Second Major in Data Science & Analytics (DSA)

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

2. 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. 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. 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. 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 ggplot(data=STAT201.example, aes(y=Adj.exam, x=Test))+ geom_point(aes(color=Major, size=ACS)) pairs(~exam+test+assign+part) 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 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.