Technology can't help us in understanding data. Who needs the most data literacy competences: policy makers, journalists, doctors, patients or civil sector activists?

1. Data literacy
necessity and challenges
Srđan Verbić

2. Data literacy is all
about effective
communication and
decision making

3. Data scientists, what barriers are faced at work?

4. Technology and software can’t help
us to increase human competences.

5. Who cares about data literacy?

6. Main target groups
Decision makers (business, politics)
Public policy creators (civil servants)
Journalists
Doctors and patients
Teachers
Employees in digital world
Activists and researchers in civil sector

7. Data journalist should know basic statistics

8. How to fake GDP growth rate?

9. 1. How was the data collected?
2. What’s in there to learn?
3. How reliable is the information?

10. How to ask doctors about the
evidences and avoid to be thrown out

11. They sent me for an x-ray...

12. ...and “the medical finding was age appropriate”!?

13. 1. What are my options?
2. What are the specific benefits and harms to me?
3. What happens if I do nothing?

14. Components of data literacy

15. DATA LITERACY CHECKLIST
Someone who is “data literate” possesses the following
knowledge, skills, attitudes and behaviors.
A data literate person:
Knowledge:
1. Knows how to distinguish between different elemental forms of
data
2. Is familiar with ways that data is collected, structured and
stored
3. Grasps fundamental principles of analysis and statistics and
when they apply
4. Understands ways to visualize data and their respective
benefits and drawbacks
BEN JONES
Founder of Data Literacy, LLC
https://dataliteracy.com

16. Skills:
5. Reads and understands visual displays of data created by others
6. Cleans dirty data and combines multiple data sets together for analysis
7. Explores data sets and identifies relevant facts, patterns and trends
8. Creates clear visual displays of data to reveal insights to others
9. Communicates effectively using data and information gleaned from it

17. Attitudes:
10. Believes that data is a language that anyone can learn to read
and speak
11. Feels confident using data as a resource to answer questions &
identify new ones
12. Is alert to common pitfalls into which people fall when working
with data
13. Considers ethical use of data and the impact on society to be of
utmost importance

18. Behaviors:
14. Seeks out and creates data as a means of gathering
information
15. Identifies areas of improvement in the data and associated
analysis
16. Advocates for the effective usage of data in communication
and decision-making
17. Spreads data literacy through the active and competent use of
the language

19. 1. Statistical literacy
2. Work with data (methods and tools)
3. Argumentation and decision making
4. Managing private and public data
5. (Asking questions)
Domains for Data literacy

20. How to read and
interpret results of
international studies?

21. Tuberculosis vs. swine flu
Spanish flu 2018: 40-50 thousands
Swine flu 2009: 200 thousands (only H1N1)
“Ordinary” flu 2009: 10-50 thousands
Tuberculosis 2009: 150 thousands (without HIV possitive)
Hans Rosling had counted 253,442 articles related to swine flu in two weeks.

22. Data literacy & AI

23. People create golden calf once again

24. We need Hippocartic oath in
the same way it exists for medicine

25. Think twice before you
give away your personal
genetic information!