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Learning Outcomes

TopPredictive 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

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	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

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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

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Continuous Assessment (CA)	Class Participation/Forums/Tutorials	5%
	Individual Assignment(s)	25%
	Group Project	30%
Finals	Exam	40%
Total		100%

Workload

Top2-0-1-3-4

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