1. Second Major in Data Science & Analytics (DSA)
Education Professor KWONG Koon Shing, PhD, FSA, CERA

2. Increasing volume of data Advanced computing technologies
Background
Increasing volume of data
Advanced computing technologies
Need to train more data analysts to meet demand
Significant shortage of data analysis workforce in Singapore
Local universities recently launched Data Science degrees
On 31 August 2018, Andrew C Goldin, Director of PwC Consulting states that Singapore has 15,000 skills shortage in the Data and Analytics market landscape.
DSA 2019 Information session

3. Data Science
Statistics is the science of learning from data through statistical inference, stochastic modelling, and predictive analysis
Computing is any operation that involves computers, such as computer engineering, software engineering, information systems and technology, etc.
Data Science is the integration of Statistics and Computing to learn from raw data and then extract useful information for decision making in the most efficient and effective way
DSA 2019 Information session

4. DSA is open to all SMU students
New Second Major in DSA
School of Economics (SOE) will launch a new second major in DSA in AY2019/2020
DSA is open to all SMU students
First targeted batch is all first-year students in AY2019/2020 and first-year students in AY2018/2019
DSA 2019 Information session

5. Closely collaborate with School of Information Systems
Highlights of DSA
Focus on stochastic modelling, computing, simulation and predictive modelling
Closely collaborate with School of Information Systems
Solid foundation in computational analysis and statistical concepts
Adopt hands-on pedagogy with extensive training in the R programming language
DSA 2019 Information session

6. Open source language (free to use!!)
Why learn R?
Open source language (free to use!!)
Powerful language for statistical analysis and data management
One of most popular statistical languages, used by Google, Microsoft, Uber, etc.
Easy to learn R with the user-friendly RStudio platform
Many R packages available for solving real-world problems
DSA 2019 Information session

7. Python and R
DSA 2019 Information session

8. Example of R-Programming
pairs(~exam+test+assign+part)
ggplot(data=STAT201.example, aes(y=Adj.exam, x=Test))+
geom_point(aes(color=Major, size=ACS))
DSA 2019 Information session

9. DSA Curriculum: 5 core courses
STAT201 Probability Theory and Applications
DSA201 Statistical Inference for Data Science
DSA211 Statistical Learning with R
DSA212 Data Science with R
IS103 Computational Thinking
Prerequisite: Calculus and STAT101/STAT151
DSA 2019 Information session

10. DSA Curriculum: 4 elective courses (at least one course in each list)
DSA 2019 Information session

11. Tentative Timetables
DSA 2019 Information session

12. Teaching Faculty of DSA
Education Professor of Statistics CHOW Hwee Kwan
Education Professor of Statistics KWONG Koon Shing
Professor of Economics TSE Yiu Kuen
Recruit new professors
DSA 2019 Information session

13. DSA students should be able to:
DSA Outcomes
DSA students should be able to:
Apply state of the art data analysis approaches
Understand computer intensive methods
Construct, manage, and maintain databases
Build reliable stochastic and predictive models by conducting proper data checking and validation
Handle different types of data, such as cross-sectional data, time series data, spatial data, etc.
Master R programming skills
DSA 2019 Information session

14. Thank you.