Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: IFB214TC
Module Title: Data Mining Applications
Module Level: Level 2
Module Credits: 2.50
Academic Year: 2021/22
Semester: SEM1
Originating Department: School of Intelligent Finance and Business
Pre-requisites: N/A
   
Aims
This module aims to provide students with an in-depth understanding of the key technologies of Data Mining and Big Data Analytics. It covers not only classical data mining techniques but also techniques in addressing issues and challenges raised in Big Data Analytics by introducing scalable parallel data mining algorithms which can be executed on computer clusters, including data stream mining techniques and algorithms for the analysis of high velocity data as well as software systems used for Data Mining and Big Data Analytics.
Learning outcomes 
A Demonstrate a systematic and critical understanding of the challenges of Data Mining and Big Data Analytics;

B Use algorithmic knowledge and key techniques to tackle the challenges of Data Mining and Big Data Analytics;

C Show expertise in the use of state-of-the art software tools for the implementation of solutions to Data Mining and Big Data Analytics;

D Conduct an analysis of complex data and develop real world applications of Data Mining and Big Data Analytics.
Method of teaching and learning 
Formal lectures including the use of labs/tutorials to enable the appreciation of data mining and the ability to use the relevant hardware tools.
Syllabus 
1. Introduction to data mining and big data analytics principles and challenges (1 week)

2. Data management and databases (1 week)

3. Data pre-process algorithms and tools (1 week)

4. Basic data mining algorithms and tools (1 week)

5. Parallel data mining techniques for large dataset analysis (2 weeks)

6. Data mining algorithms and tools for the analysis of fast streaming real time data (2 weeks)

7. Data mining techniques for building recommender systems (2 weeks)

8. Data mining techniques and algorithms for unstructured data analysis (2 weeks)
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 14    7      54  75 

Assessment

Sequence Method % of Final Mark
1 Group Work (Report Or Powerpoint Slide)(1000 Words) 50.00
2 Business Simulation(1000 Words) 50.00

Module Catalogue generated from SITS CUT-OFF: 10/21/2020 7:19:40 PM