The document provides an overview of data journalism training covering topics such as data types, data ethics, finding data sources, and what makes a good visualization. It discusses qualitative and quantitative data, as well as discrete, continuous, categorical, and ordinal data. It outlines best practices for data analysis including keeping original data untouched, maintaining a paper trail, and having others verify work. The document also explores search operators, advanced search techniques, and elements that make visualizations effective such as the target audience, key message, simplicity, and explanation.

1. DATA JOURNALISM TRAINING
Day 1

2. WHAT IS DATA

3. Asking a question

4. Name Gender Age Height Feeling
Mandy F 21 150cm Swamped
Shani F 23 167cm Nervous
Zizo F 25 167cm Curious
Ashleigh F 22 163cm Relaxed
Danyal M 22 156cm Optimistic
Jason M 36 200cm Flustered
Hannah F 35 167cm Very excited
Phumlani M 24 180cm Grumpy
Milena F 29 160cm Excited

5. Data types
● QUALITATIVE DATA: is everything that refers to the
quality of something: A description of colours, texture
and feel of an object , a description of experiences, and
interview are all qualitative data.
● QUANTITATIVE DATA: is data that refers to a number.

6. Data types
● DISCRETE DATA: is numerical data with values which
are distinct and separate, i.e. they can be counted.
Examples might include the number of kittens in a litter;
the number of patients in a doctors surgery;
● CONTINUOUS DATA: is numerical data with a
continuous range. You can count, order and measure
continuous data. For example height, weight,
temperature, the amount of sugar in an orange, etc.

7. ● CATEGORICAL DATA: puts the item you are
describing into a category; Examples can include
gender, colour, size, etc.
● ORDINAL DATA: data which can be ranked (put in
order) or have a rating scale attached. You can count
and order, but not measure, ordinal data; Example: a
scale from 1 to 5
Data types

8. Data types quiz
Role: Drummer
❏ Continuous Data
❏ Categorical Data
❏ Quantitative Data
Year Born: 1963
❏ Qualitative Data
❏ Discrete Data
❏ Continuous Data
❏ Categorical Data
Name: Rick Allen
❏ Quantitative Data
❏ Qualitative Data
❏ Discrete Data
Size: M
❏ Ordered Data
❏ Categorical Data
❏ Continuous Data
Height: 187cm
❏ Discrete Data
❏ Categorical Data
❏ Continuous Data
❏ Qualitative Data
Date: 5th of March 2014
❏ Discrete Data
❏ Categorical Data
❏ Continuous Data

9. Jargon busting

11. Data pipeline

12. DATA ETHICS &
VERIFICATION
[Jason]

13. Good practices and basic ethics
● Save original copy of data and do not touch it.
● Paper trail - Keep a log with every step that you take in the
analysis.
● Do not change original columns. Duplicate them and make
the changes here.
● Have several drafts and look at how your analysis
developed.
● Spend to understand your data. Read the methodology.

14. Good practices and basic ethics
● Do not assume what the data is. Run integrity check on each
column.
● Clean the data before interviewing it
● Count the records. Cross-reference with the methodology.
Report any inconsistency and request the missing data or a
recount. Keep the total records in mind while analysing the data.
● If a result looks to good to be true, it probably is.
● Make a summary of the end results, as if you were writing a
press release. Look for mistakes

15. Good practices and basic ethics
● Have somebody else verify your work, preferably
somebody who knows nothing about your project.
● Check your biases and look at your data from new
angles
● Look for context that would explain your results to
yourself and to your audience
● e.g. Egypt worst country for women’s rights
● Bounce your results against experts

16. FINDING DATA
& DATA
SOURCES

17. Advanced search
● Google Advanced Search
● Wayback Machine – for the dead web (1996 onwards)
http://archive.org/web/

18. Search operators
● * (asterix) – substitutes a word and will allow your search to
cover similar phrases
● Cache: - allows you to find web pages hidden in Google’s
cache
● filetype: - will get look for the specified file type
● Link: - helps you find all the sites that link to a particular
page

19. Search operators
● ‘ ‘ or “ “ (Quotation marks) – help you find the exact phrase
● + or AND – narrows down your search by returning the exact
word phrases
● OR – expands search by including either of two search
phrases
● - or NOT – it would tell an engine to exclude a term
● e.g. Monsanto-’agent orange’

20. WHAT MAKES A
GOOD
VISUALISATION
?

21. What makes a good visualisation
For each of these visualisations think of:
● What is the target audience
● What is the key message
● How successful are they in communicating the
message
● What makes them stand out?
● How well are they explained?
● How simple/ complex they are?

22. Source: The Economist

23. Source: BBC News

24. Source: The Guardian

25. Source: New York Times, Amanda Cox;

26. Source: The Functional Art, Alberto Cairo

27. Source: Lower Saxony State Elections

28. Source: Population pyramid

29. Source: Hans Rosling, 200 Countries, 200 Years, 4 Minutes

30. Source: The Wall Street Journal

31. Source: Where does my money go, UK

33. Source: Where does my money go, UK

34. Source: Spending stories

35. Source: Driven by Data, Gregor Aisch

36. Source: The Guardian

37. Source: Transparency International

38. Source: The Guardian

39. Source: Migrations Map