1. Prof Andy Koronios Head School of Information Technology & Mathematical Sciences Data Science Education

2. Big Data – ‘Virtual trail of physical reality’

3. Everything, Everywhere…. An Intelligent, Instrumented & Interconnected world! 2.2 Billion People use the Internet 60 % of Australians used it today The Internet of Things

4. Big Data… Everywhere! Lots of Hadoopalooza All these are widely available & virtually free

5. ‘Data Scientists’ are not widely available and certainly not ‘free’ “Data Scientists are better at statistics than software engineers and better at programming than statisticians” “they make discoveries while swimming in data”

6. Data Science: A Multidisciplinary Activity

7. Data Sciences’ Value Chain TransactionsTransactions Social MediaSocial Media Stream DataStream Data o Environmental o Industrial o GPS o Image/Video Exhaust DataExhaust Data o Network data o System logs High rate financial dataHigh rate financial data Data Capture Data Mgt Data Storage & Access AnalyticsApplicationEvaluation

8. IntegrationIntegration SecuritySecurity LCMLCM MDMMDM Data QualityData Quality Data Capture Data Mgt Data Storage & Access AnalyticsApplicationEvaluation Data Sciences’ Process Model

9. Hadoop HDFSHadoop HDFS Map ReduceMap Reduce DWHDWH Federated Discovery & NavigationFederated Discovery & Navigation Data Capture Data Mgt Data Storage & Access AnalyticsApplicationEvaluation Data Sciences’ Process Model

10. Descriptive AnalyticsDescriptive Analytics o Association Rules o Sequence Rules o Segmentation Predictive AnalyticsPredictive Analytics o Regression o Classification Decision TreesDecision Trees Neural NetworksNeural Networks Text AnalyticsText Analytics Real time AnalyticsReal time Analytics Data Capture Data Mgt Data Storage & Access AnalyticsApplicationEvaluation Data Sciences’ Process Model

11. Discussion of Insights with domain experts;Discussion of Insights with domain experts; Running experiments at scale;Running experiments at scale; Operationalising the Models;Operationalising the Models; ROI calculationsROI calculations Business Case DevelopmentBusiness Case Development Implementation Issues;Implementation Issues; Data Capture Data Mgt Data Storage & Access AnalyticsApplicationEvaluation Data Sciences’ Process Model

12. Monitoring;Monitoring; Model Optimisation;Model Optimisation; Evaluation of initiativeEvaluation of initiative Data Capture Data Mgt Data Storage & Access AnalyticsApplicationEvaluation Data Sciences’ Process Model

13. Attributes of a Data Scientist 1. Communication Skills are underrated; 2. The biggest challenge is not modelling, it is collecting and cleaning; 3. A Data Scientist is better at statistics than a SW engineer and better at SW engineering than a statistician; 4. A curiosity about working with data is a quality better than technical skills; 5. Good storytelling is a must. 6. The area is nascent and the role is freeform – good time to join; https://s3.amazonaws.com/leada/handbook/Handbook_Pt1.pdf Ask the right Qs * Analyse data * Build statistical models * Developing data apps

14. A Very Rare Creature Indeed! “a hybrid of data hacker, analyst, communicator, and trusted adviser…”

15. Data Scientist Employment Growth The U.S. could face a shortage by 2018 of 140,000 to 190,000 people with "deep analytical talent" and of 1.5 million people capable of analyzing data in ways that enable business decisions. (McKinsey & Co)

16. Big Data and its Impact …. ‘there are no university programs offering degrees in data science’….. Circa late 2012….. HbR, 2012

17. Data Science degrees Today US Universities North Carolina State Stanford UC Berkley MIT North Western Washington George Mason NY Etc… Australian Universities UniSA Deakin Macquarie UTS ……+++ Certification Programs EMC Data Science Associate (EMCDSA) Cloudera CCP-Data Scientist Insight Data Science Fellows Program, SAS Institute for Data Science and Engineering More than 250 universities World wide now offer some courses in Data Science & Big Data

18. Late in 2013

19. UniSA MDSc - Key features Suite of nested programs developed in conjunction with the Institute of Analytics Professionals of Australia (IAPA) and SAS, industry leader in business analytics Available face-to-face or entirely online, part-time or full time Emphasis on professional practice Technical skills in Data Science as well as project management, communications and visualisation School of Information Technology & Mathematical Sciences

20. Entry pathways School of Information Technology & Mathematical Sciences Master of Data Science Graduate Certificate Graduate Diploma Bachelor degree in Information Technology OR Mathematics Bachelor degree in any discipline (plus relevant work experience)

21. Program structure

22. Partnership with SAS Benefits: –Licence to use SAS software in a number of courses. –SAS certification for graduates of the Master program. –Eligibility for placement in the final semester through SAS Work Placement Program. Approximately 20 placements a year across Australia. A good final year student in the Master of Data Science should have a good chance of obtaining a placement, but it cannot be guaranteed. School of Information Technology & Mathematical Sciences

23. Data Science Professional Development Aim to train 1000 Data Scientists Offer Short courses in Data Science

24. Program Student Demographics Variety of backgrounds, mainly technical –Engineers, Mathematicians, Computer Scientists, Finance specialists, Marketers Mostly part time/online; 2/3 ‘Out-of-State’; 2/3 Male; Median Age 39; Mostly employed in similar role (mainly BI); Highly motivated; Already in demand.

25. School of Information Technology & Mathematical Sciences