The Art and Science of Big Data Education
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1 The Art and Science of Big Data Education Anne L. Washington, PhD School of Policy, Government, and International Affairs George Mason University Wed August BDA EdCon AMCIS 2015 Big Data and Analytics Education Conference st Americas Conference on Information Systems Art and Science of Big Data Education - [email protected] 1
2 Agenda Creating data-driven managers Open data workshops experience Recommendations for training Art and Science of Big Data Education - [email protected] >> 2
3 Will history repeat itself? Ross & Weill (2002) Strategy without data Technology without strategy Management without authority Activity without productivity Art and Science of Big Data Education - [email protected] >> 3
4 Managers as data consumers Request data analytics Interact with data products Supervise data scientists Art and Science of Big Data Education - [email protected] >> 4
5 How to create data-driven managers? Training non-stem users Big data with everyday knowledge No private identifying information Complex but not complicated Domain neutral Art and Science of Big Data Education - [email protected] >> 5
6 Open data Sharing Licensing Technical standards Open government data documented mandated across time Art and Science of Big Data Education - [email protected] >> 6
7 Political Informatics Open government data for innovative academic research Social science scholars Computer scientists Co-PI John Wilkerson, University of Washington Art and Science of Big Data Education - [email protected] >> 7
8 Grant PI-NET Poli-Informatics Research Coordination Network Funded under NSF # Data challenges leading to workshops and training Art and Science of Big Data Education - [email protected] >> 8
9 EXPERIENCES Art and Science of Big Data Education - [email protected] 9
10 Data Challenges DC Financial Crisis Seattle Climate Change Art and Science of Big Data Education - [email protected] >> 10
11 Examples Transcripts Geospatial data Inter-related Documents Art and Science of Big Data Education - [email protected] >> 11
12 From data challenges to workshops Focus on interpretation 1. Context 2. Methods Art and Science of Big Data Education - [email protected] >> 12
13 1 Context What questions can data answer? Idealistic products Technical possibilities Data arrangement and connection Subject expertise Art and Science of Big Data Education - [email protected] >> 13
14 2 Method What questions can we ask today? Realistic implementation Technical strength / weakness Data category limitations Skills Art and Science of Big Data Education - [email protected] >> 14
15 RECOMMENDATIONS Art and Science of Big Data Education - [email protected] 15
16 Workshop Outcomes Broad inclusive conversations Common ground Knowledge-sharing Critical thinking Art and Science of Big Data Education - [email protected] >> 16
17 Challenges for big data training Thinking at scale Thinking across time Thinking like a network Leveraging technology limitations Working with data realities Art and Science of Big Data Education - [email protected] >> 17
18 Questions for interpretation When are data? (Borgman, 2015) Who are data? How are data? Why are data? What are data? Art and Science of Big Data Education - [email protected] >> 18
19 Conclusion Art of data Observations not facts Categories in context Science of algorithms Certainty Consistency Art and Science of Big Data Education - [email protected] >> 19
20 The art and science of big data education Anne L. Washington, PhD Assistant Professor School of Policy, Government, and International Affairs Organizational Development & Knowledge Management George Mason University, Arlington VA Art and Science of Big Data Education - [email protected] 20
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