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.