FAQ

2. FAQ Switch off your phone now Lunch Room will be closed during lunch Wifi, password=?Presentations will be available online

4. wiki.complexworld.eu

5. Data Science How we ended up here? http://innaxis.org/wh-questions-about-data-science

6. #2 #3 #5 #1 #4 #6 1990’ 2000’ 1980’ 1920’ 1920’ 18th century Bayesian and classical statistics

7. #2 #3 #5 #1 #4 #6 1990’ 2000’ 1990’ 1920’ 1980’ 18th century Parametric models

8. #2 #3 #5 #1 #4 #6 2000’ future? 1990’ 1920’ 1980’ 18th century Highly non-linear relationships in large datasets in complex systems of the real world

9. #2 #3 #5 #1 #4 #6 second half 19th century 2000’ future 1990’ 1920’ 18th century new analytical techniques that enable the analysis of highly nonlinear relationships in very large datasets

10. #2 #3 #5 #1 #4 #6 second half 19th century 2000’ future 1990’ 1920’ 18th century Machine Learning

11. #2 #3 #5 #1 #4 #6 second half 19th century 2000’ future 1990’ 1920’ 18th century future?

12. last 30 years focus on better algortihms future? focus on designing processes

13. Developments on Information Technologies Storage Computing Communications

14. Statistical Modeling Data Mining Machine Learning and Knowledge Discovery Data Science IT infrastructure Computer Science

15. Data Science Data science is a set of fundamental principles that support and guide the principled extraction of information and Data science and data-driven decision making involve for understanding phenomena via the with the ultimate goal of knowledge from data processes and techniques automaticanalysis of data improving decision making

16. Data Science knowledge from data processes and techniques automaticanalysis of data improving decision making New data-driven operational paradigms

17. Data Science Science? Art? Multi-disciplinary Statistical Analysis Artificial intelligence Machine Learning Computer Science IT infrastructure

18. Data Science Science? Art? Multi-disciplinary Visualization Statistical Analysis Complexity Science Artificial intelligence Statistical physics Machine Learning Network Theory Computer Science Scalable Analytics IT infrastructure Domain Knowledge

19. Data Science Challenges!! Unstructured

20. Data Science Challenges!! Diversity of data sources Unstructured

21. Data Science Challenges!! Veracity, accuracy Unstructured Diversity of data sources

22. Data Science Challenges!! Data-streaming Unstructured Diversity of data sources Veracity, accuracy

23. Data Science Challenges!! Volume and Big Data Unstructured Diversity of data sources Veracity, accuracy Data-streaming

24. Data Science Challenges!! Unstructured Predictive Analytics Domain analysis Diversity of data sources Complexity Prescriptive Analytics Veracity, accuracy Decision making process Deployment techniques Data-streaming Validation Cultural Shifts Volume and Big Data Visualization Confidentiality, privacy Non-concurrency Infrastructure PA Automation Security Uncertainty Actionable outputs

25. Data Science knowledge from data processes and techniques automaticanalysis of data improving decision making New data-driven operational paradigms for Air Transport Safety Passenger delay Costs models Capacity Resilience Network congestion ATM Automation Data Security Emissions

26. innovation@innaxis.org