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TRANSCRIPT
LEE Wee Leong
Associate Professor of Information Systems (Education) &
Programme Director, MITB(Analytics)
Leading
Business with
Data
1
How Do We Make Decisions?
3
*40% of major decision making are based on gut feeling!* Accenture, 2008
12% - Hard Facts88% - Gut Feel* Aspect Consulting, 1997
10% - Hard Facts90% - Gut Feel* Economist Intelligence Unit, 2014
What is Data Analytics?
4
Analytics is the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.
~ Thomas H. Davenport ~ Competing on Analytics: The New Science of Winning
Statistical &Quantitative
Analyses
Explanatory &Predictive Modeling
Fact-based Management
Decisions & Actions
DATA
Why Data Analytics is “HOT” Now?
5
Increase in
Processing Power
& Cheaper Storage
Explosion
of data –
Big Data Ease of Analyzing Data
Big Data Intel Inside Pentium
Intel Inside Pentium 4
Intel Inside Core Duo 2
Intel Inside Core Quad 2
Intel Inside Core i5
Oracle
Weka The University of Waikato
SAS
IBMSPSS Software
Rapidminer
IBM WatsonKNIME
Revolution Analytics
What is BIG DATA?
6
VolumeScale of Data
Variety Different Forms of Data
VelocityAnalysis of Streaming Data
VeracityUncertainty of Data
The FOUR V’s of Big
Data
Analytics Applications
Financial Industry
Telco Consultancy
Healthcare RetailTransportation
& Logistics
7
Foundation Courses
Analytics Framework &
Business Context
Data Analytics Lab
Elective Courses
Customer Analytics
Operations Analytics
Big Data: Tools & Techniques *(new)
Visual Analytics
Text Analytics
Social Analytics
Applied Machine Learning *(new)
Predictive Analytics using Simulation
*(new)
Hands-on & Industry Linkage
Internship/ Capstone Project
Monthly industry seminars
General
Management
and IT &
Project
Management
courses
Full time : 1 year
Part time : 2 years
Introduction to
Statistics
Introduction to R
Programming
MITB (Analytics) Curriculum
9
B. Analytics
Technology &
Applications
C. Information Technology
Management
D. General Management
for Technology &
Operations
E. Internship / Capstone
Project
B.1 Analytics Framework &
Business Context
B.2 Data Analytics Lab
B.3 Customer Analytics &
Applications
B.4 Operations Analytics &
Applications
B.5 Big Data: Tools &
Techniques
B.6 Visual Analytics &
Applications
B.7 Text Analytics &
Applications
B.8 Social Analytics &
Applications
B.9 Applied Machine
Learning
B.10 Predictive Analytics
using Simulation
C.1* Innovation
Management
C.2 Spreadsheet Modeling
for Technology &
Operations Decisions
C.3 IT Project & Vendor
Management
C.4 Global Sourcing of
Technology &
Processes
D.1A* Financial Accounting
D.1C* Management
Accounting for T&O
Managers
D.2 Strategy & Organisation
D.3 Finance for T&O
Managers
D.4* HRM for Technology &
Operations Managers
E.1 Internship
• Internship Job description
definition
• Resume writing,
internship application &
interviews
• Industry attachment
E.2 Capstone Project
• Project definition,
development & critique
workshops
• Industry expert seminars
& company site visits
• Project Delivery
Equip with innovation, IT and project
management skills
The Curriculum in a Nutshell
10
B. Analytics
Technology &
Applications
C. Information Technology
Management
D. General Management
for Technology &
Operations
E. Internship / Capstone
Project
B.1 Analytics Framework &
Business Context
B.2 Data Analytics Lab
B.3 Customer Analytics &
Applications
B.4 Operations Analytics &
Applications
B.5 Big Data: Tools &
Techniques
B.6 Visual Analytics &
Applications
B.7 Text Analytics &
Applications
B.8 Social Analytics &
Applications
B.9 Applied Machine
Learning
B.10 Predictive Analytics
using Simulation
C.1* Innovation
Management
C.2 Spreadsheet Modeling
for Technology &
Operations Decisions
C.3 IT Project & Vendor
Management
C.4 Global Sourcing of
Technology &
Processes
D.1A* Financial Accounting
D.1C* Management
Accounting for T&O
Managers
D.2 Strategy & Organisation
D.3 Finance for T&O
Managers
D.4* HRM for Technology &
Operations Managers
E.1 Internship
• Internship Job description
definition
• Resume writing,
internship application &
interviews
• Industry attachment
E.2 Capstone Project
• Project definition,
development & critique
workshops
• Industry expert seminars
& company site visits
• Project Delivery
The Curriculum in a Nutshell
Develop business management skills
11
B. Analytics
Technology &
Applications
C. Information Technology
Management
D. General Management
for Technology &
Operations
E. Internship / Capstone
Project
B.1 Analytics Framework &
Business Context
B.2 Data Analytics Lab
B.3 Customer Analytics &
Applications
B.4 Operations Analytics &
Applications
B.5 Big Data: Tools &
Techniques
B.6 Visual Analytics &
Applications
B.7 Text Analytics &
Applications
B.8 Social Analytics &
Applications
B.9 Applied Machine
Learning
B.10 Predictive Analytics
using Simulation
C.1* Innovation
Management
C.2 Spreadsheet Modeling
for Technology &
Operations Decisions
C.3 IT Project & Vendor
Management
C.4 Global Sourcing of
Technology &
Processes
D.1A* Financial Accounting
D.1C* Management
Accounting for T&O
Managers
D.2 Strategy & Organisation
D.3 Finance for T&O
Managers
D.4* HRM for Technology &
Operations Managers
E.1 Internship
• Internship Job description
definition
• Resume writing,
internship application &
interviews
• Industry attachment
E.2 Capstone Project
• Project definition,
development & critique
workshops
• Industry expert seminars
& company site visits
• Project Delivery
The Curriculum in a Nutshell
Acquire hands-on industry experience, with internship opportunities for full-time
students.
MITB (Analytics) – At a Glance
First Master Program in Analytics in Asia
Launched in Jan 2011
10th intake (Aug 2016)
Our case won TUN teaching innovation award 2013
Our simulation games shortlisted for Wharton-QS Stars Reimagine Education Awards 2015
Largest ecosystem in Data & Decision Analytics related research
Wide selection of internship opportunity
Summary
• Benefits for commuters:– With real-time commuters data, model developed
will be able to give commuters a good gauge of the average waiting time at the train stations.
– New or existing mobile applications can be developed to tap into this useful information.
• Benefits for train operators:– The simulation model can be used as a test bed
to test various scenarios by varying some operational conditions like:• Train capacity (new models or add seats)
• Inter-station travelling time (change speed of trains)