Data analytics is increasingly recognized as a pivotal element across various sectors. This course aims to demystify prevalent jargon, including data mining, big data, artificial intelligence, machine learning, and deep learning, prevalent across diverse media platforms. We intend to dissect the foundational concepts associated with these buzzwords, in addition to exploring a spectrum of methodologies such as multivariate statistical inference, alongside unsupervised and supervised learning algorithms. Throughout the course, R or Python will serve as the instrumental tools, facilitating the analysis, synthesis, and application of these methodologies in real-world scenarios.

This course is meticulously structured in a blended learning format, encompassing a variety of components: asynchronous video content for independent learning, interactive face-to-face discussions, practical homework exercises, and a culminating group project.

Prospective participants are encouraged to attend the inaugural lecture to gauge the course's alignment with their academic and professional aspirations. Access codes for course registration will be issued post the initial session enrollment.