Home
About
Mobile
Open Content
Search
Module Overview
Description
Facilitators
Weblinks
Timetable
TBA2104
PREDICTIVE ANALYTICS
2018/2019, Semester 2
School of Continuing & Lifelong Edn (School of Continuing & Lifelong Edn)
Modular Credits: 4
Tags:
--
Collapse All
Learning Outcomes
Top
Predictive analytics uses an assortment of statistical techniques to predict future events or behaviors based on collected data. For example, businesses could use predictive analytics to answer questions about consumer behavior and market movements and to anticipate future events, forecast plausible outcomes, and make informed decisions that enable organisations to gain and sustain competitive advantages. Data can be combined and analysed to make predictions with a certain degree of reliability. Students will learn predictive analytics by using Excel or R to construct statistical models like regression, classification, clustering among others.
Schedule
Top
Activities
Remarks
Week 1
14 Jan to 18 Jan
L0 Course Overview
L1 Introduction
Week 2
21 Jan to 25 Jan
L2 Basics on R Programming
Week 3
28 Jan to 1 Feb
L3 Data Preparation
Week 4
4 Feb to 8 Feb
No Lecture (Chinese New Year)
Week 5
11 Feb to 15 Feb
L4 Regression
Week 6
18 Feb to 22 Feb
L5 Classification I (Logistic Regression)
Recess Week
23 Feb to 3 Mar
Week 7
4 Mar to 8 Mar
L6 Classification II (Naive Bayes & Resampling Methods)
Week 8
11 Mar to 15 Mar
L7 Classification III (Support Vector Machines)
Assignment 1 Due:
15 March
17 March
Week 9
18 Mar to 22 Mar
L8 Decision Tree
Week 10
25 Mar to 29 Mar
L9 Clustering
Week 11
1 Apr to 5 Apr
L10 Application : Text Mining
Week 12
8 Apr to 12 Apr
L11 Applications of decision making in various domains
Assignment 2 Due:
12 April
14 April
Week 13
15 Apr to 19 Apr
Group Presentations
Tenative Schedule (subjected to changes)
Group Presentation Order
Top
Groupings
Topic
Qiu Yu, Lianhan
Stocks Trading
Clement, Guo Wei, Hwee Chen, Kris
Movie Recommendation System
Chen Bin, Jin Bao, Lien Hong, Wendi
Analysis on Crimes in Boston
Zhihong, Kheng Hui, Jia Wei, Zelig
Email Spam Filtering
Kheng Hwa, Chye Soon, Rachel, Chi Xiang
Housing Pricing Analytics
Presentation starts at:
7:00pm
Each group given
20 mins
to present
Assessment
Top
Continuous Assessment (CA)
Class Participation/Forums/Tutorials
5%
Individual Assignment(s)
25%
Group Project
30%
Finals
Exam
40%
Total
100%
Workload
Top
2-0-1-3-4
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