Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: CSE410
Module Title: Data Mining and Big Data Analytics
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM1
Originating Department: Computer Science and Software Engineering
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.


Because of the specialist knowledge and techniques the module provides, it serves as a required module for MSc Applied of informatics. As module provides common computing skills, it serves as an optional module for MRes Computer Science, MSc Social Computing and MSc Financial Computing.
Learning outcomes 
A Demonstrate a systematic and critical understanding of the challenges of Data Mining and Big Data Analytics;

B Use advanced 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 advanced analysis of complex data and develop real world applications of Data Mining and Big Data Analytics.

Method of teaching and learning 
Students will be expected to attend two hours of formal lectures as well as to participate in two hours of supervised practical (tutorial / lab) classes in a typical week. Students will be asked to devote six hours of unsupervised time for reflection and consideration of lecture material and will be required to research and read widely on the subject, and where possible use their personal experiences from work placements. Students will be encouraged to explore the challenges and opportunities of analytics delivery within a business.
Syllabus 
 Introduction to data mining and big data analytics principles and challenges (1 week)

 Data management and databases (1 week)

 Data pre-process algorithms and tools (1 week)

 Basic data mining algorithms and tools (1 week)

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

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

 Data mining techniques for building recommender systems (2 weeks)

 Data mining techniques and algorithms for unstructured data analysis (2 weeks)

 Revision (1 week)
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26     13  13    98  150 

Assessment

Sequence Method % of Final Mark
1 Project 40.00
2 Written Examination 60.00

Module Catalogue generated from SITS CUT-OFF: 12/14/2019 3:30:37 PM