Applied Data Science for e-Commerce

Join Arul Bharathi for a look at the application of data science in e-commerce and how data science disrupts this industry in terms of consumer behaviour and experience. The session will cover:

Recommendation Systems – A conceptual walk-through of item-based, user-based collaborative filtering and content-based filtering which are used prevalently in e-commerce

Consumer Behavioural Modelling – A code walk-through and live demo of using tree-based models to decode the general strata of user behaviour and its effect on retention and loyalty (including model building and evaluation of original e-commerce data set; Technology – Python, scikit-learn, fast.ai )

Deep Learning for User Experience – Arul will cover how companies such as Amazon and Alibaba leverage the state-of-the-art techniques of deep learning to enhance their revenue and consumer loyalty. He’ll provide demos of performing product category classification and image-based recommendation systems (technology – PyTorch, fast.ai)

Arul is a Data Scientist, Co-Op student at Realtor.com and is a current graduate of the SFU Big Data Masters program.