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Learning Outcomes

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Aim:
This module provides a general introduction to using various IT-driven tools, software and techniques for decision making. Various algorithms will be taught to provide the theoretical foundations and tools will be introduced to aid in applying these techniques to real-world data. Students will also learn how to mine data and apply data analytic tools and techniques to gather useful information from the data. Such information could be useful for decision making and also provide insights to optimize business processes.

Objectives:

Understand the conceptual foundations of decision making processes at the individual and organizational levels, and IT-driven decision making domains and application
Understand the methodological foundations of various decision analysis and decision making methods, algorithms and techniques
Acquire and mine data for knowledge discovery
Apply analytic and visualization tools, techniques, and methods for individual and organizational level decision support

Prerequisites

Top(CS1010 or its equivalents) and IS1112 and (MA1521 or MA1102R)

Teaching Modes

TopIndependent self-study, Lectures, and Tutorials/Lab

Schedule

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Week	Lecture	Remarks
Week 1 10 Aug to 14 Aug	NO LECTURE	10 Aug is Public Holiday
Week 2 17 Aug to 21 Aug	L0 Course Overview L1 Introduction
Week 3 24 Aug to 28 Aug	L2 Introduction to R L2 Introduction to R (E-Lecture) (NO TUTORIAL - Tutorials start in Week 4)
Week 4 31 Aug to 4 Sep	L3 Linear Regression
Week 5 7 Sep to 11 Sep	L4 Classification (Part 1)
Week 6 14 Sep to 18 Sep	L5 Classification (Part 2)
Recess Week 19 Sep to 27 Sep
Week 7 28 Sep to 2 Oct	L6 Web Mining
Week 8 5 Oct to 9 Oct	L7 Text Mining
Week 9 12 Oct to 16 Oct	L8 Decision Tree
Week 10 19 Oct to 23 Oct	L9 Clustering
Week 11 26 Oct to 30 Oct	L10 Applications of Decision Making and Course Summary
Week 12 2 Nov to 6 Nov	Group Presentations
Week 13 9 Nov to 13 Nov	Group Presentations (NO TUTORIAL)

Assessment

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Continuous Assessment (CA)	Class Participation/Forums/Tutorials	5%
	Individual Assignment(s)	15%
	Group Project	30%
Finals	Exam	50%
Total		100%

Project Presentation Schedule
2 Nov
EA : Andre, Dion, Eddy, Tewan, Thiru
Starbucks: Wanzheng, Hu Xi, Chuxin, Zijian, Baoyao
AXA: Wilson, Martinus, Shumin, Hui Yi, Shao Quan
PAP: Celeste, Yin Xiang, Jing Yu, Niveetha, William
Adidas: Justin, Meng Lu, Khanh, Uyen, Ruihan
HSBC: Cheryl, Corinne, Yen Leng, Michelle

9 Nov
Pebble: Yikun, Weiran, Ye Qin, Zou Meng, Weixin
Game of Thrones: Shanghongtao, Siyuan, Shenghui, Preya, Sisi
Flyscoot: Si Yi, Nabihah, Hui Yee, Xiaoqing, Yi Sen
Evernote: Mingyan, Yiying, Yujiao, Zhijing, Yuelin
Uber: Qiaojing, Madeleine, Kit Yee, Vera
Canon: Theodore, Timothy, Khan, Kaidi, Max

Preclusions

TopNIL

Workload

Top2-1-0-3-4

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week