Introduction to Data Journalism
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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.
![DATA ETHICS &
VERIFICATION
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Introduction to Data Journalism
- 2. WHAT IS DATA
- 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
- 11. Data pipeline
- 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
- 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’
- 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?
- 23. Source: BBC News
- 25. Source: New York Times, Amanda Cox;
- 26. Source: The Functional Art, Alberto Cairo
- 27. Source: Lower Saxony State Elections
- 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
- 35. Source: Driven by Data, Gregor Aisch
Editor's Notes
- #4: What we mean by data when we do data journalism? Whether you began with a question or not, you should always keep your eyes open for unexpected patterns, unusual results, or anything that surprises you. Often, the most interesting stories aren’t the ones you were looking for.
- #7: Discrete data is counted, Continuous data is measured Discrete Data Discrete Data can only take certain values. Example: the number of students in a class (you can't have half a student). Continuous Data Continuous Data can take any value (within a range) Examples: A person's height: could be any value (within the range of human heights), not just certain fixed heights, Time in a race: you could even measure it to fractions of a second, A dog's weight, The length of a leaf
- #10: Machine readable - if it is in a format that can be easily processed by a computer. Digital ≠machine readabale. Example: a PDF document containing tables of data (is digital but are not machine-readable because a computer would struggle to access the tabular information even though they are very human readable!). The equivalent tables in a format such as a spreadsheet would be machine readable. In general, HTML and PDF are *not* machine-readable.
- #23: COMPARE
- #24: COMPARE AND PUT IN CONTEXT: put in context the loss of men and women in the Afgan was as compared to Vietnam and the second World War
- #25: Show trends
- #26: SHOW TREND OVER TIME
- #27: Trend over time Compares different presidencies
- #30: Show trend over time Tell a story Engage, captivate Compares countries
- #31: Patterns
- #32: Personal angle
- #33: Show hierarchy
- #34: Personal angle - people get where they fit in the bigger picture Compares, puts things into perspective
- #36: Show relations