本課程以英語授課。

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.

Students from this course shall learn to: 1. understand the data characteristics and the fitness of different algorithms; 2. pretreat and clean the data; 3. extract and select significant features; 4. explain the analytical results; 5. use R/Python for quick data analytics.

probability, statistics, linear algebra, and programming skills

‧ Strang, G. (2006). Linear Algebra and Its Applications ‧ Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers ‧ Rencher, A. C., & Christensen, W. F. (2012). Methods of Multivariate Analysis ‧ Johnson, R., & Wichern D. (2014). Applied Multivariate Statistical Analysis ‧ Izenman A. J., 1st edition, Modern Multivariate Statistical Techniques ‧ James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning ‧ Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning