Second Major in Data Science Analytics DSA Education

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

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 Pw. C Consulting states that Singapore has 15, 000 skills shortage in the Data and Analytics market landscape. DSA 2019 Information session 2

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 3

New Second Major in DSA • School of Economics (SOE) will launch a new second major in DSA in AY 2019/2020 • DSA is open to all SMU students • First targeted batch is all first-year students in AY 2019/2020 and first-year students in AY 2018/2019 DSA 2019 Information session 4

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 5

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 6

Python and R DSA 2019 Information session 7

Example of R-Programming pairs(~exam+test+assign+part) DSA 2019 Information session ggplot(data=STAT 201. example, aes(y=Adj. exam, x=Test))+ geom_point(aes(color=Major, size=ACS)) 8

DSA Curriculum: 5 core courses 1. 2. 3. 4. 5. STAT 201 Probability Theory and Applications DSA 201 Statistical Inference for Data Science DSA 211 Statistical Learning with R DSA 212 Data Science with R IS 103 Computational Thinking Prerequisite: Calculus and STAT 101/STAT 151 DSA 2019 Information session 9

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

Tentative Timetables DSA 2019 Information session 11

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 12

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 13

Thank you.