Training with Actuartech
The home of actuarial technology.
View our range of courses below.
This bundle offers an introduction to performing AI risk management, discussing guidelines for AI risk management, walking through explainability techniques and best practice, with a curated resource library and assignment. £450 for 3 months' access.
£450
Discover how to interpret the principal requirements of IFRS 17 through learning the basics of IFRS 17, including measurement models, terminology, and hands-on calculations, with a supporting resource library. £325 once-off fee for 3 months' access.
£325
Learn the fundamentals of Python, discover data management techniques, statistical packages, and explore regression analysis, building models, validation, and visualisations, supplemented by our resource library. £325 once-off fee: 3 months' access.
£325
Learn to code in Python. This bundle includes Foundations in Python, the practical assignment, and access to the Resource Library. £450 for 3 months' access.
£450
Learn the fundamentals of R through Notebooks, discover data management techniques, statistical packages, and explore regression, building models, validation, and visualisations, supplemented by a resource library. £325 once-off: 3 months' access.
£325
Learn to code in R. This bundle includes Foundations in R, the practical assignment, and access to the Resource Library. £450 for 3 months' access.
£450
Discover how to use advanced techniques in Python such as time series analysis, Lee-Carter model, data cleaning & visualisations to forecast mortality rates in an end-to-end walkthrough. Resource library included. £325 once-off: 3 months' access
£325
Learn advanced techniques, such as fitting regression and classification ML models, feature engineering, and visualisations, investigating lapse rates in this end-to-end walkthrough, with a resource library. £325 once-off fee: 3 months' access
£325
Explore utilising advanced techniques in Python, including traditional actuarial methods and data cleaning, in this end-to-end walkthrough of predicting claims' frequency, supplemented by a curated resource library. £325 once off: 3 months' access
£325
Learn advanced techniques in R, such as fitting various ML models, data cleaning, and visualisations to understand the drivers of mortality in this end-to-end walkthrough, supplemented by a resource library. £325 once-off: 3 months' access.
£325
Discover how to use visualisation techniques in Python to present key messages from complex data with hands-on examples, including dashboarding to identify patterns and trends, supplemented by a resource library. £325 once off: 3 months' access.
£325
Discover how to use visualisation techniques in R to present key messages from complex data with hands-on examples, including dashboarding to identify patterns and trends, supplemented by a curated resource library. £325 once-off: 3 months' access.
£325
Discover how to use advanced techniques such as fitting regression & classification ML models, feature engineering, and reporting in this end-to-end walkthrough investigating lapse rates. Resource library included. £325 once off: 3 months' access
£325
Explore building a technical tariff through GLMs in R to learn the basics of non-life pricing using data science techniques through interactive Jupyter Notebooks. £300 once-off fee: 3 months' access.
£300
Discover how to use advanced techniques for non-life pricing such as regression models and calibrating machine learning models in R through interactive Jupyter Notebooks. £300 once-off fee: 3 months' access or pay monthly.
£300
We offer various different payment plans depending on your training needs and budget. Please refer to our specific training courses for more information, here: https://training.actuartech.com/ or contact us at [email protected] for our corporate rates.
Our training platform provide training in common practical areas of data science but is focussed specifically on actuaries and insurance professionals wanting to apply data science in their fields. We provide relevant data science in the context of insurance and actuarial use cases.
We aim our case studies at actuarial & business problems and using data science techniques to solve these. In some cases the problem in question may be relevant to non insurance problems. Feel free to reach out if you need specific guidance in this area.
In total our courses range between 8 and 16 hours of effort however we recommend you spend 2-3 hours per week on the content for the duration of your access to our platform.
We recommend 2-3 hours per week, in addition to an hour-long weekly live lesson we offer. Depending on your level of coding experience and general interest, that may increase. We recommend supplementing our platform with personal coding. We feel one of the best ways to learn is trough hands-on training, such as exploring a dataset or experimenting with various models.
Our basic subscription typically allow access for 3 months in total. This gives enough time to follow through the weekly Live Lessons and go through the course in your own time. Please enquire for specific subscription options: [email protected]
Our terms and conditions are here: https://training.actuartech.com/pages/terms
Our Foundations in R or Python courses are suited to anyone wanting to get a basic overview of R or Python and is perfect if you have no previous experience with coding or if you want a quick refresher of the basics. Access our courses here: https://training.actuartech.com/
We would recommend you start with the Case Studies. Please note that we require that you complete and pass our Assignment in the language you are experienced in before proceeding to the Case Studies.
Our Foundations in R course covers some initial actuarial concepts. Combined with the case studies you will get a good understanding of the specific actuarial skills involved in tackling the specific problem in question. You are also welcome to reach out to us for specific help on any actuarial related matters: [email protected]
We recommend having a good foundation in statistics (understand mean, correlation, error measures, typical distributions, etc.) and knowledge of topics such as linear regression and generalised linear models (GLMs). We also assume knowledge of standard insurance terms such as present value, interest rates, premiums, lapses, mortality rates, reserving, sum assured, exposure, risk, policy, etc.
A Jupyter Notebook is an interactive development environment that supports coding blocks and text blocks allowing you to combine the data, coding and description of your data science pipeline all in one place. This allows users to run code and document it. Jupyter supports primarily Python, R, and Julia but supports many others through each language's respective "kernel". Please also refer to this webinar providing an introduction to Jupyter: https://www.actuartech.com/webinars/the-power-of-computational-notebooks-introducing-jupyter
Our platform supports at least all the major browsers available on both Windows and Mac including certain mobile devices.
Please feel free to reach out to us at [email protected] or if your query is relevant to our wider community please also make use of our topic specific community forums.
We recommend you start by looking at the discussion forums to start with. You are welcome to email us at [email protected] and we will get back to you as soon as we can. Please include a screenshot if appropriate of your technical query so we can assist.
No, this is not required as the entire platform runs in your browser. We recommend however that you install the relevant language on to your system (with the necessary approvals required for you to do so) to get the best learning and practical experience alongside your formal training with us - we can provide assistance on how to install the relevant software successfully.
In some cases you may have to use your personal PC to complete the course, however feel free to put us in touch with your IT should they have specific questions regarding the security of our platform.
We recommend at least a 1 Mbps internet speed. The Notebooks themselves are not very internet-intensive but some components of the Resource Library (such as embedded videos) will require a faster internet connection to ensure a smoother user experience.
Try restart your Notebook by clicking the "Kernel -> Restart & Run All" shortcut button at the top of your notebook. If the problem persists, try refresh your browser window.
Notebook cells come pre-run when you open the course. However, you cannot run the notebook with adjust code, restart the notebook by going to "Kernel -> Restart & Run All".
Our Notebooks are set-up with a specific set of packages, called a stack (also includes the programming language version). This contains a host of relevant data science packages and some specific to certain sections. It will be updated from time-to-time to stay relevant. If you have a specific package you would like to use, let us know at [email protected] so we can research that and potentially include in the future.
We use Python 3 through version 3.8x - or next latest.
The Data Science Pipeline is a helpful framework for structuring problems. The steps include: 1. Problem Specification 2. Data Collection 3. Data Management 4. Model Building 5. Reporting and Valdiation Visualisation and data goverance & ethics underpins the pipeline and is applied across all stages. We aim to apply the framework across all courses and Case Studies.
We do address these both directly (accessing .csv and .xls data is taught in Foundations) and indirectly (additional resources on accessing SQL databases).. If you have specific questions please contact us on [email protected]
One of our main aims is to show case how data science techniques could be applied to actuarial problems in order to gain business insights or add value. Our use cases cover some of the technical content in order for you to obtain an understanding of the data science pipeline applied to the particular problem in question. In addition our Resource Library provides a rich set of additional technical and non technical material for additional support.