EconomicsStatistics
Domain
Knowledge
Programming
Machine
Learning
Communication
Valuation
& Pricing
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
1800 1900 2000
1809: Gauss publishes
monograph introducing
normal distribution
1936: Alan Turing’s
paper on computability
published
2000’s: the rise of
data analytics
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
Who is Using Data Analytics?
The real question is, who isn’t?
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
“I’ve always felt (analytics)
was overrated. Obama got
the votes much more so
than his data processing
machine. And I think the
same is true with me” –
Donald Trump (May 2016).
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
Data Science in the Wild
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
“Don’t bother with any
candidate who can’t code.”- Thomas H. Davenport and D.J. Patil (October 2012). Data Scientist: the Sexiest Job of the 21st Century. Harvard Business Review.
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
Typical Skill Requirements for Data Scientist Roles
• Tertiary qualifications in a quantitative discipline (e.g. mathematics,
statistics, computer science, etc) – often Masters or PhD level required
or highly regarded;
• Programming experience in SAS, Python, R, SQL, etc;
• Experience in applying statistical data analysis techniques, such as
predictive modelling and machine learning;
• Experience in data exploration, manipulation and visualisation;
• Strong written and verbal communication skills, including experience
in communicating technical concepts to a non-technical audience;
• Experience working in a similar role/industry.
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
Typical Skill Requirements for Data Scientist Roles
• Tertiary qualifications in a quantitative discipline (e.g. mathematics,
statistics, computer science, etc) – often Masters or PhD level required
or highly regarded;
• Programming experience in SAS, Python, R, SQL, etc;
• Experience in applying statistical data analysis techniques, such as
predictive modelling and machine learning;
• Experience in data exploration, manipulation and visualisation;
• Strong written and verbal communication skills, including experience
in communicating technical concepts to a non-technical audience;
• Experience working in a similar role/industry.
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
Resources for Learning Data Science SkillsProgramming• Code Academy (www.codecademy.com)
• Data Camp (https://www.datacamp.com/)
• Python Challenge (www.pythonchallenge.com)
Machine Learning• Stanford University Machine Learning MOOC
(https://www.coursera.org/learn/machine-learning)
• University of Washington Machine Learning Specialization
(https://www.coursera.org/specializations/machine-learning)
Data Visualization• Perceptual Edge (http://www.perceptualedge.com/library.php)
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.
Applying Data Science Skills To Actuarial Work
This presentation has been prepared for the 2016 General Insurance Seminar.The Institute Council wishes it to be understood that opinions put forward herein are not necessarily
those of the Institute and the Council is not responsible for those opinions.