Data analytics has become an increasingly vital component across a wide range of industries. This course is designed to make sense of commonly encountered terms such as data mining, big data, artificial intelligence, machine learning, and deep learning, which frequently appear in contemporary discourse. We will dive into the fundamental principles underlying these buzzwords and explore the diversified methodologies, including multivariate statistical inference and both supervised and unsupervised learning algorithms. The course will employ R or Python as key analytical tools, enabling participants to integrate theory with practice and apply these methodologies to real-world challenges.

Structured as a blended learning format, this course combines several pedagogical elements, such as asynchronous video lectures for self-paced learning, interactive in-person discussions, hands-on assignments, and a collborative group project. This dynamic format ensures the depth of understanding and engagement. You are highly encouraged to attend the initial session to evaluate how well the course aligns with their academic and professional needs.

Students who are interested in enrolling MUST attend the first lecture and remain until the end of the class. ADDITIONAL enrollment codes will be distributed during the final part of the first lecture. Students who do not attend the first lecture should NOT email to request an enrollment code. 

1. 本校尚無訂定 A+ 比例上限。
2. 本校採用等第制評定成績，學生成績評量辦法中的百分制分數區間與單科成績對照表僅供參考，授課教師可依等第定義調整分數區間。詳見學習評量專區 (連結)。