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Module Overview
Description
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Timetable
BT2101
IT AND DECISION MAKING
2015/2016, Semester 1
School of Computing (Information Systems & Analytics)
Modular Credits: 4
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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
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(CS1010 or its equivalents) and IS1112 and (MA1521 or MA1102R)
Teaching Modes
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Independent 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
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NIL
Workload
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2-1-0-3-4
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