This is a jupyter book for the course CS5483 Data Warehousing and Data Mining at City University of Hong Kong. The course introduces the subject of automatic/semi-automatic knowledge discovery from data. It gives an overview of the principles and applications of supervised/unsupervised learning, and reinforces the concepts with hands-on experience using some state-of-the-art software. The students will learn to apply different learning algorithms and evaluate them with appropriate statistical performance metrics and techniques that avoid overfitting. Students will also learn how corporations manage their data with dimensional models and OLAP for simple and fast data analysis.
git submodule init Course Intended Learning Outcomes (CILOs):
Identify and explain the main characteristics of different data warehousing and data mining techniques through observation of their operations.
Critically evaluate the strengths and limitations of current data warehousing and data mining techniques.
Apply the main algorithms in data warehousing and data mining in a computationally efficient way.
Design new solutions for data warehousing and data mining problems by improving and combining current techniques.