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Module Overview


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BT2101 

IT AND DECISION MAKING
   2015/2016, Semester 1
   School of Computing (Information Systems & Analytics)
Modular Credits: 4
  Tags: --

Learning Outcomes

Top
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

Top
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

Top
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

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