Explore advanced queries in SQL centred around data management techniques showcased through an indicative IFRS 17 example.
If you’re looking to learn more about advanced functionalities of SQL, this course is ideal for you. An individual subscription gives you 3 months’ online access to:
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.
The purpose of this course is to expose students to advanced uses of SQL and gives an overview of how to run SQL queries. The course builds on the “Foundations in SQL for Actuaries” course by using Jupyter Notebooks to connect to a MySQL workbench and exploring complex queries.
Throughout this course, students are exposed to data science topics such as data storage, data processing, and data manipulation, as well as ethical and wider business considerations when using data science in practice.
Furthermore, we use an indicative IFRS 17 dataset as an example to showcase advanced queries. We also discuss how to create visualisations in SQL.
This course is presented through our training platform and uses Jupyter Notebooks, with the code and explanations embedded, to facilitate interactive coding. This allows you to run the code and make your own tweaks to see how it affects the outcome. In order to offer the interactive coding environment, we make use of Python to integrate the SQL syntax into our environment.
Chapter 1 introduces the Problem Specification, beginning with an overview of MySQL and Python. It also provides a recap of basic SQL concepts.
Chapter 2 covers Data Collection which addresses how to connect and retrieve a database and discusses foundational and advanced queries on tables.
Chapter 3 on Data Querying showcases how to use different filters to get the desired data, how to flag and replace error entries, and how to use an absolute function.
Chapter 4 outlines Data Management which includes an introduction to views, how to create views, how to use data management techniques to manipulate data, performing calculations using advanced SQL queries, and creating a backup of results in databases.
Chapter 5 is an overview of Data Science Model Building and outlines some of the various statistical tools Python has and discusses manipulation and calculation using the SQL data model rather than building a new model.
Chapter 6 on Visualisation shows 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.
The Appendix contains additional reading and references to some of the packages discussed, as well as additional guides for MySQL and 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.
