Demystifying A/B Testing, Putting the Science Back in Data Science

Two more exciting presentations at Data Science Sydney's November session, both from actuarial data scientists, and both on a scientific theme.

Demystifying A/B Testing
A/B Testing is a surprisingly contentious subject, characterised by divergent and seemingly opposing philosophies. The abundance of schemes and materials can often bewilder practitioners, adding complexity to the practice. In this talk, we primarily aim to explain the Neyman-Pearson testing approach from first principles, exploring various testing methodologies and their practical applications in ecommerce settings.

Putting the Science Back in Data Science
In this talk I will explore the application of scientific principles in the field of data science, through a consultant's lens. This will look at the scientific method, highlighting its role in guiding data-driven inquiry, hypothesis testing and the iterative hypothesis generation-testing cycle, the data science model control cycle, and some real-life examples of successful applications.

Speaker bio - Sophia Liu - Demystifying A/B Testing
Sophia is an experienced data scientist and actuary who has dedicated nearly a decade to the general insurance industry. She spent many years in large corporations on insurance pricing and integration of data science to the actuarial discipline. Presently, she works at an international insurtech start-up company named Cover Genius, tackling intricate data science challenges with the aim of pioneering solutions within the ever-evolving insurance sector.

Speaker bio - Anthony Maher - Putting the Science Back in Data Science
Anthony is a data scientist and actuary at KPMG with over 15 years’ experience in data analytics and predictive modelling, with a focus on building data-driven solutions in the health, human services, education and insurance sectors. Prior to joining KPMG in 2019, Anthony worked for over 5 years as a data scientist at Quantium, Australia’s largest specialist data analytics consultancy. Prior to this, he worked in academia, delivering data-driven modelling solutions in biomedical sciences at Imperial College London and Neuroscience Research Australia

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