Handbook

The proliferation of digital platforms, IoT devices, and online transactions has led to an explosion of data. In today's data-driven world, organizations are increasingly leveraging advanced analytics methods to extract business insights from their forever growing data. Predictive analytics have emerged as pivotal tools for organizations to gain insights into customer behaviours, market trends, and operational efficiencies.

This course will guide you through fundamental business analytics methods to conduct predictive analytics. These methods are categorized into Supervised, Unsupervised, Reinforcement learning. We primarily use supervised learning and Reinforcement learning methods for predictive analytics projects. However, in the course of a predictive analytics project, analysts may use unsupervised learning techniques to understand the data and to expedite the model building process.

You will apply these business analytics methods using Python. Python is a versatile programming language with rich libraries for data wrangling and analysis. Throughout this course, hands-on experience and business case studies will be included alongside the technical aspects of Python coding.