The combination of data science and professional business skills is already improving the impact and relevance of analytics, helping provide new solutions and empowering leadership teams to make better, data-driven decisions.
Pick from any of our introductory courses to Python as well as advanced course with bespoke insurance and actuarial specific case study
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 Assignments relevant to actuarial work; and based on relevant datasets provided. You have the option to interact and network with your peers.
Learn the fundamentals of Python through interactive Jupyter Notebooks. Discover data management tools & techniques using pandas and NumPy. Explore regression analysis, model building, validation & visualisations.£150 once off fee: 3 months' access.
This is a taster to R & Python for beginners. Through a combination of webinars & interactive notebooks, users are able to get a taste of R and Python with the use of a practical example. £40 once off fee: 3 months' access.
We explore the Lee-Carter model to forecast mortality rates and introduce other forecasting techniques such as time series analysis and more advanced machine learning. PREREQUISITE: Foundations in Python. £150 once off fee: 3 months' access
We utilise data science techniques to analyse motor claims By fitting advanced data science models we aim to predict the frequency of these claims to assess risk, in the context of pricing and reserving £150 once off fee: 3 months' access
By joining our community, you will have the opportunity to learn best practice data science and technology techniques, collaborate with fellow actuaries and data scientists and be empowered to derive optimal insights from data.
Select from one of our three training paths, or contact us to provide you with guidance on where to start
Select from one of our training paths, or contact us to provide you with guidance on where to start