A0L256Q Data Mining and Data Analysis

(2 Credits, 32 Hours; Course Category: Elective Course; Specialty: Software Engineering; Prerequisite: A0L236Q (Programming), A0L238Q (Data Structure)
Along with the widespread use of IT techniques, many companies have collected huge business related data from their everyday activities. It is more and more important to distill valuable information to help business decision. And data analysis is becoming more and more popular. The requests for qualified data analysts are then becoming indispensable for business enterprises to ensure their competitive power and advantage.
The course tries to expand the abilities of juniors majoring in software engineering. It is necessary for them to master concepts and techniques closely related to practical implication. From the instruction of the course they will master the basic knowledge of data mining and analysis, frequently used methods and models to enhance the students’ hand-on ability and innovative ability. Aiming to introduce the basic concepts and techniques of data analysis, the course covers many topics including the models, algorithms and techniques for users to understand the data, and make appropriate decisions based on the data. The first part of the course is focused on distilling from statistics, one popular technique helpful for users to understand data. The second part of the course is data mining, which aims to discover valuable knowledge from the huge collection of data. The third part is the introduction of popular software used now in real applications to data analysis, such as Data Warehouse, OLAP (On-Line Analysis Processing), etc.
To ensure the ability of data analysis after this course, many manual computation questions will be used during the course. And many practices will be proposed as projects for students to use given data and software (SPSS, Clementine – now called SPSS Modeler, Weka)for data analysis. Students are required to finish a document for each project to demonstrate their understanding of the related algorithms, and data processing procedure. By those assignments, students can not only understand the related concepts, algorithms and techniques of data analysis, but also master the necessary skills to carry out data analysis, like using software, and writing documents.