Building on the previous module, participants will further develop their expertise in unsupervised learning. They will learn the core concepts of K-means and hierarchical clustering, and how they are related to but differ from dimensionality reduction and when they can aid supervised learning.

Learn how to execute unsupervised machine learning. Participants will learn the essential concepts of dimensionality reduction and its applications. They will learn how to use a cutting-edge Tidymodels framework in the R data science ecosystem when executing PCA (linear) and UMAP (nonlinear), interpreting the results of dimensionality reduction. The course will guide participants to apply PCA to solve real-world business problems while also discussing the potential use of PCA and UMAP in the processes of supervised learning.

Participants will learn the differences between solving regression and classification problems in the workflow of predictive models like performance metrics. They will learn how to run and draw insights from both binary classification and multi-class classification tasks.

This module is part of a sequential programme and is not available on a standalone basis.

This module will equip participants with the fundamental concepts and systemic framework of supervised machine learning. They will be introduced to a cutting-edge unified interface to predictive models and machine learning in R language’s data science ecosystem, namely Tidymodels. Through theory, live demonstrations and lab sessions, participants will develop a practical understanding of how these technologies are applied.

This module is part of a sequential programme and is not available on a standalone basis.

In the wake of COVID-19, massive amounts of supplier data that can affect risk assessment are continuously changing. RPA plays an important role in acquiring and inputting this data, while AI and machine learning can quickly assess the associated risk factors involved.

This module introduces packages of Shiny, plumber, and pins that will equip participants with productivity tools in the R data science ecosystem. Beyond the R Markdown documents, participants will be shown how to put their machine learning algorithms and predictive models into production. They will learn to run an interactive dashboard that deploys the results of their models and algorithms on the web.

This module is part of a sequential programme and is not available on a standalone basis.

The Fourth Industrial Revolution has begun, spurring a wave of digital transformation that has spread across industries, sectors, and the wider economy. For many working in service-based organisations and “white collar” operations, this trend has led to a focus on the automation of business processes, with new digital technologies such as Robotic Process Automation and Intelligent Assistants increasingly taking root. The resulting impact on work in commercial and government sectors can be radical and disruptive.

Disaster often strikes with little warning, but if you’re prepared to act, you can move forward with minimal damage to your business operations. An integral part of the crisis-management approach involves taking steps to prevent crises on top of responding to them when they happen. The causes of many common online crises can be stemmed with the use of appropriate policies, agreements, and internal processes.

Online reputation can be affected by a myriad of factors - negative reviews (whether genuine or malicious), acts of sabotage by competitors, cyberattacks, etc. In the face of negative online content targeted against a business, a paramount concern would be to manage and respond to such content constructively. However, there may arise situations where a business must take action against negative content arising out of malice and deliberate acts of sabotage so as to be accountable to its stakeholders.

The key lies in responding quickly, and in a transparent way which shows your customers, and prospective customers, that you truly care about them, and are willing to go the extra mile to prove you are the brand they think you are.

In this module, participants will explore how to diffuse and manage online crises from a communications perspective with a view of mitigating the harm occasioned by such crises.