Learn the fundamentals of Python through interactive Jupyter Notebooks, discover data management tools & techniques, statistical packages, and explore regression analysis, building your first model, validation and visualisation.
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If you’re looking to learn the foundations of Python, this course is the ideal place to start. An individual subscription gives you 3 months’ online access to:
As Well As
Our Industry and Actuartech Resource Libraries which feature curated additional content to assist you on your data science journey.
You can also request to do the online assignment for an additional fee; and if successful a course completion certificate could be issued.
“Foundations in Python for Actuaries” introduces students to the data science pipeline whilst teaching them the fundamentals of the open source programming language, Python.
Throughout this course, students are exposed to data science topics such as data cleaning, data processing, model building, and visualisation, as well as ethical and wider business considerations when using data science in practice.
This course is presented using Jupyter notebooks with the code and explanations embedded to facilitate hands-on learning.
In this course, we consider training and testing Generalised Linear Models (GLM), and validate the results, as this is easily facilitated by Python. Python has libraries/packages for statistical analysis that allow users to easily fit arange of models from standard GLM’s through to neural networks.
Module 1 of this course introduces Problem Specification which includes an overview of the basic concepts, using Python as a calculator, conditional if/else statements, and Python’s plotting functionalities. It also covers Data Collection which addresses data structures, libraries that can assist with data management, and the automation of data collection from different sources.
Module 2 on Data Management show cases how to write purpose-built functions to manage data, and transform and manipulate a data set in preparation for model fitting.
Module 3 outlines Model Building using the many statistical tools Python has and introduces students to linear regression techniques.
Module 4 covers Visualisation, showing students how to use a variety of statistical functions to produce some basic graphs, which assists in understanding the data better and validating the models.
Further Resources contains additional reading and references to some of the packages discussed, as well as an additional guide for Python.
We have tailored packages available to ensure that corporate teams have the option to attend structured live lessons by our tutors, and the option to request a practical assignment and bespoke feedback. Invoicing option available.
