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.
You can download individual notebooks using the toolbar at the top of the page. Some code can be executed directly on the site such as:[1]
from time import asctime, localtime
print(f"{asctime(localtime())}: Hello, World!")You will have a better experience by launching the notebooks on a Jupyter server using the action buttons at the top:
- JHub opens a JupyterHub server for registered students, offering remote storage and computing resources for an engaging AI-assisted literate programming environment.
- JLite opens a JupyterLite site, accessible without login. It runs entirely in your browser without complicated setup, but it does not support some of the packages required by the notebooks.
- To run all the core packages locally to complete the assignments, you may follow the instruction to install and run Docker.
Click the ⏻ button followed by the ▶ button.