This course aims to explain commonly used terms like data mining, big data, artificial intelligence, machine learning, and deep learning. Students will learn fundamental principles and methodologies, including multivariate statistical inference and both supervised and unsupervised learning algorithms. The course will use R or Python as the primary analytical tools for practical application. It is structured as a blended learning format, combining pedagogical elements such as asynchronous video lectures for self-study, paced learning, interactive in-person discussions, hands-on assignments, and a collaborative group project. This dynamic format ensures both depth of understanding and engagement. We encourage all students to attend the first session to determine if the course meets their needs.

To ensure a fair enrollment process, students MUST attend the first lecture in its entirety to receive enrollment codes, which will be distributed at the very end of the session. Students who do not attend the first lecture should NOT email to request a code.

1. Understand data characteristics and the fitness of different algorithms
2. Pretreat and clean data
3. Extract and select significant features
4. Explain analytical results
5. Use R/Python for quick data analytics