Mortality Experience Analysis in R (R3)

Discover how to use advanced techniques in R, including fitting various machine learning models, data cleaning, and visualisations to understand the drivers of mortality in this end to end walkthrough of mortality modelling.

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Introduction

If you’re looking to learn how to utilise advanced applications of R in an insurance context, this course is ideal for you. An individual subscription gives you 3 months’ online access to:

  • Course materials
  • A personal coding environment through Jupyter Notebooks
  • Discussion forums to engage and collaborate with like-minded individuals
  • Instructional videos
  • Option to ask tutors questions through forums and Q&A sessions
  • Hands-on practical examples linked to actuarial work
  • On demand access

As Well As

Our Data Science Resource Library which features Actuartech and Industry specific curated additional content to assist you on your data science journey.

You can also request to access toa coding project to practice the skills you learn in this course.

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Pick from any of our introductory or advanced courses with bespoke insurance and actuarial specific case studies.

Our platform is easy to use and embeds the coding environment and learning material in one place to enable you to apply data science hands-on.

We provide case studies and projects relevant to actuarial work, and based on relevant datasets provided. You have the option to interact and network with your peers.

Overview

This course guides students through an end-to-end walkthrough of various approaches to modelling mortality rates using machine learning algorithms and packages within R. The aim of this case study is to understand the drivers behind mortality through regression analysis and to balance fitting accuracy with smoothness. The models fitted include GLMs, GAMs, and gradient boosting machines.

We guide the student through importing, cleaning, investigating, modelling, visualising, and interpreting data using interactive Jupyter notebooks. We also investigate the appropriateness of various factors in the dataset and aim to create a smoothed mortality rate with respect to age.

If you're not already familiar with R, we recommend that you start with our Foundations in R course.

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Mortality Experience Analysis in R

Sign up for a free preview of this advanced R case study

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£150 once-off (3-month access)

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Course Structure

Chapter 1 introduces the data science pipeline and defines the problem we wish to investigate.

Chapter 2 sees us performing a preliminary analysis and feature engineering, and we outline any packages we may need going forward.

In Chapter 3 we utilise machine learning techniques to solve the problem we’ve identified and we test alternative models after improving the models fitted.

In Chapter 4 we interpret, validate, and report on the results of the models and their effectiveness in solving our identified problem.

We also make use of visualisation techniques throughout.

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Who's this course suitable for?

  • Individuals with a good grasp of the fundamentals of R.
  • Individuals and teams who wish to explore alternatives to GLMs, particularly in a life insurance context.
  • Individuals wanting to learn more about advanced machine learning models such as GAMs.

Why is this topic important?

  • Balancing accuracy with smoothness is a key concern when deriving pricing rates since mortality is inherently random and being able to address these issues is important within an actuarial context.
  • Advanced machine learning techniques are able to address non-linear relationships between variables that GLMs cannot always pick up.

Short note to say really enjoyed today’s webinar. It had a very clear message. […] fully in agreement with the comments that it is imperative we maintain our professional and ethical stance at all times if we want to continue to be trusted and relied on.

Webinars

I just wanted to say what an interesting presentation that was. Thank you so much for taking the time to put this on for us, it is very much appreciated by all – especially the flexibility around hosting as a webinar instead of the original [in-person] format. It worked very well indeed!

Webinars

I think I’m [one of the first actuaries in my area] who are pointing towards Data Science, creating the new [role] of Actuarial data Scientist. For this reason i [sic] decided to follow a post graduate master in Business Intelligence and Big data analytics. I'm actively following your company and i [sic] think it is one of the best Actuarial consulting company [sic] who [sic] is pointing towards data Science!

Webinars

I love your videos - being free and accessible really helped me. The Q&A session was fantastic! It always comes down to execution and I feel this should always accompany your presentations - answering the question of how will your participants use what you give them. Keep up the great work!

Round Table

Thank you for having me along. I really found it the most motivating conversation I’ve had in a while, and made me think about what I’m trying to achieve within this area. We all need evenings like that to get some perspective on what we *think* is going on and what actually is. It was a very good evening.

Round Table

It was a really good introduction to Data Science and afterwards I felt that I now have a platform that I could use to further my understanding in this area.

Webinars

I am really happy to have been part of the talk. It was very insightful and please keep doing more of this. I am a data science student currently but I have an actuarial background. I worked in life insurance for about 5 months before resigning to do my masters in Data science so that I blend the actuarial world and Data science together. The talk gave me perspective. Even suggested some potential topics for my Thesis.

Webinars

Short note to say really enjoyed today’s webinar. It had a very clear message. […] fully in agreement with the comments that it is imperative we maintain our professional and ethical stance at all times if we want to continue to be trusted and relied on.

Webinars

I just wanted to say what an interesting presentation that was. Thank you so much for taking the time to put this on for us, it is very much appreciated by all – especially the flexibility around hosting as a webinar instead of the original [in-person] format. It worked very well indeed!

Webinars

I think I’m [one of the first actuaries in my area] who are pointing towards Data Science, creating the new [role] of Actuarial data Scientist. For this reason i [sic] decided to follow a post graduate master in Business Intelligence and Big data analytics. I'm actively following your company and i [sic] think it is one of the best Actuarial consulting company [sic] who [sic] is pointing towards data Science!

Webinars

I love your videos - being free and accessible really helped me. The Q&A session was fantastic! It always comes down to execution and I feel this should always accompany your presentations - answering the question of how will your participants use what you give them. Keep up the great work!

Round Table

Thank you for having me along. I really found it the most motivating conversation I’ve had in a while, and made me think about what I’m trying to achieve within this area. We all need evenings like that to get some perspective on what we *think* is going on and what actually is. It was a very good evening.

Round Table

It was a really good introduction to Data Science and afterwards I felt that I now have a platform that I could use to further my understanding in this area.

Webinars

I am really happy to have been part of the talk. It was very insightful and please keep doing more of this. I am a data science student currently but I have an actuarial background. I worked in life insurance for about 5 months before resigning to do my masters in Data science so that I blend the actuarial world and Data science together. The talk gave me perspective. Even suggested some potential topics for my Thesis.

Webinars
Get started

Mortality Experience Analysis in R

Sign up for a free preview of this advanced R case study

Free Preview

Preview

£150 once-off (3-month access)

Enroll Today

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