1. Data Science: Challenges and Directions
A paper by longbing cao
Presented by Nathan Navarro griffin

2. The Paper
The data
The present
The future

3. What Is Data Science?
The intersection of statistics, informatics, computing, communication, sociology, and management given data, domain, and thinking
Goals of translating data into insight and intelligence
Complex systems
X-Complexities
X-Intelligence

4. The data

5. X-Complexities Complex relationships encoded in data Often hidden
Some involved complexities:
Behavior of subjects
Domain (expert knowledge, policies, …)
Social dynamics
Learning (for humans)

6. X-Intelligence Refers to comprehensive and valuable information
Informs underlying challenges
Complex systems
X-complexities
Some involved intelligences:
Behavior intelligence
Domain intelligence
Human intelligence
Social intelligence

7. The present

8. Understanding Known-to-Unknown
Modern data science seeks to find hidden “CKI”:
Complexities
Knowledge
Intelligence
Modern data science focuses on:
“We do not know what we do not know”
Tackles CKI invisibility

9. Modern Data Science Challenges in areas not currently well managed
Data/business understanding
Mathematical and statistical foundation
Data quality and social issues
Data value, impact, utility
Data-to-decision and action-taking challenges
Mine/model data to better understand these issues

10. The IID Assumption Relatively narrow assumptions are made
IID says data is simple, even though it is not
Assumes strongly structured data
Often ignores many x-complexities
Influences in data that affect linkage/co- occurrence/correlation/etc.
Needs more research to resolve
Deeper understanding of non-IID characteristics

11. Data Characteristics Data characteristics
X-complexities
Other factors (dimension, heterogeneity, uncertainty…)
A need to understand data characteristics
1. How do we define data characteristics?
2. How do we represent and model data characteristics s?
3. data characteristics driven data understanding
4. How do we evaluate quality of understanding?

12. The Future

13. Human-like Intelligence
Public likes to talk about it
AlphaGo
Nothing has exhibited human-like thought
Creativity
Curiosity
Models cannot yet upgrade their own intelligence
Data analytical thinking

14. Building Complex Systems
Need to synthesize X- intelligence in complex data from many fields
Ubiquitous intelligence to understand data
Qualitative-to-quantitative metasynthesis
Iterative cognitive
Human-machine-cooperative

15. What to take away
Challenges in the hidden data reflect the immaturity of the field
X-intelligence
X-complexities
Future work will require cross-disciplinary action
Impact of the field could be unprecedented
Draw parallels to the internet revolution

16. Questions?