The following Q&As are used to explain the teaching plan adopted by this course, Data Engineering and Mining (DEM):
What are the themes of this course?
Terminologies and definitions will be discussed minimally in this course.
Instead, the following three themes will be emphasized and enforced:
Effective methods and practical works,
Trends of data engineering and mining, and
Google Firebase Database and TensorFlow (an AI platform).
Why are the DEM topics diverse?
Unlike the disciplines such as databases or the World Wide Web, DEM is one of the disciplines (like image processing or artificial intelligence) without coherent methods or algorithms.
Many methods (such as artificial neural networks or relevance feedback) are used by DEM and each method is usually not closely related to other methods (like decision trees or sequential pattern mining).
Why are only few topics covered in detail?
In order to show what the DEM is in a semester, this course has to pick a small number of fundamental topics, instead of many topics, to investigate.
Students then use the training to choose appropriate methods for the problems they encounter in the future.
Why are the covered topics fundamental or primitive?
DEM (and information retrieval) is a mature subject.
A wide variety of methods have been used, and the current methods are rather complicated because of its maturity.
In order to cover more topics, the methods introduced in this course are fundamental or primitive.
Students learn how the DEM methods work, and may try to enhance the methods or apply them in their programming exercises or works.